FLUORESCENCE SPECTROSCOPY AND PARALLEL FACTOR ANALYSIS OF WATERS FROM MUNICIPAL WASTE SOURCES

A Thesis presented to

the Faculty of the Graduate School at the University of Missouri – Columbia

In Partial Fulfillment

of the Requirements for the Degree

Master of Science

by

Benjamin Teymouri

Dr. Baolin Deng, Thesis Supervisor

AUGUST 2007

The undersigned, appointed by the Dean of the Graduate School, have examined the thesis entitled

FLUORESCENCE SPECTROSCOPY AND PARALLEL FACTOR ANALYSIS OF WATERS FROM MUNICIPAL WASTE SOURCES

Presented by Benjamin J. Teymouri a candidate for the degree of Master of Science, and hereby certify that in their opinion it is worthy of acceptance

________________________________________________ Dr. Baolin Deng

________________________________________________ Dr. Zhiqiang Hu

_________________________________________________ Dr. Allen Thompson

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ACKNOWLEDGEMENTS

I would like to sincerely thank my thesis supervisor, Dr. Baolin Deng, for his

support and guidance during this project. Throughout my undergraduate and graduate

career, he has been a caring and insightful source for knowledge and encouragement. I

deeply appreciate the assistance of committee members Dr. Allen Thompson and Dr.

Zhiqiang Hu with this project and with valuable coursework.

Thanks to all members of my research group for friendship and willingness to be

of assistance. This especially applies to Dr. Bin Hua. His work with PARAFAC

modeling and guidance made this project possible.

I would like to thank personnel at the Columbia Sanitary Landfill and Columbia

Regional Wastewater Treatment Plant for their assistance with sample collection. Craig

Cuvellier was very kind and helpful to provide water quality data from the wastewater

treatment plant. Financial support from Missouri Department of Natural Resources

Superfund program and from Missouri Water Resources Research Center is gratefully

acknowledged. Finally, I would like to thank my parents for their loving devotion

throughout my life.

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TABLE OF CONTENTS

ACKNOWLEDGEMENTS ……………………………………………………………… ii LIST OF TABLES ………………………………………………………………………..v

LIST OF FIGURES …………………………………………………………………….. vii ABSTRACT …………………………………………………………………………… viii Chapter 1 INTRODUCTION ………………………………………………………......... 1 Chapter 2 LITERATURE REVIEW ……………………………………………………. 5 2.1 Fluorescence Spectroscopy Analysis …………………………………………5

2.1.1 Description of Analysis ……………………………………………..5

2.1.2 Excitation Emission Matrices ……………………………………… 8

2.2 Fluorescent Substances ……………………………………………………... 10

2.2.1 Fluorescence Characteristics of Landfill Leachate ……………….. 13

2.2.2 Fluorescence Characteristics of Municipal Wastewater …………...15

2.2.3 Fluorescence Characteristics of River Water .……………………. 17

2.3 Parallel Factor Analysis …………………………………………………...... 18

2.4 Applications of EEM Fluorescence Spectroscopy using PARAFAC …......... 21 Chapter 3 MATERIALS AND METHODS ………………………………………….... 26

3.1 Sample Collection and Handling …………………………………………… 26 3.2 Creation of Excitation Emission Matrices ………………………………….. 26 3.3 Parallel Factor Modeling …………………………………………………… 29 3.4 Water Quality Analysis …………………………………………………....... 31

Chapter 4 SAMPLING SITE CHARACTERISTICS …………………………………..34

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4.1 Columbia Sanitary Landfill ………………………………………………… 34 4.2 Columbia Regional Constructed Wetlands Treatment Area ……………….. 34 4.3 Missouri River at Eagle Bluffs Conservation Area ………………………… 37

Chapter 5 RESULTS AND DISCUSSION ……………………………………………. 40

5.1 Water Quality Parameters …………………………………………………... 40

5.2 Fluorophore Identification ………………………………………………….. 43

5.2.1 Wastewater Fluorophore Identification ………………………….. 43 5.2.2 Landfill Leachate Fluorophore Identification …………………….. 45 5.2.3 Missouri River Fluorophore Identification ……………………….. 47 5.2.4 Additional EEMs from Missouri WWTPs and Landfills ………… 48

5.3 PARAFAC Modeling Results ………………………………………………. 51

5.3.1 Number of Components …………………………………………... 51

5.3.2 PARAFAC Component Description ……………………………… 55

5.3.3 Component Composition of Sample Sources ……………………. 60

5.3.4 Seasonal Variation of PARAFAC Component Scores …………… 64

5.3.5 Constructed Wetlands Impact on Wastewater

Component Score ……………………………………………… 71

5.3.6 PARAFAC Modeling Validation …………………………………. 75

5.3.7 Correlation with Water Quality Parameters ………………………. 77 Chapter 6 SUMMARY AND CONCLUSIONS ………………………………………. 80 REFERENCES …………………………………………………………………………. 83

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LIST OF FIGURES

Figure Page

2.1 Electronic transitions of an excited molecule ………………………………. 7

2.2 Example of an excitation emission matrix (EEM) …………………….…… 10 2.3 Generalized structure of humic acid ……………………….………………. 11 2.4 Fluorescence centers of humic-like, protein-like and xenobiotic-like substances from various sources ……………………………………...... 13 3.1 EEM created in MATLAB, Raleigh light scattering removed …………...... 30 4.1 Site 1 – Landfill leachate (LL) ………………………...…………………… 34

4.2 Site 2 – Wastewater treatment plant effluent (WW) ...................................... 36

4.3 Site 3 – Unit 4 wetlands effluent (U4) ........................................................... 36

4.4 Site 4 – Wetlands effluent (WE) …………………………………………… 36

4.5 Map of constructed wetlands ……………………………………………..... 37 4.6 Site 5 – Missouri River at Eagle Bluffs Conservation Area ……………….. 38 4.7 Sampling Locations ………………………………………………………... 39

5.1 Wastewater EEM ………………………………………………………....... 45 5.2 Landfill leachate EEM …………………………………………………....... 46 5.3 Missouri River EEM ……………………………………………………….. 47 5.4 EEMs created from four Missouri wastewater treatment

plant effluents ……………………………………………………........... 49

5.5 EEMs created from four leachates collected from Missouri landfills ……………………………………………………… 50

5.6 Split half analysis results for three components …………………………… 53 5.7 Split half analysis results for four components ……………………..……… 53

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5.8 Split half analysis results for five components …………………………….. 54 5.9 Split half analysis results for six components ………………………..…….. 54 5.10 Excitation and emission loadings for Component 1 ……………………..… 55 5.11 Excitation and emission loadings for Component 2 …………………..…… 56 5.12 Excitation and emission loadings for Component 3 …………………..…… 56 5.13 Excitation and emission loadings for Component 4 …………………..…… 57 5.14 Components 1-4 modeled by PARAFAC ……………………………..…… 59 5.15 PARAFAC component composition of sample locations

based on normalized fluorescence score ……………………………..… 61

5.16 PARAFAC component composition of sites with dilution factors accounted for and without normalization …………………….… 62

5.17 Site classification based on PARAFAC component scoring ………….…… 63 5.18 Seasonal variation of PARAFAC components from Site 1 – LL …….……. 66 5.19 Seasonal variation of PARAFAC components from Site 2 – WW …….….. 67 5.20 Seasonal variation of PARAFAC components from Site 3 – U4 …….……. 68 5.21 Seasonal variation of PARAFAC components from Site 4 – WE …...…….. 69 5.22 Seasonal variation of PARAFAC components from Site 5 – MR …….…… 70 5.23 Component 2 and protein peak reduction through

constructed wetlands …………………………………………………… 74

5.24 PARAFAC modeling validation by comparison with ‘peak picking’ method for wastewater samples …………………...…… 76

5.25 Correlation of protein-like Component 2 with BOD and COD ……………. 79

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LIST OF TABLES

Table Page

2.1 Water quality parameter correlation with PARAFAC component scores ……………………………………………………..... 24

3.1 Setup parameters for creation of EEMs……………………………..……… 27

3.2 Fluorescence wavelength accuracy check parameters ……………...……… 28

4.1 Project sampling locations …………………………………………….…… 38

5.1 Summer water quality results ………………………………………….…… 42 5.2 Winter water quality results …………………………………………...…… 42 5.3 PARAFAC modeling results for 1-8 components …………………….…… 52 5.4 Description of components derived from PARAFAC modeling ………...… 59 5.5 Water quality parameter reduction through constructed wetlands ………… 71

5.6 PARAFAC component reduction through constructed wetlands ………….. 73

5.7 Component reduction and sky cover information for sampling dates …...… 74 5.8 Correlation of wastewater PARAFAC component scores

with select water quality parameters ………………………………….... 78

viii

FLUORESCENCE SPECTROSCOPY AND PARALLEL FACTOR ANALYSIS OF WATERS FROM MUNICIPAL WASTE SOURCES

Benjamin Teymouri

Dr. Baolin Deng, Thesis Supervisor

ABSTRACT

Excitation-emission matrix (EEM) fluorescence spectroscopy is becoming a

valuable tool for studying the complex nature of dissolved organic matter. EEMs can

identify fluorescence emitting organic substances (fluorophores) based on fluorescence

peak location. Parallel Factor Analysis (PARAFAC) has recently been used to

effectively model EEM data sets. This thesis continues the study of the EEM/PARAFAC

technique by applying it to waters of municipal waste sources.

Bi-weekly samples were collected over a one-year period from the Columbia

Sanitary Landfill, Columbia Regional Wastewater Treatment Plant Constructed Wetlands

and the Missouri River at Eagle Bluffs Conservation Area. EEMs were created for each

sample and modeled using PARAFAC.

Humic-like, protein-like and xenobiotic-like fluorophores identified from EEMs

were consistent with recent studies. The three sample sources were clearly differentiated

based on their organic composition. Seasonal development of PARAFAC results

indicated increasing humification within the landfill and elevated levels of humic-like

fluorescence from the constructed wetlands during summer. Protein-like fluorescence

was reduced by constructed wetlands treatment. Correlation of PARAFAC results with

water quality parameters was weak, but consistent with previous studies. Results support

the continued study of EEM/PARAFAC towards practical applications in the future.

1

CHAPTER 1 – INTRODUCTION

Dissolved organic matter (DOM) is ubiquitous in aquatic systems and consists of

complex mixtures of proteins and organic acids. They play influential roles in chemical

interaction within their environment and high levels of some organic substances can be

considered pollutants. Traditional chemical analysis is not appropriate for efficient

monitoring of the heterogenic nature of organic substances in natural and wastewaters.

Fluorescence spectroscopy has become an important tool for additional characterization

of organic matter over more general measurements such as dissolved organic carbon

(DOC) and biochemical oxygen demand (BOD).

Fluorescence spectroscopy is a rapid, sensitive, non-invasive approach to studying

fluorescent organic substances (Bro 2005). Fluorescing compounds are commonly

referred to as fluorophores in the literature and throughout this thesis. Excitation-

emission matrix (EEM) fluorescence spectroscopy has been used since the early 1990s

for studying fluorescent matter in marine environments (Coble 1990; Coble et al. 1993;

Mopper and Schultz 1993). The process involves exciting a sample over a range of

wavelengths and recording the fluorescence emission over another range of wavelengths.

Combining the data produces a contoured map, often referred to as a “fingerprint”

displaying fluorescent peak locations and intensities. The peak locations indicate the

type of fluorescent substance and the intensity represents the concentration.

Additional studies have examined the fluorescing properties from rivers (Yan et

al. 2000), urban watersheds (Holbrook et al. 2006), municipal wastewater (Saadi et al.

2006), landfill leachate (Baker and Curry 2004) and industrial discharge (Baker 2002).

2

This has led to the identification of several organic constituents using EEM fluorescence

spectroscopy including humic acid, fulvic acid, tryptophan and tyrosine. They are

commonly referred to as humic acid-like or tyrosine-like. This is because additional

chemical analysis was not performed to verify it was indeed that fluorophore producing

the signal. For this reason, fluorophores are referred to with ‘like’ suffixes throughout

this thesis. Based on consistent documentation within the literature, it is acceptable to

associate these fluorophores with the representative peak locations.

A single EEM can contain several thousand data points. For many applications, it

is necessary to evaluate hundreds EEMs to compare organic composition. Traditional

“peak-picking” methods for interpretation of EEMs are inefficient and unreliable. In

recent years, a research group led by Stedmon, Markager et al. have coupled EEM

fluorescence spectroscopy with Parallel Factor Analysis (PARAFAC) to effectively

model fluorescence spectra. The method decomposes a large data set of combined EEMs

into separate components – representing fluorescent groups. Individual EEMs are

decomposed into the same components and scores are assigned representing the

concentration of each fluorescent group.

This EEM/PARAFAC analysis creates potential for further understanding of the

dynamic composition of organic matter in aquatic systems. It has recently been applied

to trace photochemical and microbial reactions with organic matter (Stedmon and

Markager 2005), in water source classification (Hua et al. 2007), correlation with water

quality parameters (Holbrook et al. 2006), and correlation with disinfection by-product

formation potential (Hua 2006).

3

EEM/PARAFAC analysis of organic matter has only recently been studied and

could be used in the future for various monitoring purposes. Water treatment process

monitoring and landfill leachate monitoring are two such applications and will be the

focus of this study. As stated by Baker et al. (2004), little research has investigated the

fluorescence properties of landfill leachate.

The scope of this thesis includes bi-weekly sampling of five Columbia, Missouri

area locations over a one-year period. Landfill leachate was collected from the Columbia

Sanitary Landfill. Three wastewater samples were collected from the city’s constructed

wetland treatment area. One of these was effluent from the Columbia Regional

Wastewater Treatment Plant (CRWWTP). Another wastewater sample was taken from

effluent of the first constructed wetlands treatment unit. The final wastewater sample was

taken from final effluent discharge after completing the entire treatment process. The

fifth sample taken was a Missouri River sample collected at the Eagle Bluffs

Conservation area.

Samples were analyzed with a fluorescence spectrophotometer and EEMs were

created for each sample. Fluorophores were identified by peak locations that were

consistent with recent literature. EEMs were combined and arranged into proper format

for PARAFAC modeling using in-house programs written by Dr. Bin Hua of the Civil

and Environmental Engineering Department, University of Missouri-Columbia.

PARAFAC analysis was performed using the N-Way Toolbox for MATLAB (Andersson

and Bro 2000).

PARAFAC results demonstrated clear classification among the three sample types

(landfill leachate, wastewater, river water) based on their organic composition. The

4

method provided a means to monitor the organic composition from the five locations

over the one-year period and examine seasonal variation. Transformations in organic

content from wastewaters were tracked along the constructed wetlands treatment process.

Correlations between PARAFAC component scores and water quality parameters

(provided by CRWWTP) were also examined.

5

CHAPTER TWO – LITERATURE REVIEW

2.1 Fluorescence Spectroscopy Analysis

Fluorescence spectroscopy has been used as an analytical tool by scientists for

many years. The process involves the excitation of molecules in a sample with a high

energy source and recording the spectroscopic reaction as the excited molecules release

energy through fluorescence. The excited molecules have several modes for releasing

energy other than by fluorescence. Most substances do not have the ability to fluoresce at

all. However, waters containing high levels of dissolved organic matter are capable of

producing fluorescence. Fluorescence spectroscopy studies using waters high in natural

organic matter have recently grown because of commercially available

spectrophotometers and development of new data evaluation methods.

2.1.1 Description of Analysis

During fluorescence spectroscopic analysis, molecules in a sample undergo

electronic transitions based on their bonding structure. Certain bonding types in

molecules create higher probability of fluorescence emission. Aromatic compounds are

the most likely to fluoresce because of their delocalized pi bonding. Strongly localized

sigma bonding structure does not enable fluorescence emission. A general description of

the molecular changes that occur during analysis is now provided from Sharma and

Schulman (1999) . A visual representation of the process is provided in Figure 2.1.

6

Ground State – All molecules are assumed to be in their lowest possible

vibrational state. The sample to be analyzed is at thermal equilibrium with its

environment prior to excitation.

Excitation – The sample is excited by a high energy light source such as a xenon

lamp over a range of frequencies. Electrons in the sample molecules absorb energy from

the light source and are promoted to unoccupied higher energy orbitals. This transition

raises the molecule to several possible excited singlet states (S1, S2). These excited

states are further divided into vibrational sublevels representing various vibrational states

(V0, V1, V2, V3).

Relaxation of Excited Molecules – Molecules in higher vibrational states will lose

energy through vibration until reaching the lowest vibrational level of its corresponding

excited singlet state. Once this V0 level is reached, there are two likely mechanisms by

which the molecule will drop to a lower energy level – internal conversion or tunneling.

Energy differences between upper and lower excited levels dictate which mechanism will

be utilized. Vibrational relaxation will then occur as before until the V0 level of the

current energy state is reached.

Lowest Excited Singlet State – The above processes occur until the molecule is in

the lowest excited singlet state S0. At this point, the molecule will proceed to its initial

ground state by three possible mechanisms: internal conversion as before, singlet-triplet

intersystem crossing where the electron spin is altered, or through fluorescence.

Fluorescence can arise when there is an appreciable difference in energy between the

lowest excited singlet state and the ground state.

7

Fluorescence Emission – When deactivating from the lowest excited singlet state

to the ground state, the molecule may emit visible or ultraviolet fluorescence.

Vibrational relaxation will then occur as before until the lowest vibrational level of the

ground state is reached.

Figure 2.1 Electronic transitions of an excited molecule from Sharma et al. (1999)

There are several factors that may affect the fluorescence yield during analysis

including temperature, pH, the presence of fluorescence quenchers and

primary/secondary inner filtering effects. Ahmad and Reynolds (1995) determined that

only large variations in temperature and pH will significantly impact the fluorescent

matter of sewage wastewaters. However, Westerhoff, Chen et al. (2001) discovered a 30-

40% decrease in fluorescence of tertiary treated wastewaters when the pH was lowered

8

from 7 to 3. The effect of metal ion quenching has been noticed in samples containing

even low (0.2 ppm) concentrations of Cu2+ and Ni2+ (Ahmad and Reynolds 1995). The

study found that low concentrations of Cu2+ and Ni2+ attenuated fluorescence signals up

to 40% in untreated sewage wastewaters. Dissolved oxygen is also known as a

fluorescence quencher, but is not an important factor in samples containing high levels of

organic matter.

Primary inner-filtering and secondary inner-filtering (reabsorption) are important

issues in the fluorescence spectroscopy analysis of samples high in organic matter

content. Primary inner-filtering describes an attenuation of the excitation light source

traveling through the sample before reaching the center interrogation zone (where the

measurement is collected). Secondary inner-filtering refers to the reabsorption of emitted

fluorescence after excitation (Tucker et al. 1992). Mobed, Hemmingsen et al. (1996)

suggest a heavily cited absorbance method for reducing inner-filtering effects.

Fluorescence spectroscopy analysis assumes the sample is optically dilute; therefore

absorbance data should be collected prior to fluorescence analysis in samples of known

high organic content. High absorbance values indicate the samples should be diluted

prior to fluorescence analysis.

2.1.2 Excitation Emission Matrices

Creating an excitation-emission matrix (EEM) is a method for displaying

fluorescence data. Fluorescence emission intensity is displayed over a range of excitation

wavelengths. This produces a “fingerprint” or a “contoured map” which displays peak

locations and intensities.

9

An example is displayed in Figure 2.2. The location of the peak indicates the type

of molecule (fluorophore) emitting the fluorescence. Peak intensities represent

concentrations of the fluorophore in the sample. However, quantification of exact

fluorophore concentration is difficult to accomplish because of interference with

additional compounds, quenching and other factors influencing fluorescence yield

(Mayer et al. 1999). The EEM method has worked well in detecting types of fluorescent

matter and relative concentrations- leading to potential in monitoring, source

classification and other applications.

The use of EEM fluorescence spectroscopy for identifying natural organic matter

began in the early 1990’s. Studies led by P. Coble (1990), (1993) and K. Mopper (1993)

used EEMs to investigate seawater in dynamic estuary environments. By creating

fingerprints of different sample locations (i.e. surface waters, deep water column),

organic matter fractions can be compared leading to a better knowledge of organic matter

distribution. A growing number of studies are now employing EEM fluorescence

spectroscopy for various applications. Commercial availability of fluorescence

spectrometers and useful software programs has led to quick analysis and EEM data.

An example of an EEM is provided in Figure 2.2. The linear features represent

first and second order Raleigh light scatters. The line on the left side is from first order

Raleigh light scattering due to molecules oscillating at the same frequency as the incident

light which leads to the emission at that same wavelength (Rinnan et al. 2005). Second

order scattering is emission at twice the incident wavelength and is seen on the right side

of the graph. Since these features do not represent organic matter fluorophores, they

should be removed prior to EEM data modeling.

10

Figure 2.2 Example of an excitation-emission matrix (EEM)

2.2 Fluorescent Substances

Various substances have been identified by creating EEMs including polycylic

aromatic hydrocarbons (Nahorniak and Booksh 2006) and pesticides (Jiji et al. 1999).

However, natural waters contain two primary fluorescing groups derived from dissolved

organic matter (DOM): humic-like and protein-like.

The humic-like fluorescing group is composed of humic substances – fulvic acids

and humic acids. These humic substances are a complex mixture of aromatic and

aliphatic compounds which are formed through the decay of organic matter. Fulvic acids

are characterized by greater aliphatic content and are soluble at any pH. Humic acids are

dominated by aromatic content and precipitate at a pH level below 2. The generic

empirical formula for humic acid is C187H186O89N9S for humic acids and C135H182O95N5S2

for fulvic acids. A generic structure for humic acids from (Watts 1998) is provided in

Figure 2.3 They are ubiquitous in the environment and make up the largest fraction of

11

organic matter in natural water – approximately 40-60% of dissolved organic carbon

consists of humic substances (Senesi 1993). Physical and chemical characteristics such

as surface activity and hydrophobic/hydrophilic sites create high potential for chemical

interaction. Pesticides and other organic pollutants may react with humic substances thus

altering their rate of dissolution, volatilization, transfer to sediments, biological uptake

and bioaccumulation, or chemical degradation (Senesi 1993). Because of their influential

role in the environment, it is necessary to study and monitor the behavior of humic

substances.

Figure 2.3 Generalized structure of humic acids from (Watts 1998)

The second primary group of organic matter detected in excitation-emission

matrices of natural water is described as protein-like. This group consists of two

dissolved amino acids which produce a fluorescence signal – tryptophan and tyrosine.

Possible sources for these proteins include estuaries which support high biological

12

activity, and waters receiving wastewater treatment plant discharges or some types of

industrial discharges.

Several studies performed in the 1990’s have successfully identified these two

fluorescent groups using excitation-emission matrices (Mopper and Schultz 1993; Coble

1996; Mayer et al. 1999). Humic-like substances produce fluorescent peaks at emission

wavelengths of 420-450 nm from excitation wavelengths of 230-260 nm and 320-350

nm. Protein-like substances produce fluorescent peaks at emission wavelengths of 300-

305 nm and 340-350 nm from excitation wavelengths of 220 nm and 275 nm,

respectively. Figure 2.4 provides a visual representation of these peak locations

identified by mentioned studies. The ‘Xeno’ label represents xenobiotic-like fluorophore

location and will be discussed in the next section.

Initial studies by Coble and others used sea water samples for identification of

humic-like and protein-like substances. Other studies have used freshwater sources (Yan

et al. 2000), sewage samples and sewage impacted streams (Baker 2001), and landfill

leachate sources (Baker 2005), (Baker and Curry 2004). Similar peak locations for

humic-like and protein-like substances were found.

2.2.1 Fluorescence Characteristics of Landfill Leachate

Landfill leachate is the liquid that has percolated through solid wastes in a landfill

extracting biological and chemical constituents. The chemical composition of landfill

leachate varies due to primary waste inputs (municipal waste, industrial waste, etc.) and

also due to landfill age. Landfill leachates typically contain high values of BOD, COD,

TOC, ammonia, heavy metals and other pollutants.

13

Figure 2.4 Fluorescent centers of humic-like, protein-like and xenobiotic-like substances from various sources

Concentrations of these pollutants tend to decrease as the landfill matures

(Tchobanoglous et al. 1993). The results of Kang et al. (2002) supported this when

evaluating water quality parameters from a young landfill (<5 years old), medium-aged

landfill (5-10 years old) and mature landfill (>10 years old). Concentrations of COD,

BOD, DOC, solids and ammonia all decreased with landfill age. The main objective in

the study was to characterize the humic substances present in these three contrasting

200

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280

320

360

400

440

480

520

250 280 310 340 370 400 430 460 490 520 550 580

Emission Wavelength (nm)

Exci

tatio

n W

avel

engt

h (n

m)

Protein-Like

Humic-Like

Xeno

14

landfills. Their studies determined that the aromatic character and molecular size of

humic substances is higher in leachates from older landfills – indicating an increase in

humification as a landfill matures.

These humic-like substances can produce fluorescence in landfill leachates as

well as protein-like substances. However, a third fluorescent group has been found to

dominate the fluorescent composition of most landfill leachates. Baker et al. (2004)

created excitation-emission matrices from three landfill leachates and reported an intense

fluorescence signal at emission wavelengths of 340-370 nm from excitation at 220-230

nm. They suggest this fluorophore is produced from a xenobiotic organic matter fraction

such as naphthalene. The intensity of this fluorescent peak was highly correlated with

ammonia concentration (r = 0.98, 0.95, 0.98) for all three landfills evaluated, and for

BOD5 (r = 0.98, 0.94) for two of the landfills evaluated.

Baker and Curry (2004) stated that the fluorescence properties of landfill leachate

have not been adequately studied. They suggest further study of possible pollutants

producing the xenobiotic fluorescent peak and the analysis of additional landfill leachates

to develop a database of source fluorescence properties.

2.2.2 Fluorescence Characteristics of Municipal Wastewater

Municipal wastewater and treated effluent is characterized by high levels of

organic matter. This results in elevated levels of biochemical oxygen demand (BOD),

chemical oxygen demand (COD), ammonia and nutrients. Since many organic

components have demonstrated the ability to fluoresce, sewage treatment plant effluents

15

and streams impacted from plant discharges have been popular candidates for

fluorescence EEM spectroscopy analysis.

Although there are many possible fluorescing species in wastewater, several

studies have identified fluorescent characteristics common to all wastewaters. The most

important of these characteristics is a protein-like fluorescent peak located at or very

close to 340nm emission from excitation at 280 nm (Reynolds and Ahmad 1997), (Baker

2001), (Baker 2002), (Reynolds 2003), (Arunachalam et al. 2005), (Saadi et al. 2006).

These groups believed that the protein-like fluorophore could be attributed to the

amino acid tryptophan. This suggestion was supported by Reynolds (2003) who used

high performance liquid chromatography (HPLC) to measure tryptophan concentrations

in treated wastewater. These values were then correlated with fluorescent peak

intensities using synchronous fluorescence spectroscopy (SFS) at the 280nm EX/340nm

EM location (R2=.99). SFS measures the same spectral characteristics as EEM

spectroscopy. Peak quantification is simpler using SFS, but matrix “fingerprints” are not

created which makes fluorophore identification more difficult.

Tryptophan is one three aromatic amino acids- the other two being tyrosine and

phenylalanine. Less work has investigated the fluorescence properties of these other two

amino acids in wastewaters but their aromatic structure creates potential for fluorescence

emission. Tyrosine has been linked to WWTP effluent and streams impacted from

effluent discharges in limited studies. Stedmon and Markager (2005) suggest tyrosine

produces a fluorescent peak at 305nm emission from excitation at 280nm. The group

reported almost identical fluorescent peaks of free tyrosine dissolved in water to a source

derived from autochthonous processes in a marine estuary.

16

The 280nm EX/ 340nm EM peak was investigated in earlier studies when it was

still not clear if tryptophan produced the signal. Reynolds and Ahmad (1997) reported

high correlations of this peak intensity with BOD concentrations (R2= .89 - .94) for three

wastewater treatment plant sources. This correlation was slightly lower than UV

absorbance at 254 nm (a more conventional surrogate for BOD analysis) correlated with

BOD (R2= .87 - .97). Baker et al. suggest that tryptophan fluorescence intensity is a more

valuable tool than UV absorbance at 254nm for fingerprinting sewage impacted waters

(2001). Correlations of tryptophan fluorescence intensity with other parameters TOC,

COD (Reynolds and Ahmad 1997) and ammonia (Baker 2002) have been attempted with

only limited success.

Monitoring the intensity of the tryptophan fluorescent peak throughout treatment

process stages has recently been examined. The use of online fluorometers has been

suggested as a valuable tool for wastewater treatment operators to monitor treatment

levels as influent sewage proceeds through a treatment system. Arunachalam et al.

(2005) reported a tryptophan peak intensity reduction that paralleled volatile solids

reduction in an aerobic digestion monitoring study. The two parameters were modeled

with a semi-empirical exponential decay equation- thus implying the tryptophan peak

intensity measurement could be used as a substitute for volatile solids concentration.

Saadi et al. (2006) reported diverse results when the group monitored the

tryptophan peak intensity in wastewater throughout a microbial degradation process. The

peak intensity at first decreased, but displayed an overall increase when final

measurements were made 60 days following the start of the experiment. The group

17

attributed this increase to either the creation of new fluorescing compounds through the

decay process, or to a reduction in quenching compounds.

The two studies had various differences that will not be discussed, but the results

support the need for further understanding of wastewater fluorescence properties. There

is currently no information regarding the direct impact that constructed wetland

wastewater treatment may have on the tryptophan peak levels in effluent.

2.2.3 Fluorescence Characteristics of River Water

The fluorescent properties of rivers are varying and largely dependant upon the

specific sources for river input. Sewage (Baker 2001), industrial discharges (Baker 2002)

and landfill leachate (Baker 2005) have been successfully traced in river waters using

fluorescence spectroscopy.

However, ‘cleaner’ river waters, not impacted from anthropogenic activity, are

also capable of fluorescence emission because of dissolved natural organic matter.

Humic substances are comprised of humic acids and fulvic acids that are ubiquitous in

natural waters. Dissolved aquatic humic substances make up the largest fraction of

natural organic matter in water. Approximately 40-60% of the dissolved organic carbon

in water is from humic substances. Approximately 60-70% of total soil organic carbon is

comprised of humic substances (Senesi 1993). These values vary greatly depending on

the types of water bodies (wetlands, marine, etc.).

These humic substances found in aquatic systems may come from decaying plant

and animal matter. Terrestrially derived organic matter from soils deposited into rivers

can dissolve and also add to the fluorescence potential. Fluorescence spectroscopy

18

studies of water sources have found fulvic acids to dominate the organic content of

natural waters (Ma et al. 2001) by separating organic fractions using reverse osmosis and

ion exchange. Baker et al. (2001) observed a sharp increase in the tryptophan/fulvic

fluorescence intensity ratio in sewage impacted rivers when compared to non-effected

waters upstream. In general, it is assumed that river water with limited impact from

anthropogenic activity derives fluorescence potential from naturally occurring humic

substances.

2.3 Parallel Factor Analysis

It is clear that excitation-emission matrices are excellent visual tools for

qualitatively identifying fluorophores and examining the organic composition of a

sample. It is less obvious, but should be noted that EEMs can be very useful for

quantification of organic matter fractions as well. Over the last decade, parallel factor

analysis (PARAFAC) has become the most important tool for analyzing fluorescence

EEM data sets. PARAFAC is a multi-way method for decomposing sets of EEMs into

components representing fluorescent groups. Other multivariate models such as principal

component analysis (PCA) have been used to model fluorescence spectra (Thoss et al.

2000; Persson and Wedborg 2001; Baker and Curry 2004). PCA is incapable of

producing a unique solution and therefore cannot predict pure spectra. This is because

PCA is marked by rotational freedom, where an infinite number of solutions will give the

same model fit. PARAFAC does not have this problem because of its three-way nature,

and unique solutions representing fluorescence spectra can be obtained (Bro 1998-2002).

19

A very basic method for analyzing EEM data involves scrolling to peak locations

on the matrix and finding the peak intensity. There are major problems with this “peak-

picking” method. The number of data points in a single EEM equals the number of

excitation wavelengths used multiplied by the number of emission wavelengths. A

typical EEM may consist of around 10,000 data points. If many EEMs with varying

composition are to be interpreted, peak-picking turns into an inefficient and unreliable

method.

PARAFAC has recently become the most widely used tool for modeling

fluorescence spectra. This is possible because fluorescence data of dilute samples behave

in approximate accordance with a PARAFAC model (Bro 1998-2002). Bro (1997) has

published a tutorial explaining the multi-way decomposition method and describes

fluorescence EEM applications among others. A set of EEMs, when combined, consists

of excitation data and emission data for a number of samples. An example is 20 samples

consisting of 80 excitation wavelengths and 100 emission wavelengths. This three-way

nature is appropriate for PARAFAC modeling which decomposes the data matrix into a

set of trilinear terms and a residual array as

1

F

ijk if jf kf ijkf

a b cχ ε=

= +∑ (1)

where χijk is the intensity of fluorescence for the ith sample at emission

wavelength j and excitation wavelength k, aif is the concentration factor of the fth

analyte in sample i, bif is the fluorescence quantum efficiency factor describing the

20

amount of absorbed energy emitted as fluorescence, and ckf is the specific absorption

coefficient at excitation wavelength k. F defines the number of components in the model.

The solution to the model is found by minimizing the sum of squares of residuals

represented by the residual array εijk.

If the correct number of components is chosen, then the underlying spectra will be

found given by three vectors indicating component score, emission loading and excitation

loading. Each sample will be assigned a score for each of the components chosen.

Because each component represents a specific fluorescing group, the organic composition

of the data set is resolved. Choosing the correct number of components is an important

task and several methods of assistance are available. The function Core Consistency

Diagnostic (CORCONDIA) (Bro and Kiers 2003) is a simple tool to use (although the

theoretical understanding of the method is not yet complete) to validate the appropriate

number of components in a PARAFAC model. CORCONDIA output indicates if the

model has explained trilinear variation between the three-dimensional EEMs, or just

random variation which is not trilinear.

Split-half analysis is another method in which the EEMs are divided into two

groups and separate PARAFAC models are found. If the correct number of components

was chosen, then both models should produce similar excitation and emission loadings.

Stedmon et al. (2003) were one of the first groups to combine fluorescence EEM

spectroscopy with PARAFAC to resolve organic matter fractions within a watershed. In

this application and others it is impossible to determine concentrations of the fluorophore

components. This is because the organic composition of each sample is a very complex

mixture of several fluorophores. Only relative concentrations between samples in an

21

EEM data set can be compared. In contrast, at a laboratory setting with known

concentrations of isolated fluorophores such tryptophan or tyrosine, calibration

techniques can predict accurate concentrations in unknown samples of the same

fluorescing compound.

2.4 Applications of EEM Fluorescence Spectroscopy using PARAFAC

A research group in Denmark led by C. Stedmon, S. Markager, has coupled EEM

fluorescence spectroscopy with PARAFAC to study dissolved organic matter (DOM).

The team has published several papers in this area that has led to further study (Holbrook

et al. 2006; Nahorniak and Booksh 2006; Hua et al. 2007) as well as this thesis.

The technique is appropriate for examining DOM because PARAFAC derived

components represent fluorophores studied in previous research. Stedmon et al. (2003)

states “the agreement between model components and previously identified peaks is

encouraging and suggests that PARAFAC modeling is an effective method of

characterizing DOM with EEMs”. In that study PARAFAC produced five components.

Based on component peak location, three components were believed to represent humic

substances and one was believed to represent the protein tryptophan (one was

unidentified). Stedmon and Markager (2005) used an expanded data set with 1,276 EEM

samples over a one year period. The larger data set accounted for seasonal variation and

was able to identify an additional two components (seven total) from the same area of the

previous study. Four components represented humic groups, two components

represented fulvic acids, one was linked to tryptophan and one was linked to tyrosine.

Holbrook et al. used 55 EEM samples and were able to validate three PARAFAC

22

components. Component one was similar to component three of Stedmon et al. (2003)

which was attributed to a humic-like fluorescent group. Component two was also

comparable to previous work and representative of fulvic-like material. Component 3

was similar to the protein fluorophore identified as Component 5 in the Stedmon and

Markager (2005) study.

This agreement between PARAFAC components and specific fractions of DOM

enables EEM/PARAFAC techniques for use in many applications. Separate sources or

different areas within the same watershed can be characterized based on their

composition of PARAFAC components. This was accomplished in the Stedmon et al.

and Holbrook et al. studies. DOM sources such as wastewater impacted river, forest

stream, urban runoff and marine were uniquely characterized by their PARAFAC

component composition. For example, Stedmon and Markager (2005) reported samples

taken from “forest stream” environments near the Horsens Estuary (East Coast of

Denmark) were dominated by components 1 and 3 which represent humic and fulvic acid

fluorophore groups, respectively. However, samples drawn from a stream near a

wastewater treatment plant discharge were dominated by components 7 and 8

representing the two protein fluorophores – tryptophan and tyrosine and had very low

scores for components 1 and 3.

This identification capability supports the use of EEM/PARAFAC as a valuable

monitoring tool for several applications. Holbrook et al.’s study sampled from several

different land-use areas within the Occoquan Watershed (Northern Virginia, U.S.). Three

components were identified and the distribution of the components at different sites

varied considerably. Component composition of locations that were heavily impacted

23

from anthropogenic activity differed from more natural settings. Their results support the

use of EEM/PARAFAC to monitor human impact on aquatic systems. Another possible

application could monitor water treatment – tracking component composition changes

through a series of treatment processes.

Another aspect of the Occoquan Watershed project was the attempt to correlate

common water quality parameters with component scores. The results were varied but

the group concluded that PARAFAC could be used to provide estimates ( + 30%) of

select analyte concentration in surface waters. Table 2.1 displays correlation results with

select analytes.

For each component, DOC displayed moderate yet stronger correlation compared

with COD concentrations. This indicates EEM/PARAFAC modeling corresponds better

with organically bound carbon as opposed to the oxygen equivalent of organic matter

content (Holbrook et al. 2006). The protein-like component 3 showed moderate

correlation with SKN and no correlation with NH3-N. As stated in (Holbrook et al.

2006), this is consistent with non-humic material enriched in organic nitrogen (defined as

the difference between SKN and NH3-N) that may result from microbial and/or

anthropogenic activity. The group used the correlations from Table 2.1 to produce

multiple and simple regression relationships between water quality parameters and

PARAFAC scores.

Two later studies (Stedmon and Markager 2005; Stedmon et al. 2007) examined

the photochemical and microbial degradation of organic matter using EEM/PARAFAC.

In the 2005 study, 396 marine samples were collected near Bergen, Norway. Samples

24

were subjected to various levels of photochemical degradation (i.e. ultraviolet light alone,

ultraviolet + visible light) and microbial degradation experiments.

Table 2.1 Water quality parameter correlation with PARAFAC component scores, from Holbrook et al. (2006)

PARAFAC Component Water Quality Parameter R2 n

1 Chemical Oxygen Demand (COD) 0.55 44Humic-Like Dissolved Organic Carbon (DOC) 0.58 41

Total Soluble Phosphorous (TSP) 0.56 42CL- -0.71 15SO4

2- -0.76 15Absorption254nm 0.91 55Absorption280nm 0.88 55Humification Index 0.69 55

2 COD 0.54 44Fulvic-Like DOC 0.67 51

TSP 0.59 42Absorption254nm 0.55 55Absorption280nm 0.5 55

3 COD 0.5 44Protein-Like DOC 0.6 51

TSP 0.54 42Soluble Kjeldahl Nitrogen (SKN) 0.69 37Total Dissolved Solids (TDS) 0.6 26Cl- 0.73 15SO4

-2 0.59 15K+ 0.76 32Na+ 0.59 32Conductivity 0.64 52

The different light exposures did not exceed typical environmental exposure.

EEM/PARAFAC analysis resulted in seven components (five humic-like, two protein-

like) that were monitored throughout the tests. In general, results indicated that humic-

like components accumulated from microbial degradation and degraded through visible

25

and ultraviolet (UV) exposure. One of the protein fluorophores which exhibited

tryptophan-like fluorescence properties was degraded through microbial degradation and

UV light only. The sink for the second protein fluorophore was not identified but

aggregation or microbial uptake were hypothesized mechanisms. To be an effective

monitoring tool, further work is necessary to see how EEM/PARAFAC analysis responds

in diverse environments.

26

CHAPTER 3 – MATERIALS AND METHODS

3.1 Sample Collection and Handling

Samples were collected on a bi-weekly basis for a one-year period. The landfill

leachate samples were retrieved out of a tap connecting to a leachate collection well. The

others were taken as grab samples and all were stored in 125 ml polypropylene bottles.

These bottles were cleaned by soaking in HCL and then rinsed with tap water, distilled

water and de-ionized water. The bottles were kept in an ice-packed cooler while

transported and then kept refrigerated prior to analysis. Sample analysis was performed

within 24 hours of collection. Samples were allowed to equilibrate with room

temperature (21 +2 oC) prior to analysis. A replicate sample from one of the five

locations was also taken. A YSI model 58 portable dissolved oxygen meter and a HACH

model portable pH meter were used to make in-situ measurements.

3.2 Creation of Excitation-Emission Matrices

Fluorescence measurements were made with a Hitachi F-4500 fluorescence

spectrophotometer. Excitation-emission matrices were created using FL Solutions

software. Prior to analysis, samples were allowed to equilibrate with room temperature

(21 +2 oC). Next, samples were filtered with Fisherbrand .45 micrometer, nylon syringe

filters. The first 1-2 ml of filtered samples were discarded so that organic surfactants of

the filtering media would not impact spectral measurements.

Wastewater and landfill leachate samples were diluted to correct for inner-

filtering and reabsorption effects. Mobed et al. (1996) explained that absorbance

27

correction is necessary to represent fluorophores from samples high in organic matter.

The group showed how fluorescent peaks may shift to longer excitation and emission

wavelengths due to the attenuation of fluorescence emission. Wastewater samples were

diluted 2x and the landfill leachate samples were diluted 60x. These dilution factors

allowed absorbance values between 250 nm and 550 nm to remain below 0.15. River

water samples did not need to be corrected. Fluorescence measurements were made with

the following parameters:

Table 3.1 Setup parameters for creation of EEMs

Parameter

Scan Mode EmissionData Mode FluorescenceExcitation Wavelength Range (nm) 220 - 540Excitation Step Invertval (nm) 4Emission Wavelength Range (nm) 250 - 600Emission Interval (nm) 3Speed (nm/min) 12,000Delay (s) 0Excitation Shutter Opening (nm) 5Emissiom Shutter Opening (nm) 10PMT Voltage (V) 700Response AutoReplicates 1Shutter Control ONSpectrum Correction ON

The excitation range (220-540nm) and step interval (4nm) resulted in 81

excitation wavelength data points (220nm, 224nm, 228nm…. 540nm). Emission range

(250-600nm) and step interval (3nm) resulted in 117 emission wavelength data points.

The total size of each EEM consisted of 9477 (81*117) data points. Following the

28

creation of EEMs, they were then exported into Excel files and later Sigma Plot files and

MATLAB files for further interpretation and modeling.

A quality assurance check was made prior to each analysis session by performing

a sensitivity and drift check. These two checks were provided in the FL Solutions

software program. The sensitivity check determined signal to noise ratios in the Raman

spectrum of de-ionized, distilled water. Drift checks determined the variation of emission

intensity per unit time. Fluorescence intensities of de-ionized, distilled water at 277 nm

EX/ 303 nm EM were recorded to assure consistent measurements between analyses.

Wavelength accuracy checks were made four times throughout the study to assure

consistent emission from the xenon lamp. The parameters listed in Table 3.2 were set

(according to software guidelines) to analyze a standard diffusion element.

Table 3.2 Fluorescence wavelength accuracy check parameters

Parameter

Scan Mode EmissionData Mode LuminescenceExcitation Wavelength (nm) 0Emission Start Wavelength (nm) 440Emission End Wavelength (nm) 480Scan Speed (nm/min) 60Delay (s) 0Excitation Shutter Opening (nm) 5Emission Shutter Opening (nm) 1PMT Voltage (V) 400Response (s) 0.5Replicates 1Shutter Control OFFSpectrum Correction OFF

29

3.3 Parallel Factor Modeling

Excitation-emission matrices were combined into a single Excel file. Each EEM

was assigned to a separate worksheet, resulting in one Excel file consisting of 118

worksheets. This file was imported into MATLAB along with a deionized water sample

EEM to be subtracted for removal of Raman scattering. Next, the arrays had to be put

into the appropriate format to be modeled by parallel factor analysis (PARAFAC). To

accomplish this, three functions written by Dr. Bin Hua of the Civil and Environmental

Engineering Department (2005) were executed. The first of these datinf, arranged the

EEMs horizontally side by side and subtracted the DI “blank” from each sample EEM.

The scatter function was implemented in order to remove the first and second order

Raleigh scatters. Values outside of the boundaries formed by the Raleigh scatters were

assigned “NaN” values (Not a Number). The reason for this is because the values in this

area do not describe organic matter fluorophores as explained in 2.1.3. The fluor

function transforms the two-dimensional “chain” of EEMs into a three-dimensional

“stacked” matrix of 117 emission wavelengths x 81 excitation wavelengths x 118

samples. Figure 3.1 displays a MATLAB EEM with Raman and Raleigh scattering

removed.

PARAFAC modeling was carried out using the N-Way Toolbox for MATLAB

found here http://www.models.kvl.dk/source/nwaytoolbox/ (Andersson and Bro 2000).

Non-negativity constraints were imposed on all three output modes: concentration,

emission and excitation. This is appropriate because negative concentrations and

wavelengths are not appropriate in the analysis of fluorescence spectra. The number of

30

components modeled by PARAFAC is a predetermined decision made by the user. The

model was run for 1-8 components.

Figure 3.1 EEM created in MATLAB, Raleigh and Raman light scattering removed

In order to validate the correct number of components, split half analysis was

performed by dividing Excel file into two equal halves. Both groups had 57 EEMs and

had the identical number of each type of sample. For example, group 1 and group 2 each

contained 12 randomly selected wastewater treatment plant effluent (WW) samples.

Formatting and modeling were performed in the same fashion as the entire data set.

Excitation and emission loadings were examined in order to validate the appropriate

number of components. The Core Consistency Diagnostic (CORCONDIA) output from

PARAFAC was also used in determining the correct number of components.

250 300 350 400 450 500 550

250

300

350

400

450

500

Wetlands Unit 4 Effluent - EEM

Emission Wavelength (nm)

Exc

itatio

n W

avel

engt

h (n

m)

31

3.4 Water Quality Analysis

Two rounds of water quality testing were performed for this study- once in

summer and once in winter. Analysis was performed in the Missouri Water Resources

Research Center Laboratory – Civil and Environmental Engineering Department,

University of Missouri (except for DOC).

Absorption at 254 nm

Absorption analysis was done with a Varian CARY model 50 Conc UV-Visible

spectrophotometer. Absorption scans were created using Cary WinUV software program

with a wavelength range from 250nm – 550 nm. Baseline correction was done based on

assumed 100% transmittance for a DI water scan. Absorption at 254nm was identified

from the scans and recorded.

Ammonia Nitrogen (NH3-N)

Ammonia concentrations were determined using HACH Method 8155 – Nitrogen,

Ammonia Salicylate Method. Spectrophotometric readings were done with a HACH

model DR/2400 portable spectrophotometer. Wastewater and leachate samples required

large dilutions to say within the detection range (0.01 - 0.5 mg/L). Duplicate samples

were run for summer tests and triplicate samples were run for winter tests.

Biochemical Oxygen Demand (BOD)

BOD measurements were made according to APHA Standard Methods for the

Examination of Water and Wastewater (APHA, 1998). HACH products were used to

create buffer solution and nutrient seed solution. Dissolved oxygen was measured on a

32

YSI model 5000 DO meter before and after the 5-day incubation at room temperature (21

+2 oC). Duplicate samples were run for summer tests and triplicate samples were run for

winter tests.

Chemical Oxygen Demand (COD)

Analysis was performed using HACH COD digestion solution (0-1500 ppm

range). A HACH COD reactor incubated the samples for 2 hours prior to spectroscopic

analysis using HACH method 435 on a DR 2000 model spectrophotometer. Duplicate

samples were run for summer tests and triplicate samples were run for winter tests.

Conductivity/ Total Dissolved Solids (TDS)

Both parameters were measured on a HACH Model 44600 Conductivity/TDS

meter.

Dissolved Organic Carbon (DOC)

Samples were taken to the Columbia Drinking Water Treatment Plant for DOC

analysis. A Phoenix model 8000 was used. The instrument was unavailable for winter

testing.

Total Suspended Solids (TSS)

TSS measurements were made according to APHA Standard Methods for the

Examination of Water and Wastewater (APHA, 1998). Fisher G4 glass fiber filter papers

were triple rinsed with DI water and dried prior to use. After filtration, the papers and

residue were dried at 105oC and desiccated until stable mass readings were obtained.

Duplicate samples were run for summer tests and triplicate samples were run for winter

tests. However, wastewater samples were excluded from winter testing because large

amounts of wastewater were needed to yield an appropriate residue mass. This was

33

because grab samples were taken from the surface where solid concentrations were

lacking due to settling.

34

CHAPTER 4 – SAMPLING SITE CHARACTERISTICS

4.1 Site 1 – City of Columbia, Missouri Sanitary Landfill

The municipal solid waste landfill is located approximately 8 miles northeast of

the University of Missouri campus at 5700 Peabody Rd., Columbia, Missouri, 65202.

The facility has been in operation since 1986 and consists of 6 cells. A pump was used to

obtain samples out of an underground leachate collection system (Figure 4.1). The

samples were drawn from Cell 2 which was opened in January 1999 and closed in

October of 2002.

Summer Fall

Figure 4.1 Site 1 – Landfill leachate (LL)

3.2 Sites 2, 3, 4 – Columbia Regional Wastewater Treatment Wetlands Area

The city of Columbia uses constructed wetlands for polishing treatment of

wastewater. This area is located near the Columbia Regional Wastewater Treatment

plant, approximately 8 miles southwest of the University of Missouri campus. The plant

is designed to handle 20.4 million gallons per day (60 MGD peak) and utilizes a

35

completely mixed activated sludge treatment process. The effluent from the treatment

plant is then sent to the constructed wetlands for “polishing” (Columbia 2002).

The constructed wetlands provide additional substrate removal through a

combination of processes. Microbial degradation of organic matter is the primary

removal mechanism. Organic substrate can also sorb onto soils and are subject to plant

uptake (Lorion 2001). Cattails (Typha latifolia) are the primary plant specie used in the

city’s constructed wetlands.

There are a total of 4 wetland units that are further divided into multiple cells.

Sample collection Site 2 – Wastewater Treatment Plant Effluent (WW) is located at the

constructed wetlands Unit 4 influent (Figure 4.2). The wastewater flow proceeds from

Unit 4 – Unit 1 – Unit 2 – Unit 3. They are not labeled in numerical order because Unit 4

was not originally part of the treatment system, but was a later addition. However,

Wetlands Treatment Unit 4 is the first wetland unit that the water passes through. Craig

Cuvellier, process scientist at the plant states that most of the constructed wetlands

treatment takes place in this first unit. Therefore, Site 3 – Wetlands Treatment Unit 4

Effluent (U4) samples were collected (Figure 4.3).

The next sampling location is Site 4 – Wetlands Treatment Effluent (WE). This

site is located at the constructed wetlands pump station (Figure 4.4). These samples had

passed through all four units of the constructed wetlands system.

36

Summer Winter

Figure 4.2 Site 2 – Wastewater treatment plant effluent (WW) Summer Winter

Figure 4.3 Site 3 – Unit 4 wetlands effluent (U4)

Summer Winter

Figure 4.4 Site 4 – Wetlands effluent (WE)

37

Figure 4.5 Map of constructed wetlands located southwest of Columbia, Missouri, from (Columbia 2004)

4.3 Site 5 – Missouri River at Eagle Bluffs Conservation Area

Effluent from the wetland treatment system is used as a water source for natural

wetlands system in the Eagle Bluffs Conservation Area. This area consists of 13 wetland

pools bordering the Missouri River, located just south of the wastewater treatment

constructed wetlands. The total land space is approximately 4300 acres (Conservation

2001). Access to banks along the Missouri River is provided. Site 5 – Missouri River at

Eagle Bluffs Conservation Area samples were taken from one of these access locations.

Sample source information is provided in Table 4.1 and site locations relative to the

University of Missouri campus are shown in Figure 4.7.

38

Summer Winter

Figure 4.6 Site 5 – Missouri river (MR) at Eagle Bluffs Conservation Area

Table 4.1 Project sampling locations

Site Number Description Location

1 Landfill Leachate (LL)Eight miles northeast of campus, 5700 Peabody Rd., Columbia, MO, 65202

2 Wastewater Treatment Plant Effluent (WW)Wetlands treatment area, 10 miles southwest of campus

3 Wetlands Treatment Unit 4 Effluent (U4)

4 Wetlands Treatment Final Effluent (WE)

5 Missouri River (MR)Eagle Bluffs Conservation Area, 10 miles southwest of campus

39

Figure 4.7 Sampling Locations

40

CHAPTER 5 – RESULTS AND DISCUSSION

Experimental results are divided into three sections: water quality, fluorophore

identification and PARAFAC modeling. The primary objectives for this thesis are based

on PARAFAC modeling of organic matter content from the five sample locations.

However, several water quality parameters were also tested in order to strengthen

understanding of each site. Initial fluorophore identification based on visual EEM

inspection is also discussed before modeling results are presented.

5.1 Water Quality Parameters

Water quality results from the sample locations were not considered critical

information for this study. Daily water quality parameters for wastewater treatment plant

effluent and for final effluent from the constructed wetlands were shared from Craig

Cuvellier of the Columbia Regional Wastewater Treatment plant. These parameters were

biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended

solids (TSS), ammonia-nitrogen (NH3-N), pH, and water temperature. Discussion of

some of these parameters and correlation results with selected PARAFAC components is

provided later.

Since water quality information was provided for wastewater samples, additional

water quality analysis was performed on the entire group of samples. The results were

intended to be used as a basic means for differentiating water quality between the three

sample sources: landfill leachate, wastewater and river. The results were not intended to

provide a detailed examination of the aquatic analyte properties of these sources.

41

Because of this, extensive quality assurance checks were not performed. For the summer

tests, duplicate samples were analyzed and results agreed with 12% for all tests. During

winter testing, triplicate samples were analyzed for BOD5 and NH3-N and standard

deviations are provided. Inappropriate dilutions were used for wastewater and leachate

BOD winter tests indicating that actual BOD levels are higher than the estimated

concentrations seen in Table 5.2. However, the water quality results do provide a useful

means for comparing characteristic water quality from the three sources. The results for

summer testing and winter testing are displayed in Table 5.1 and Table 5.2.

In general, the results were as expected. Landfill water quality was much worse

than wastewater and river water. It is characterized by high levels of organic matter,

solids and ammonia. Ammonia may be the most significant long-term pollutant from

landfills, as reported from Baker et al. (2005).

In contrast, water quality from the Missouri River is much better than the landfill

and wastewater sources. River samples were drawn several miles upstream from the final

constructed wetlands discharge into the Missouri River, therefore water quality is not

impacted from the wastewater.

42

Table 5.1 Summer water quality results

BOD5 COD TSS TDS Conductivity DOC N, Ammonia Abs @ 254nmmg/L mg/L mg/L g/L mS/cm mg/L mg/L

Landfill Leachate 22.3 1100 188 5.75 11.60 126.7 160.00 1.650

Wastewater Effluent 12.0 59 9.2 0.93 1.85 13.55 12.80 0.400

Wetlands Unit 4 7.6 54 6.8 0.92 1.84 10.8 15.20 0.275

Wetlands Effluent 4.0 52 4.8 0.90 1.80 11.21 14.40 0.308

Missouri River 1.7 19 113 0.42 0.84 3.55 0.02 0.112

Table 5.2 Winter water quality results

BOD5 COD TSS TDS Conductivity N, Ammonia Abs @ 254nmmg/L mg/L mg/L g/L mS/cm mg/L

Landfill Leachate >40 805 + 45 110 + 4 6.48 12.94 228 + 8 2.050

Wastewater Effluent >18 95 + 1 1.19 2.36 9.5 + 0 0.271

Wetlands Unit 4 >18 92 + 7 1.13 2.25 13.8 + 0.6 0.254

Wetlands Effluent 12.4 + 0.6 62 + 3 1.10 2.21 13.7 + 0.8 0.225

Missouri River 1.7 + 0.4 22 + 7 63 + 7 0.50 0.99 0.21 + 0.02 0.076

42

43

5.2 Fluorophore Identification

Figures 5.1, 5.2 and 5.3 display typical EEMs for samples collected during this

study. Peak locations and suggested fluorophores represented by them are provided in

Table 5.3. Prior to analysis landfill leachate samples were diluted 60x, wastewater

samples were diluted 2x and no dilution was done on river water samples. Dilutions were

done to correct for inner-filtering effects described in section 3.2. Because of these

dilution factors it should be noted that leachate fluorophores produce much greater

fluorescence than wastewater and river water. The scales of the EEMs listed below are

not consistent. The purpose of the following discussion is to identify location of

fluorescent centers and compare those locations with previously identified peaks and

their represented fluorophores from recent literature.

5.2.1 Wastewater Fluorophore Identification

Figure 5.1 shows a typical wastewater EEM. Samples drawn from the two

constructed wetlands locations as well as the WWTP effluent can all be represented by

this EEM. The differences between these three wastewater EEMs (WWTP effluent, unit

4 effluent, final wetlands effluent) is based on fluorescence peak intensity, not location

and will be explained later. The four labeled peaks indicate humic acid-like, fulvic acid-

like and protein-like fluorophores. Their locations are very similar previously identified

fluorophores.

The most frequent description of wastewater fluorescence pertains to protein-like

fluorophores. For this study, two protein fluorescent centers have been identified and are

labeled ‘TR’ for tryptophan-like and ‘TY’for tyrosine-like. In most EEMs from this

44

study however, the two peaks are blended into one – and it is difficult to visually

differentiate between the two. In Figure 5.1 the two peaks are connected. The tyrosine-

like peak is centered at 275 nm EX/ 305 nm EM. This is nearly identical to the

fluorescent center of the dissolved free amino acid tyrosine (Stedmon and Markager

2005). The tryptophan-like peak has been well documented from several wastewater

sources such as WWTP influent (Arunachalam et al. 2005), WWTP effluent (Saadi et al.

2006) and river water impacted from WWTP discharge (Baker 2001). Each study reports

this fluorescent peak at 275-280nm EX/ 340-350nm EM. This tryptophan fluorophore

has also been attributed to surface marine estuary environments that support high

biological activity (Mopper and Schultz 1993).

The unmarked peak underneath TY and the unmarked peak underneath TR may

be derived from tyrosine and tryptophan, respectively based on marine studies by Mayer

et al. (1999). However, the peaks have not been well documented or discussed in

wastewater fluorescence research.

The humic acid-like peak is centered at 230-245nm EX/ 400-460nm EM. The

fulvic acid-like peak is centered around 305-325nm EX/ 410-430nm EM. These two

locations compare well with earlier studies of natural waters as outlined in work by Yan

et al. (2000). These two peaks are similar to humic acid-like and fulvic acid-like peaks

from river water and leachate samples collected for this study.

45

Figure 5.1 Wastewater EEM

5.2.2 Landfill Leachate Flurophore Identification

The fluorescent character of landfill leachate observed in this study was

comparable with the two Baker et al. studies which examined three contrasting landfills

and impacted waters. The Baker studies have suggested that all landfill leachates are

characterized by intense fluorescence intensity at 220-230nm EX/ 340-370nm EM. They

suggest that this peak is derived from fluorescent components of the xenobiotic organic

matter. A similar peak location for leachate samples was observed for this study at 220-

230nm EX/ 320-355nm EM. The peak is labeled ‘X’ in Figure 5.2 and consistently

dominated fluorescent composition from leachate samples taken.

Baker et al. also noticed a fulvic-like broad fluorescent peak in leachates at 320-

360nm EX/ 400-470nm EM with varying intensities among the landfills. Unfortunately,

the group was unable to link fulvic peak intensity with landfill age or contents. Fulvic-

like fluorescence was observed from Columbia Sanitary Landfill samples (labeled ‘F’ in

TR TY

F

H

BA

46

Figure 5.2) in this study at 305-325nm EX/ 410-430nm EM with far lower intensity

compared with the ‘X’ peak.

Baker et al. reported tryptophan and tyrosine peaks with high intensities. A

protein fluorophore is also present in leachates from this study. However, after large

dilutions, they are barely detectable by visual EEM inspection (labeled ‘P’ in Figure

5.2b).

A fourth leachate fluorophore is labeled ‘H’ in Figure 5.2a. It consistently

dominates EEM composition along with the xenobiotic peak. It is a broad fluorescent

peak centered at 240-255nm EX/ 440-470nm EM. A similar peak location was reported

by Baker et al. at 230-255nm EX/ 400-440nm EM. The group described it as a poorly

understood fluorescent center widely attributed to a component of the humic fraction. It

has indeed been identified in nearly all EEM studies of natural and wastewaters with only

general descriptions.

Figure 5.2 Landfill leachate EEM

X

F

H

P

A B

47

5.2.3 Missouri River Fluorophore Identification

The river water samples collected exhibited the lowest fluorescence as expected

and no dilution was necessary prior to analysis. Humic-like fluorophores were found at

230-250nm EX/ 415-470nm EM. Fulvic acid-like peaks were found at 300-315nm EX/

410-435nm EM. These locations were similar to fluorophore locations from the

wastewater and leachate samples.

Figure 5.3 Missouri River EEM

H

F

A B

48

5.2.4 Additional EEMs from Missouri Landfills and WWTPs

Wastewater and leachate samples were collected from additional sites around

Missouri and EEMs are displayed in Figures 5.4 and 5.5. Because of agreements with

facility operators, the specific sites will not be disclosed. Many fluorescent

characteristics of these sites are consistent with the five locations monitored for this

study. Strong xenobiotic fluorescence is found in three of the four landfills. However,

protein-like peaks are not as apparent in the WWTP EEMs.

49

Figure 5.4 EEMs created from four Missouri wastewater treatment plant effluents

50

Figure 5.5 EEMs created from four leachates collected from Missouri Landfills

51

5.3 PARAFAC Modeling Results

The primary objectives of this study are based on PARAFAC modeling of

fluorescence spectra. The modeling results allow for efficient monitoring of organic

content from the five study locations.

5.3.1 Number of PARAFAC Components

The number of components modeled by PARAFAC is a predetermined value

input by the user. Choosing the appropriate number of components has been a difficult

task for all PARAFAC modeling applications. Because of this, the core consistency

diagnostic (CORCONDIA) function was developed as an efficient tool for deciding the

appropriate number of components (Bro and Kiers 2003) and is further described in

sections 2.3 and 3.3. CORCONDIA results (Table 5.3) and split half analysis results

(Figures 5.6 – 5.9) are displayed below. The CORCONDIA score is always 100 for one

and two component models, then decreases monotonically with additional components,

then sharply drops once the maximal number of appropriate components is exceeded.

Bro et al. generalize that a CORCONDIA score close to 90% can be interpreted as ‘very

trilinear”, where as a score close to 50% would be ‘problematic’ because non-trilinear

variation was being displayed by the model. A CORCONDIA score near zero, or

negative, implies an invalid model.

52

Table 5.3 PARAFAC modeling results for 1-8 components

No. of Components Iterations Sum of Squares of Residuals Explained Variation Corcondia

1 66 1.68E+09 90.78% 100.02 538 7.86E+08 95.68% 99.53 854 5.86E+08 96.78% 87.84 934 3.76E+08 97.93% 62.45 710 2.83E+08 98.44% 7.96 986 2.30E+08 98.73% 14.67 1758 1.93E+08 98.94% 1.78 928 1.76E+08 99.03% 8.6

Figures 5.6 – 5.9 display results from split half analysis (Section 3.3) performed

for a three, four, five and six component model of the EEM data set. Excitation and

emission loadings for one half are displayed as solid blue lines. Excitation and emission

loadings for the second half are displayed as dashed green lines. The greatest overlap is

observed in the four component model. This validates the four component model.

Decent overlap is also observed for 5 and even six component models. However,

CORCONDIA scores indicate that these models are not stable and represent non-trilinear

variation.

53

300 400 5000

0.1

0.2

0.3

0.4

0.5

Component 1

EX

FL In

tens

ity

300 400 5000

0.1

0.2

0.3

0.4

0.5

Component 2

EX300 400 500

0

0.1

0.2

0.3

0.4

0.5

Component 3

EX

300 400 500 6000

0.05

0.1

0.15

0.2

0.25

0.3

Component 1

EM

FL In

tens

ity

300 400 500 6000

0.05

0.1

0.15

0.2

0.25

0.3

Component 2

EM300 400 500 600

0

0.05

0.1

0.15

0.2

0.25

0.3

Component 3

EM

Figure 5.6 Split half analysis results for three components

300 400 5000

0.1

0.2

0.3

0.4

0.5

Component 1

EX

FL In

tens

ity

300 400 5000

0.1

0.2

0.3

0.4

0.5

Component 2

EX300 400 500

0

0.1

0.2

0.3

0.4

0.5

Component 3

EX300 400 500

0

0.1

0.2

0.3

0.4

0.5

Component 4

EX

300 400 500 6000

0.05

0.1

0.15

0.2

0.25

Component 1

EM

FL In

tens

ity

300 400 500 6000

0.05

0.1

0.15

0.2

0.25

Component 2

EM300 400 500 600

0

0.05

0.1

0.15

0.2

0.25

Component 3

EM300 400 500 600

0

0.05

0.1

0.15

0.2

0.25

Component 4

EM

Figure 5.7 Split half analysis results for four components

54

300 400 5000

0.1

0.2

0.3

0.4

0.5

Component 1

EX

FL In

tens

ity

300 400 5000

0.1

0.2

0.3

0.4

0.5

Component 2

EX300 400 500

0

0.1

0.2

0.3

0.4

0.5

Component 3

EX300 400 500

0

0.1

0.2

0.3

0.4

0.5

Component 4

EX300 400 500

0

0.1

0.2

0.3

0.4

0.5

Component 5

EX

200 400 6000

0.05

0.1

0.15

0.2

0.25

Component 1

EM

FL In

tens

ity

200 400 6000

0.05

0.1

0.15

0.2

0.25

Component 2

EM200 400 6000

0.05

0.1

0.15

0.2

0.25

Component 3

EM200 400 6000

0.05

0.1

0.15

0.2

0.25

Component 4

EM200 400 6000

0.05

0.1

0.15

0.2

0.25

Component 5

EM

Figure 5.8 Split half analysis results for five components

300 400 5000

0.1

0.2

0.3

0.4

0.5

Component 1

EX

FL In

tens

ity

300 400 5000

0.1

0.2

0.3

0.4

0.5

Component 2

EX300 400 500

0

0.1

0.2

0.3

0.4

0.5

Component 3

EX300 400 500

0

0.1

0.2

0.3

0.4

0.5

Component 4

EX300 400 500

0

0.1

0.2

0.3

0.4

0.5

Component 5

EX300 400 500

0

0.1

0.2

0.3

0.4

0.5

EX

Component 6

3004005006000

0.05

0.1

0.15

0.2

0.25

0.3

Component 1

EM

FL In

tens

ity

3004005006000

0.05

0.1

0.15

0.2

0.25

0.3

Component 2

EM300400500 600

0

0.05

0.1

0.15

0.2

0.25

0.3

Component 3

EM300400500 600

0

0.05

0.1

0.15

0.2

0.25

0.3

Component 4

EM300400 500 600

0

0.05

0.1

0.15

0.2

0.25

0.3

Component 5

EM300400500 600

0

0.05

0.1

0.15

0.2

0.25

0.3

Component 6

EM

Figure 5.9 Split half analysis results for six components

55

5.3.2 PARAFAC Component Description

Figures 5.10 – 5.13 display the four components identified through PARAFAC

modeling. Each of the 117 EEMs used is composed of varying amounts of these four

components. The excitation and emission loading for each of these components is shown

on the left side of the figures. Solid lines correspond to excitation loading. Dashed lines

correspond to emission loading.

By multiplying these vectors, a visual representation of the component can be

obtained – as displayed on the right ride. However, multiplied values that fall outside of

the first and second order Raleigh scatter boundaries are removed. This can be seen in

Figure 5.11 – Component 2. The effect of the second pair or excitation peaks is removed

because the values fall above where the Raleigh scatter would be – and therefore do not

represent fluorescent organic matter from the samples.

Component 1

0

0.05

0.1

0.15

0.2

0.25

0.3

220 300 380 460 540

Wavelength (nm)

Load

ing

Figure 5.10 Excitation and emission loadings for Component 1

56

Component 2

0

0.05

0.1

0.15

0.2

0.25

0.3

220 300 380 460 540

Wavelength (nm)

Load

ing

Figure 5.11 Excitation and emission loadings for Component 2

Component 3

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

220 300 380 460 540

Wavelength (nm)

Load

ing

Figure 5.12 Excitation and emission loadings for Component 3

57

Component 4

0

0.1

0.2

0.3

0.4

0.5

0.6

220 300 380 460 540

Wavelength (nm)

Load

ing

Figure 5.13 Excitation and emission loadings for Component 4 Table 5.4 provides information regarding the four components. It is clear that the

components represent fluorophores from wastewater, leachate and river water samples

(Figures 5.1 – 5.3). The components do not represent specific individual fluorophores,

but rather groups of fluorophores with very similar characteristics and/or variability.

Component 1 represents a combination of humic acid-like and fulvic acid-like

fluorophores that are common to all sample sources. Component 2 indicates a protein-

like fluorophore – probably containing both tryptophan and tyrosine amino acids.

Component 3 is most-likely composed of the same protein substances that are more

inclined to fluoresce at lower excitation wavelengths. As mentioned earlier, fluorescent

peaks at this location is not well documented in the literature.

Component 4 represents a combination of humic acid-like fluorescence and a

xenobiotic fluorophore. It appears to be difficult to discern that xenobiotic fluorescent

center from Component 3. However, Component 4 is dominant from the landfill leachate

58

samples and nearly absent from the wastewater samples – indicating its representation of

a xenobiotic fluorophore typical in leachate samples. Further discussion of the

component composition of the sample sources is provided in the following section.

59

Figure 5.14 Components 1-4 modeled by PARAFAC

Table 5.4 Description of components derived from PARAFAC modeling

Comp. Description Suggested Composition Primary Distribution Primary Peak Locations (EX/EM)

1 Humic-Like humic acids and fulvic acidsAbundant at all sites, dominates river water organic composition

240/430 320/430

2 Protein-Liketryptophan and tyrosine amino

acids at higher excitation wavelengths

Wastewater sites 275/340 235/340

3 Protein-Liketryptophan and tyrosine amino

acids at lower excitation wavelengths

Wastewater sites 225/300 225/340

4 Combination Xenobiotic/ Humic

xenobiotics such as naphthalene and minor humic acid influence Landfill leachate 225/335

1

4 3

2

60

5.3.3 Component Composition of Sample Sources

An important aspect of this study was to differentiate between the sample sources

based on their organic composition. Figure 5.15 displays the average component scores

for the four modeled components over the one-year study period. Component scores are

normalized. The figure is useful for examining the relative make up of organic matter

within each site. However, component scores between different locations cannot be

evaluated based on this figure – because scores are normalized and dilution factors are

not taken into account. Figure 5.16 shows the actual component scores for each site.

Organic matter concentrations were very high if landfill leachate samples and had to be

diluted 60x prior to analysis. This information is less useful and will not be discussed

further.

From Figure 5.15, we can clearly differentiate between the leachate, wastewater

and river water studied. The landfill leachate is dominated by the xenobiotic-like

fluorophore and contains relatively lower protein-like substances. The next three sources

in Figure 5.15 have nearly identical compositions because each is derived from

wastewater. Protein-like fluorophores dominate their content. By visual inspection, it is

difficult to distinguish between Component 3 in wastewater samples with Component 4

in leachate samples. However, PARAFAC modeling has identified their differences and

shows how Component 4 (xenobiotic-like) is dominant in leachate samples, and nearly

absent in all others.

Samples drawn from the Missouri River are dominated by Component 1 (humic-

like). Organic content of natural waters are typically characterized by this type of

fluorophore. The Missouri River is characterized with high levels of suspended solids,

61

which can be seen from high TSS levels in Table 5.1 and Table 5.2. The majority of

organic matter dissolved in Missouri River water may be derived from these sediments

transferred into the river.

Another way to visualize the capability of PARAFAC to differentiate between

aquatic sources is seen in Figure 5.17. Hua et al. (2007) used this method to distinguish

between water sources based on PARAFAC component score. Notice that in each

subfigure, three groups (wastewater, leachate, river) can be clearly identified by

comparing their component scores.

Component Composition of Sites

0

0.1

0.2

0.3

0.4

0.5

0.6

Columbia Landfill WWTP Effluent Unit 4 Effluent Wetlands Effluent Missouri River

Nor

mal

ized

FL

Sco

re

Component 1

Component 2

Component 3

Component 4

Figure 5.15 PARAFAC component composition of sample locations based on

normalized fluorescence score

62

Component Composition of Sites

0

100

200

300

400

500

600

700

Columbia Landfill WWTP Effluent Unit 4 Effluent Wetlands Effluent Missouri River

Fluo

resc

ence

Sco

re x

10-

3

Component 1

Component 2

Component 3

Component 4

Table 5.16 PARAFAC component composition of sites with dilution factors accounted for and without normalization

63

Component 1 vs. Component 2

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Component 1

Com

pone

nt 2

Component 1 vs. Component 3

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Component 1

Com

pone

nt 3

Component 1 vs. Component 4

0

0.1

0.2

0.3

0.4

0.5

0.6

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Component 1

Com

pone

nt 4

Component 2 vs. Component 3

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0 0.1 0.2 0.3 0.4 0.5

Component 2

Com

pone

nt 3

Component 2 vs. Component 4

0

0.1

0.2

0.3

0.4

0.5

0.6

0 0.1 0.2 0.3 0.4 0.5

Component 2

Com

pone

nt 4

Component 3 vs. Component 4

0

0.1

0.2

0.3

0.4

0.5

0.6

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45

Component 3

Com

pone

nt 4

Figure 5.17 Site classification based on PARAFAC component scoring

64

5.3.4 Seasonal Variation of PARAFAC Component Scores

Bi-weekly samples were drawn from the five sample sources to examine seasonal

variation in organic content from the study locations. Figures 5.18 – 5.22 display

component scores, dissolved oxygen measurements and mean 5-day temperatures

(National Weather Service 2006-2007). Temperature and dissolved oxygen follow

expected trends. However, large variation was seen in dissolved oxygen concentrations

from wastewater samples. This may have been due to fluctuations in wastewater facility

operations such as aeration control.

Clear seasonal trends in organic composition are difficult to see in most

component score plots. However, some interesting features should be noted. The

Component 1 trend from landfill leachate sampling was an unexpected result. The

development of this humic-like component does not appear to follow a seasonal trend,

but displays a gradual increase throughout the one-year study period. This may be the

result of increased humification in the aging landfill.

Kang et al. (2002) reported similar results from a study of three different aged

landfills. Absorbance of ‘older’ landfills were higher than the leachate from a ‘younger’

landfill in the UV-visible range. The higher absorbance values may indicate advanced

humification since aromaticity and molecular weight are both increased as humification

procedes. The absorption coefficient at 280nm (used as an indicator for aromaticity in

the structure of a sample) showed an increasing trend with landfill age. Fluorescence

intensity increased with landfill age, but EEMs were not created in this study.

The age difference between landfills in the Kang et al. study was significant. The

young landfill was less than 5 years into operation and the oldest was over ten years old.

65

In this thesis, a considerable change in humic-like fluorescence was not expected over

just a one year time period. However, there was a 22% increase in Component 1 from

the landfill leachate when comparing the average scores from the first three sample dates

with the last three. Kang et al. (2002) noted that tracking humification in leachates can

optimize treatment processes with respect to landfill age and help understand humic acid

interaction in terrestrial environments.

From Figures 5.18 – 5.22, clear seasonal trends are not evident in the wastewater

sources. One exception is Component 1 (humic-like) from the constructed wetlands

effluent. Concentrations are clearly higher in summer months and lower in colder during

winter. The completely mixed activated sludge treatment process (used by CRWWTP) is

capable of producing stable effluent concentrations. However, wastewater effluent is still

dynamic in organic content. This was clear from water quality data obtained from

CRWWTP – where measured BOD levels fluctuated by over 200% within the same

week. For this reason, it is difficult to obtain an obvious seasonal trend in organic

content – especially from the WWTP effluent (site 2). The constructed wetlands can be

seen as somewhat of a stabilization period for the wastewater. This may be why a clear

seasonal trend is seen for Component 1 for the final constructed wetlands effluent, a

moderate trend is seen from unit 4 effluent, and no trend seen for the wwtp effluent.

An additional explanation for large changes in component scores in the

wastewater samples may come from wildlife. During the winter months, the wetlands

provide habitat for ducks. A particular favorite spot for them is near the WWTP sample

location – where the water is slightly warmer having just left the treatment reactors. This

may explain the considerable jump in protein-like components during the last several

66

sample dates taken from the WWTP effluent – and more moderate jumps in the stabilized

constructed wetlands effluent.

Site 1 – Columbia Landfill

Component 1

0

1

2

3

4

5

6

7

8

19-Mar-06 2-Jun-06 16-Aug-06 30-Oct-06 13-Jan-07

Com

p Sc

ore

x 10

-3

Component 2

0

0.5

1

1.5

2

2.5

3

3.5

4

19-Mar-06 2-Jun-06 16-Aug-06 30-Oct-06 13-Jan-07

Com

p Sc

ore

x 10

-3

Component 3

0

1

2

3

4

5

6

7

19-Mar-06 2-Jun-06 16-Aug-06 30-Oct-06 13-Jan-07

Com

p Sc

ore

x 10

-3

Component 4

0

2

4

6

8

10

12

14

19-Mar-06 2-Jun-06 16-Aug-06 30-Oct-06 13-Jan-07

Com

p Sc

ore

x 10

-3

Dissolved Oxygen

0.0

0.5

1.0

1.5

2.0

2.5

3.0

19-Mar-06 2-Jun-06 16-Aug-06 30-Oct-06 13-Jan-07

DO

(mg/

L)

Mean 5-day Temperature

-10

-5

0

5

10

15

20

25

30

35

19-Mar-06 2-Jun-06 16-Aug-06 30-Oct-06 13-Jan-07

Tem

p (o C

)

Figure 5.18 Seasonal variation of PARAFAC components from Site 1 - LL

67

Site 2 – Columbia Regional WWTP Effluent

Component 1

0

2

4

6

8

10

12

14

19-Mar-06 2-Jun-06 16-Aug-06 30-Oct-06 13-Jan-07

Com

p Sc

ore

x 10

-3

Component 2

0

2

46

8

10

1214

16

18

19-Mar-06 2-Jun-06 16-Aug-06 30-Oct-06 13-Jan-07

Com

p Sc

ore

x 10

-3

Component 3

0

2

4

6

8

10

12

14

19-Mar-06 2-Jun-06 16-Aug-06 30-Oct-06 13-Jan-07

Com

p Sc

ore

x 10

-3

Component 4

0

0.5

1

1.5

2

2.5

3

3.5

19-Mar-06 2-Jun-06 16-Aug-06 30-Oct-06 13-Jan-07

Com

p Sc

ore

x 10

-3

Dissolved Oxygen

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

19-Mar-06 2-Jun-06 16-Aug-06 30-Oct-06 13-Jan-07

DO

(mg/

L)

Mean 5-day Temperature

-10-505

10

1520253035

19-Mar-06 2-Jun-06 16-Aug-06 30-Oct-06 13-Jan-07

Figure 5.19 Seasonal variation of PARAFAC components from Site 2 - WW

68

Site 3 – Constructed Wetlands Unit 4 Effluent

Component 1

0

2

4

6

8

10

12

19-M ar-06 2-Jun-06 16-Aug-06 30-Oct-06 13-Jan-07

Component 2

0

2

4

6

8

10

12

14

16

19-Mar-06 2-Jun-06 16-Aug-06 30-Oct -06 13-Jan-07

-3

Component 3

0

2

4

6

8

10

12

14

19-Mar-06 2-Jun-06 16-Aug-06 30-Oct -06 13-Jan-07

-3

Component 4

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

19-Mar-06 2-Jun-06 16-Aug-06 30-Oct -06 13-Jan-07

-3

Dissolved Oxygen

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

19-Mar-06 2-Jun-06 16-Aug-06 30-Oct -06 13-Jan-07

Mean 5-day Temperature

-10

-5

0

5

10

15

20

25

30

35

19-Mar-06 2-Jun-06 16-Aug-06 30-Oct -06 13-Jan-07

Figure 5.20 Seasonal variation of PARAFAC components from Site 3 – U4

69

Site 4 – Constructed Wetlands Effluent

Component 1

0

2

4

6

8

10

12

19-Mar-06 2-Jun-06 16-Aug-06 30-Oct -06 13-Jan-07

Component 2

0

2

4

6

8

10

12

14

19-Mar-06 2-Jun-06 16-Aug-06 30-Oct -06 13-Jan-07

Component 3

0

2

4

6

8

10

12

14

16

19-Mar-06 2-Jun-06 16-Aug-06 30-Oct -06 13-Jan-07

Component 4

0

0.5

1

1.5

2

2.5

3

3.5

19-Mar-06 2-Jun-06 16-Aug-06 30-Oct -06 13-Jan-07

Dissolved Oxygen (mg/L)

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

19-Mar-06 2-Jun-06 16-Aug-06 30-Oct -06 13-Jan-07

M ean 5- day Temperat ure

-10

-5

0

5

10

15

20

25

30

35

19-Mar -06 2-Jun-06 16-Aug-06 30-Oct-06 13-Jan-07

Figure 5.21 Seasonal variation of PARAFAC components from Site 4 - WE

70

Site 5 – Missouri River

Component 1

0

2

4

6

8

10

12

14

16

19-Mar-06 2-Jun-06 16-Aug-06 30-Oct -06 13-Jan-07

Component 2

0

1

2

3

4

5

6

19-Mar-06 2-Jun-06 16-Aug-06 30-Oct -06 13-Jan-07

Component 3

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

19-Mar-06 2-Jun-06 16-Aug-06 30-Oct -06 13-Jan-07

Component 4

0

0.5

1

1.5

2

2.5

3

3.5

19-Mar-06 2-Jun-06 16-Aug-06 30-Oct -06 13-Jan-07

Dissolved Oxygen

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

19-Mar-06 2-Jun-06 16-Aug-06 30-Oct -06 13-Jan-07

Mean 5-day Temperature

-10

-5

0

5

10

15

20

25

30

35

19-Mar -06 2-Jun-06 16-Aug-06 30-Oct -06 13-Jan-07

Figure 5.22 Seasonal variation of PARAFAC components from Site 5 - MR

71

5.3.5 Constructed Wetlands Impact on Wastewater Component Score

The wastewater samples were consisted of three sources which represented

locations progressing along the constructed wetlands treatment system. Data provided

from personnel at the CRWWTP (Cuvelier 2007) indicated that the constructed wetlands

improves effluent water quality. Significant decreases in BOD and COD are shown in

Table 5.5 (TSS levels were also lowered, but are not shown). Ammonia levels are not

consistently reduced and sometimes accumulate along the wetlands treatment process.

Data in Table 5.5 is provided as three-day average concentrations (explained later) from

the WWTP effluent as well as the constructed wetlands effluent, coinciding with sites 2

and 4 in this study. Visual inspection of the EEMs as well as PARAFAC modeling

results provide further evidence of treatment through the constructed wetlands.

Table 5.5 Water quality parameter reduction through constructed wetlands

BOD Reduction COD Reduction NH3-N ReductionWW WE % Change WW WE % Change WW WE % Change

12-Jun-06 35.7 6.8 81.0% 62.7 40.7 35.1% 3.2 4.6 + 43.5%26-Jun-06 52.7 5.9 88.8% 62.0 44.3 28.5% 8.8 8.2 6.9%12-Jul-06 87.3 6.8 92.3% 53.5 48.3 9.7% 8.1 7.7 4.4%24-Jul-06 68.8 6.0 91.2% 79.3 45.3 42.9% 10.3 9.8 5.5%7-Aug-06 77.1 6.2 92.0% 98.3 43.3 55.9% 9.0 6.5 28.2%

21-Aug-06 55.9 6.8 87.9% 93.3 49.3 47.1% 14.0 14.1 + 0.3%5-Sep-06 50.3 5.4 89.3% 57.0 38.0 33.3% 12.5 12.6 + .6%

19-Sep-06 47.5 4.7 90.1% 61.7 39.0 36.8% 12.9 13.0 + .6%3-Oct-06 58.3 6.8 88.3% 70.3 46.0 34.6% 12.0 12.0 0.1%

17-Oct-06 54.3 5.1 90.6% 82.7 36.0 56.5% 14.5 14.5 + .4%31-Oct-06 50.3 4.4 91.3% 54.7 29.3 46.3% 11.0 13.5 + 23.6%14-Nov-06 51.0 4.2 91.7% 66.7 38.0 43.0% 14.2 15.9 +12.2%28-Nov-06 29.9 7.3 75.7% 63.7 36.0 43.5% 16.3 13.6 16.6%13-Dec-06 32.1 7.0 78.2% 63.3 38.3 39.5% 8.2 10.0 + 21.8%27-Dec-06 22.7 7.3 68.0% 49.3 37.3 24.3% 13.1 10.4 20.7%10-Jan-07 30.4 7.0 77.1% 63.0 42.5 32.5% 15.9 12.8 19.8%26-Jan-07 46.5 31.8 31.5% 92.0 60.0 34.8% 11.4 12.5 + 8.8%7-Feb-07 54.0 12.8 76.4% 108.7 51.5 52.6% 13.4 10.4 22.6%

19-Feb-07 81.0 15.6 80.8% 131.3 56.3 57.2% 11.2 12.8 14.1%Average 51.9 8.3 82.2% 74.4 43.1 39.7% 11.6 11.3 -0.1%

72

Table 5.6 displays the changes in PARAFAC component scores for sample dates

during the study. In general, Component 1 and Component 2 were reduced through the

constructed wetlands treatment. Component 3 was reduced in the majority of sample

dates, but to a lesser extent or even accumulated. Component 4 was not included because

it is largely attributed to the xenobiotic-like fluorophore and is nearly absent in most

wastewater samples.

Component 2 (protein-like) was reduced the greatest through wetlands treatment –

component scores decreased on each sample date and the total average reduction was

over 25%. Figure 5.23 shows the average Component 2 scores for each of the 5 sample

sites over the one-year study period. The typical set of wastewater EEMs from one

sample date show a clear protein-like peak for the WWTP sample – which becomes

nearly undetectable in the wetlands effluent EEM.

Two removal mechanisms for dissolved organic matter have been studied using

fluorescence spectroscopy – photochemical and microbial degradation. Because the

system is designed to support biotic activity, microbial degradation is assumed to play the

dominant role. Plant uptake and sorption onto soils may also contribute to fluorophore

reduction. Table 5.7 displays sky cover information obtained from the National Weather

Service (2006-2007). The sample days that were characterized by overcast are presented

(as well as clear skies) based on the cloud cover that day and the previous two days.

Average Component 2 reduction was greater for the four clear sky periods. However, on

one of the cloudiest periods (May 15th), a very high Component 2 reduction was observed

– supporting microbial degradation as the primary removal source. Stedmon et al. (2005)

reported that the primary sinks for a PARAFAC component representing a tryptophan

73

protein-like fluorophore were photodegradation by UV light (not visible) and microbial

degradation.

Component 1 reduction was greater during the overcast periods than for clear

skies – indicating the humic-like fluorophores are less susceptible to photodegradation.

The Stedmon et al. study suggested visible and UV light as a primary sink for humic-like

fluorescence. However, their study examined DOM from marine sources where

microbial degradation was probably not as prevalent.

Table 5.6 PARAFAC component reduction through constructed wetlands

Component 1 Reduction Component 2 Reduction Component 3 ReductionWW WE % Change WW WE % Change WW WE % Change

20-Mar-06 11.5 8.7 24.2% 11.2 10.0 11.1% 8.1 7.2 10.6%3-Apr-06 10.7 9.6 10.4% 9.5 8.5 10.4% 11.2 13.5 + 21.1%

17-Apr-06 8.8 9.0 + 2.8% 7.8 7.6 2.2% 10.9 11.1 + 2.2%1-May-06 10.5 9.2 12.1% 9.7 7.3 24.5% 8.6 11.0 + 27.7%

15-May-06 11.3 8.2 27.0% 11.7 7.6 34.8% 8.6 9.7 + 12.0%30-May-06 11.3 10.3 8.8% 13.3 13.0 2.0% 4.5 8.3 + 82.7%12-Jun-06 9.4 9.6 + 2.5% 11.5 6.3 45.6% 4.8 4.0 16.2%26-Jun-06 10.8 8.3 22.9% 12.3 8.0 35.2% 6.3 6.2 1.8%12-Jul-06 11.1 9.8 12.2% 14.9 10.1 32.5% 7.8 6.6 15.6%24-Jul-06 10.5 8.4 19.5% 11.7 7.7 34.3% 12.3 10.5 15.0%7-Aug-06 11.4 8.9 21.8% 16.1 11.0 31.7% 7.5 6.7 10.8%

21-Aug-06 12.3 10.0 18.8% 13.4 11.4 14.7% 8.5 7.7 9.7%5-Sep-06 9.8 9.3 5.7% 11.0 9.6 12.2% 6.9 6.0 13.4%

19-Sep-06 12.6 9.8 22.2% 14.6 11.0 24.7% 9.1 7.5 16.9%3-Oct-06 12.4 9.6 22.5% 12.3 7.8 36.9% 12.3 9.6 21.6%

17-Oct-06 11.7 10.0 14.5% 12.1 8.8 27.6% 9.5 7.8 17.9%31-Oct-06 10.9 8.8 19.1% 11.3 9.3 17.8% 6.6 5.7 13.5%14-Nov-06 12.0 7.9 34.2% 11.3 10.6 6.2% 6.9 6.9 0.2%28-Nov-06 11.9 8.5 28.5% 13.0 8.1 37.9% 10.5 7.5 29.3%13-Dec-06 8.0 7.1 11.3% 8.3 6.9 16.9% 3.6 4.3 + 20.2%27-Dec-06 8.7 6.7 23.4% 10.0 7.0 29.7% 5.9 5.9 + .4%10-Jan-07 11.3 7.3 35.1% 13.6 9.3 31.6% 6.6 5.7 14.6%26-Jan-07 9.1 7.2 20.3% 10.7 7.3 31.9% 8.1 8.9 + 9.8%7-Feb-07 11.1 7.5 32.8% 16.5 9.5 42.4% 8.0 6.2 22.0%

19-Feb-07 10.5 7.2 31.2% 13.8 8.8 36.0% 8.4 8.6 + 2.7%Average 10.8 8.7 18.9% 12.1 8.9 25.2% 8.1 7.7 2.0%

74

Table 5.7 Component reduction and sky cover information for sampling dates

Overcast Skies Removal % Clear Skies Removal %

Component 1 20-Mar-06 24.2% Component 1 24-Jul-06 19.5%15-May-06 27.0% 07-Aug-06 21.8%17-Oct-06 14.5% 03-Oct-06 22.5%14-Nov-06 34.2% 31-Oct-06 19.1%

25.0% 20.7%

Component 2 20-Mar-06 11.1% Component 2 24-Jul-06 34.3%15-May-06 34.8% 07-Aug-06 31.7%17-Oct-06 27.6% 03-Oct-06 36.9%14-Nov-06 6.2% 31-Oct-06 17.8%

19.9% 30.2%

Component 3 20-Mar-06 10.6% Component 3 24-Jul-06 15.0%15-May-06 + 12.0% 07-Aug-06 10.8%17-Oct-06 17.9% 03-Oct-06 21.6%14-Nov-06 0.2% 31-Oct-06 13.5%

4.2% 15.2%

0

2

4

6

8

10

12

14

1Component 2 " Protein-Like"

Com

pone

nt S

core

x 1

0-3

Landfill LeachateWastewater EffluentUnit 4 EffluentWetlands EffluentMissouri River

Figure 5.23 Component 2 and protein peak reduction through constructed wetlands

75

5.3.6 PARAFAC Modeling Validation

The previous sections have used component scores to track changes in organic

composition. Visual examination of the component spectra (Figure 5.14) as well as

component composition (Figure 5.15) and changes in composition through treatment

(Figure 5.23) all support the concept of PARAFAC/EEM successfully modeling

fluorescent organic matter from the studied sites. Another validation tool is now

presented by comparing PARAFAC modeling with another EEM interpretation

technique.

The ‘peak-picking’ method was mentioned in section 2.3 and describes a very

basic means for interpreting EEM data. Individual peak intensities are found by scrolling

to assumed peak locations and recording the intensity values. This is an inefficient an

unreliable methods – particularly when using large data sets. However, this method was

used to measure protein-like fluorophores and compare results with PARAFAC

Component 2 (protein-like) scores from wastewater samples. The correlation results for

PARAFAC Component 2 score with ‘picked’ protein-like peak intensities are displayed

in Figure 5.24. The peaks were found be obtaining the highest fluorescence intensities

nearly surrounding the 275nm EX/ 340nm EM tryptophan peak location on all

wastewater EEMs. The results further support the ability of PARAFAC to effectively

model dissolved protein substances.

76

WWTP Effluent

0

2

4

6

8

10

12

14

16

18

3/20/06 6/20/06 9/20/06 12/20/06Date

Com

pone

nt S

core

* 10

-3

0

1

2

3

4

5

6

FL In

tens

ity *

10-2

R2 = 0.8807

0

1

2

3

4

5

6

0 5 10 15 20

Comp 2 Score x 10-3

Prot

ein

Peak

Int.

x 10

-2

Unit 4 Effluent

0

2

4

6

8

10

12

14

16

3/20/06 6/20/06 9/20/06 12/20/06Date

Com

pone

nt S

core

* 10

-3

0

1

2

3

4

5

6

FL In

tens

ity *

10-2

R2 = 0.8137

0

1

2

3

4

5

6

0 5 10 15

Comp 2 Score x 10-3

Prot

ein

Peak

Int.

x 10

-2

Wetlands Effluent

0

2

4

6

8

10

12

14

3/20/06 6/20/06 9/20/06 12/20/06Date

Com

pone

nt S

core

* 10

-3

0

1

2

3

4

5

FL In

tens

ity *

10-2

R2 = 0.83

00.5

11.5

22.5

33.5

44.5

5

0 2 4 6 8 10 12 14

Comp 2 Score x 10-3

Prot

ein

Peak

Int.

x 10

-2

Figure 5.24 PARAFAC modeling validation by comparison with ‘peak picking’ method

for wastewater samples

77

5.3.7 Correlation with Water Quality Parameters

As discussed earlier, water quality data from wastewater samples was provided by

the Columbia Regional Wastewater Treatment Plant. The plant measures data from three

locations – influent into the plant, effluent from the plant and effluent from the

constructed wetlands. The latter two sites coincide with sample sources 2 (WW) and 4

(WE) from this study. PARAFAC scores for components 1, 2 and 3 were correlated with

water quality data.

Three-day average values coinciding with sample collection dates were used to

account for large daily fluctuations in analyte concentrations. It is unknown what time of

day the CRWWTP staff collected their samples and collection times for this study

changed from date to date. For this reason it is unclear if samples for this study were

more representative CRWWTP measurements taken the previous day, same day or

following day. Since there were often times significant fluctuations between CRWWTP

readings from day to day, a three-day average was used in correlation efforts.

In general, correlations between component scores and water quality

parameters were weak. Based on previous studies, strong correlations were not expected.

Table 2.1 shows R2 values for component scores correlated with PARAFAC components

from the Holbrook et al. (2006) study. Their study indicated better correlation of

PARAFAC components with DOC compared with COD. The humic-like component

correlated well with absorbance. Soluble Kjeldahl Nitrogen (SKN) was moderately

linked to the protein-like component (R2 = .69). Non-significant correlation was found

between the protein-like component and ammonia (R2 = .3). Ammonia (NH-3)

correlations with protein-fluorophores has been attempted in other studies (Baker 2002;

78

Baker et al. 2003) without success. Holbrook et al. report stronger correlation between

protein fluorescence and SKN compared with NH-3, which is consistent with previous

research.

For this study, the strongest correlations between PARAFAC scores and water

quality were observed for the protein-like Component 2. This was expected since this

fluorophore is frequently linked to sewage impacted waters and WWTP effluent. Based

on the Holbrook et al. study, stronger correlation may have been observed with DOC

concentrations, but this parameter was not measured. Ammonia correlation agreed with

recent studies producing as no correlation was found.

Table 5.8 Correlation of wastewater PARAFAC component scores with select water quality parameters

PARAFAC Component Water Quality Parameter R2

1 BOD (mg/L) 0.42COD (mg/L) 0.26NH-3 (mg/L) 0.04Temperature (oC) 0.37

2 BOD (mg/L) 0.55COD (mg/L) 0.46NH-3 (mg/L) 0.04Temperature (oC) 0.11

3 BOD (mg/L) 0.14COD (mg/L) 0.13NH-3 (mg/L) 0.15Temperature (oC) 0.07

79

R2 = 0.5535

0102030405060708090

100

0 5 10 15 20

Component 2 Score x 10-3

BO

D (m

g/L)

R2 = 0.4559

0

20

40

60

80

100

120

140

0 5 10 15 20

Component 2 Score x 10-3

CO

D (m

g/L)

Figure 5.25 Correlation of protein-like Component 2 with BOD and COD

80

CHAPTER 6 – SUMMARY AND CONCLUSIONS

This thesis continued the emerging research of excitation-emission fluorescence

spectroscopy coupled with parallel factor analysis. Results support the use of

EEM/PARAFAC to effectively model organic content of aquatic sources and for

monitoring changes in composition. More specifically, this work traced seasonal

variation of five sources and development of wastewater organic dynamics throughout a

constructed wetlands treatment process. Information gathered from water quality

parameter correlation provides additional understanding for future applications.

PARAFAC components derived from modeling resembled humic-like, protein-

like and xenobiotic-like fluorophores identified in this study and from many other

research groups. By comparing component scores with fluorescence intensities obtained

from peak-picking, it is clear that the EEM/PARAFAC method effectively models

changes in fluorophore concentration.

The three sample sources could be separately characterized based on their

PARAFAC component score – representing organic composition. Leachate samples

were dominated by a xenobiotic-like fluorescence, wastewater samples were dominated

by protein-like fluorescence, and river water was dominated by humic-like fluorescence.

The one-year study period allowed observation of seasonal variation from the

sample locations. Clear patterns for most PARAFAC component scores were not

noticed. However, humic-like fluorescence from the wetlands effluent sample location

was clearly higher in summer months. An increase in humification within the landfill

81

was also assumed from a gradual rise in humic-like fluorescence intensity (22%) during

the study period.

Changes in organic composition were tracked as wastewater traveled through the

constructed wetlands treatment process. The greatest and most consistent reduction

(25.2% average) was the protein-like Component 2. This fluorophore has frequently

been reported to dominate wastewater, so its reduction supports the use of constructed

wetlands as a legitimate treatment process.

In general, strong correlation of PARAFAC components with water quality

parameters was not found. However, results resembled those of previous studies.

Strongest correlation with wastewater analytes were observed with protein-like

Component 2, R2 = 0.55 for BOD correlation.

Fluorescence spectroscopy has proven to be a useful fool for studying organic

matter. Coupling the analytical technique with parallel factor analysis strengthens its

potential for use in various applications. This thesis has continued the investigation of

EEM/PARAFAC by applying the method to additional environmental settings.

Eventually similar methods may be used in practical investigations to monitor

organic content of water bodies. This is important as more land continues to be subjected

to development and altered for agriculture. Runoff from these altered sources and

additional inputs from municipal and industrial discharges will influence the organic

composition of impacted waters. Complex organic molecules are difficult to analyze and

tracking their changes are difficult. EEM/PARAFAC could provide watershed managers

with a useful tool to efficiently monitor these changes in organic content. This ability

may help to keep areas safe for water supply, recreation and protecting ecosystems.

82

Online monitoring of water quality using EEM fluorescence spectroscopy could

be a valuable tool for treatment plant operators in the future. Fluorometers could be

installed at several locations along a water treatment system – each reporting EEM data

to a central control unit. With proper calibration, the data could help operators monitor

substrate degradation and notice reasons for concern. Less laboratory analysis for

monitoring would be necessary – leading to lower operational and personnel costs.

Perhaps less reporting to regulatory agencies would be required for plants employing

fluorescence monitoring systems.

Online fluorescence monitoring was studied by Arunachalam et al. (2005) in efforts

to optimize aeration controls in an aerobic sludge digestion process. Future work should be

continued in water treatment process monitoring. Combining online fluorescence monitoring

with PARAFAC analysis could lead to a rapid and straightforward means for assessing water

quality.

83

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