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wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 6 7 9e1 6 9 0
Available online at w
journal homepage: www.elsevier .com/locate/watres
Assessment of dissolved organic matterfluorescence PARAFAC components before and aftercoagulationefiltration in a full scale water treatmentplant
Nancy P. Sanchez*, Andrew T. Skeriotis, Christopher M. Miller
Department of Civil Engineering, Auburn Science and Engineering Center (ASEC), 210, The University of Akron, Akron, OH 44325, USA
a r t i c l e i n f o
Article history:
Received 6 September 2012
Received in revised form
19 December 2012
Accepted 20 December 2012
Available online 3 January 2013
Keywords:
Water treatment plants
Coagulation
Fluorescence spectroscopy
Parallel factor (PARAFAC) analysis
Uncorrected Matrix Correlation
(UMC)
Abbreviations: DOM, dissolved organic mUMC, Uncorrected Matrix Correlation; NOMfluorescence regional integration; EEM, exccarbon; UV254, ultraviolet absorbance at 254 nIR, individual raw water model; IT, individuafluorescence model; SVD, singular value deunits; PS, PARAFAC sample loadings summa* Corresponding author. Tel.: þ1 330 972 244E-mail address: [email protected] (N.P. S
0043-1354/$ e see front matter ª 2012 Elsevhttp://dx.doi.org/10.1016/j.watres.2012.12.032
a b s t r a c t
Fluorescence monitoring of the raw and treated water after coagulationefiltration in
a drinking water treatment plant in Northeast Ohio was conducted during a period of 32
months. Principal fluorophore groups present in the dissolved organic matter (DOM) of the
raw, treated, raw-treated combined water and differential fluorescence data sets com-
prising over 680 samples were determined through Parallel Factor (PARAFAC) analysis.
Four components (two humic-like and two with protein nature) were identified in each
model and their degree of similarity was evaluated using the Uncorrected Matrix Corre-
lation (UMC), a measure of spectral overlapping. Results show that spectral characteristics
of the components in the independent models are comparable (average UMC > 0.98),
indicating that from a PARAFAC perspective, components in the raw water are not expe-
riencing major transformations beyond removal through the treatment process and new
fluorescent components are not being formed. Coagulation assessment based on PARAFAC
application to the differential excitation-emission matrices (DEEM), representing the por-
tion of fluorescence removed after treatment, is introduced in this paper along with the
volumetric evaluation of the components present in a sample as an alternative approach to
determine their relative contribution. Volumetric analysis revealed a predominance of
humic components, constituting about 80% in the raw and treated water. Results of the
DEEM model indicated that the most amenable component to be removed by coagulation
(removal w50%) at full scale operation is a humic-like fluorophore with predominance in
the raw water, while removal of the protein-like components was about 30%. Results also
show that the PARAFAC sample loadings exhibit a higher association with the total EEM
signal in the raw and treated water samples when compared with alternative analysis
atter; DEEM, differential excitationeemission matrix; PARAFAC, parallel factor analysis;, natural organic matter; DBP, disinfection by-products; TOC, total organic carbon; FRI,itationeemission matrix; DWTP, drinking water treatment plant; DOC, dissolved organicm; DI, deionized; IFE, inner filter effect; RSD, relative standard deviation; R.U, Raman units;l treated water model; CM, raw-treated combined water model; IDF, individual differentialcomposition; SSE, sum of squared error; OFI, overall fluorescence intensity; AU, Arbitrarytion; DEEMs, differential excitationeemission matrices (EEMraweEEMtreated).4; fax: þ1 330 972 6020.anchez).ier Ltd. All rights reserved.
wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 6 7 9e1 6 9 01680
techniques. These results support the analysis of the PARAFAC components present in the
raw and treated samples as a viable measure for assessment of the coagulation process in
a drinking water treatment plant.
ª 2012 Elsevier Ltd. All rights reserved.
1. Introduction present in the sample (Bro, 1997). The basic formulation of
Natural organicmatter (NOM) present in freshwaters has been
defined as a complex and not fully characterized mixture of
a variety of aliphatic and aromatic compounds with a broad
range of molecular weights (�Swietlik and Sikorska, 2005).
Composition and characteristics of the aquatic NOM are
highly location-dependent and are determined by the source
of the organicmatter, the water chemistry and environmental
conditions (e.g., temperature and pH) and the biological pro-
cesses occurring in the water source (Leenheer and Croue,
2003). NOM plays a major role in any drinking water treat-
ment facility because of its influence on the performance of
treatment stages such as the coagulation-flocculation process
(Edzwald, 1993; Owen et al., 1995; Rebhun and Lurie, 1993) and
the formation of disinfection by-products (DBP) derived from
the NOM-chlorine reaction in the disinfection step (Rook,
1974). NOM removal optimization in a drinking water treat-
ment plant requires an understanding of its specific charac-
teristics and seasonal variability in the water source, which
will likely determine its treatability (Parsons et al., 2004).
Fluorescence spectroscopy analysis, based on the presence
of fluorophores associated with humic-fulvic and protein like
compounds, has emerged among other techniques as a rapid
characterization tool with high sensitivity towards NOM,
requiring minimal sample preparation and with potential for
on-line implementation (Ahmad and Reynolds, 1999; Belzile
et al., 2006; Carstea et al., 2010; Hudson et al., 2007). Fluo-
rescence spectroscopy offers insight on the characteristics
and composition of the NOM unlike aggregate parameters
typically used in drinking water treatment plants (e.g., total
organic carbon (TOC) and ultraviolet absorbance) (Bridgeman
et al., 2011; Leenheer and Croue, 2003).
Excitationeemission pairs (peak picking) (Coble, 1996),
fluorescence indexes (Hood et al., 2003; Huguet et al., 2009;
McKnight et al., 2001), fluorescence regional integration (FRI)
(Chen et al., 2003) and Parallel Factor (PARAFAC) analysis
(Stedmon and Markager, 2005; Stedmon et al., 2003) have been
used as the main methods to analyze the fluorescent signal
emitted by the fluorophoresmoieties present in theNOM. Peak
location tracking constitutes a useful approachwhen a limited
number of excitationeemission pairs are being monitored;
however, as excitationeemissionmatrices (EEM)have emerged
as a rapid and standard fluorescence procedure (Coble, 1996),
FRI and PARAFAC have become more common techniques for
the analysis of the specific fluorescence information.
PARAFAC offers a higher degree of information about the
specific components in the EEM (Baghoth et al., 2011) and
approaches to the modeling of the excitationeemission ma-
trix by considering this as the result of the contribution of the
fluorescence of a specific number of different fluorophore
groups (components) with common spectral characteristics
PARAFAC has been well-documented in previous studies
(Baghoth et al., 2011; Stedmon et al., 2003). Each PARAFAC
component is the product of three factors: a sample, an
excitation and an emission loading. The contribution of the
different components present in a sample explains the fluo-
rescence intensity at any excitationeemission pair in the EEM.
According to this, the number of components that contribute
to the fluorescence in the sample; an estimate of the excita-
tion/emission spectra (excitation and emission loadings) of
each component and a respective estimate of the concentra-
tion of each fluorophore group in the form of the samplemode
loading (sample scores) can be obtained by PARAFAC.
A limited number of studies have reported the use of flu-
orescence analysis for the assessment of engineered systems
including drinkingwater treatment processes (Ishii and Boyer,
2012). Excitationeemission pairs (Cheng et al., 2004) and
location and intensity change of the peaks in the EEM before
and after coagulation have been used to evaluate the perfor-
mance of this process at different pH levels in a full scale plant
(Bieroza et al., 2011a; Bieroza et al., 2010) and at laboratory
scale (Gone et al., 2009). Analyses of the EEM at different stages
in full scale water treatment plants located in UK using sur-
face waters were conducted based on the variation of the in-
tensity of the peaks and shifts in the location of their
excitation-emission maxima (Bieroza et al., 2009b, 2011a).
Self-organizing maps and comparison of different analysis
methods applied to fluorescence data collected at full-scale
operation in UK have also been reported for the study of the
performance of the drinking water treatment processes
(Bieroza et al., 2009a, 2011b; Bieroza et al., 2012). The use of
PARAFAC for the study of the fate of the NOM in drinking
water treatment plants was also reported for a combined set
of samples collected at two treatment facilities in the Neth-
erlands (Baghoth et al., 2011). PARAFAC components in sets
and subsets of water and leachate samples have been repor-
ted previously suggesting that the degree of similarity in the
retained components can differ for different data sets and
according to the number of fluorophore groups included in the
model (Beggs, 2010; Bieroza et al., 2011b; Bieroza et al., 2012).
To the best of our knowledge and with the exception of some
short-term monitoring studies conducted exclusively during
summer periods (Johnstone et al., 2009; Pifer and Fairey, 2012),
PARAFAC has not been commonly reported as an alternative
for the process monitoring of drinking water treatment plants
(DWTPs) in the United States and no long-term fluorescence
studies accounting for the seasonal variability inherent to the
NOM character have been reported.
The purpose of this study was to conduct a comprehensive
assessment of the components resulting from PARAFAC
analysis of a large set of samples collected before and after
coagulation-filtration in a full scale drinking water treatment
wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 6 7 9e1 6 9 0 1681
plant. The study explored the generation and validation of
independent PARAFACmodels on raw and treated water data
sets and the comparison of the resulting components based
on a UMC analysis in order to establish if from a PARAFAC
perspective, notable changes could be observed in the NOM
character after treatment or if new fluorescence components
could be detected. Significant changes in the NOM structure
after treatment would imply that the use of PARAFAC models
based on combined data sets (e.g., raw and treated water
samples) to track the NOM fate in a drinking water treatment
train should be evaluated. To address this point, components
from a composite PARAFAC model fit on the combined raw
and treated water data set were also analyzed on a UMC basis.
A novel approach based on PARAFAC application to the dif-
ferential fluorescence matrices and the examination of the
volume based distribution of the components in the raw and
treated water was used to determine the coagulation effect on
the components identified in the water before treatment and
to establish the changes in the distribution of the fluorophores
in the DOM pool. A further objective was to study the associ-
ation between the total fluorescence signal in the EEM and the
PARAFAC sample loadings when compared with the normal-
ized volumes obtained through FRI application. Specific
capability of both approaches to describe the EEM signal for
a large set of raw and treated water samples was examined.
2. Materials and methods
2.1. Sample collection
Sampling was conducted during 32 months comprised be-
tween October 2009 and July 2012. Raw and filter effluent
water samples were obtained one to three times a week at the
Ravenna DWTP located in Ravenna (Ohio). The treatment
process consists of a typical train comprising pre-oxidation
(e.g., potassium permanganate and chlorine dioxide), rapid
mix (e.g., coagulant addition), clarification, filtration and
chlorination. Ferric chloride was used as coagulant for an
average treated water flow around 7.6 ML/d.
Raw water samples were obtained from Lake Hodgson
(Ravenna, OH), which serves as the water source for the water
treatment facility. No previous treatment was applied to the
raw water samples. Treated water samples were collected
after filtration before any final chlorine application. Samples
were collected in Teflon-lined caps amber bottles and stored
at 4 �C for a maximum of 3 days until analysis. Samples were
filtered through 0.45 mm Whatman nylon membrane filters
and analyzed for dissolved organic carbon (DOC), pH, ultra-
violet absorbance at 254 nm (UV254) and fluorescence excita-
tioneemission matrices.
2.2. Raw and treated water characterization
Non-purgeable dissolved organic carbon was measured using
a TOC-5000A Total carbon analyzer (Shimadzu, Japan). Sam-
ples were acidified to pH 2with 1 MHCl and spargedwith pure
air for removal of the purgeable fraction of the inorganic
carbon. DOC levels varied from 4.5 to 7.8 mg/L (mean:
5.64 � 0.57) and 2.9e6.7 mg/L (mean: 4.10 � 0.56) for the raw
and treated water respectively during the sampling period.
Ultraviolet absorbance at 254 nmwas determined using a 1 cm
quartz cell in a 1601 UVevisible spectrophotometer (Shi-
madzu, Japan) and ranged from 0.10 to 0.14 cm�1 (mean:
0.12 � 0.009) and 0.05e0.1 cm�1 (mean: 0.073 � 0.01) for the
raw and treated water respectively. Accumet Basic AB15 pH
meter (Fisher Scientific, USA) was used for the pH measure-
ments. Raw and treatedwater pH varied from 6.9 to 8.3 (mean:
7.6 � 0.3) and 7.6 to 8.6 (mean: 8.4 � 1.7) respectively.
2.3. Fluorescence analysis and data processing
Filtered raw and treated water samples were diluted (1:2 ratio)
using deionized (DI) water in order to prevent any inner
filter effect (IFE). DI water was obtained using a Barnstead
ROpureLP system (Barnstead/Thermolyne, USA). The ultravi-
olet absorbance spectrum between 220 and 400 nm was
recorded for the diluted raw and treated water samples. The
spectra showed levels of absorption below 0.1 cm�1 in the
specific wavelength range and therefore no corrections for IFE
were applied (Lakowicz, 2006; Larsson et al., 2007). Ionic
strength and pH of the filtered samples were adjusted ac-
cording to procedures presented previously (Baghoth et al.,
2011; Chen et al., 2003; Westerhoff et al., 2001). Ionic strength
was adjusted by addition of a 1 M KCl solution resulting in
0.01 M KCl. Sample pH was adjusted to 3.0 � 0.15 using 0.01 M
H2SO4.
Fluorescence excitation-emission matrices were recorded
using an F-7000 fluorescence spectrophotometer (Hitachi,
Japan). Excitation and emission wavelength ranges were set
from 204 to 404 nm and 290e550 nm respectively. Excitation
and emission scanning intervals were set at 5 nm and 2 nm
respectively. Scan speed was set at 60,000 nm/min (Table S1),
and excitation and emission slit widths were fixed at 10 nm.
Photomultiplier detector voltage was fixed at 400 V. Instru-
ment spectral corrections were applied according to the
manufacturer instructions (e.g., Rhodamine B as the quantum
counter for excitation spectrum and quartz diffuser for emis-
sion spectra) in order to have corrected excitation and emis-
sion spectra accounting for specific instrumental response. A
blank solution consisting of DI water with adjusted pH and
ionic strength according to the sample preparation protocol
was examinedwith each set of samples for analysis. The blank
was subtracted fromthe sample 3Dscan in order to account for
the effects of the KCl and acid added to the sample and to
remove Raman and Rayleigh scatter. Variation in the lamp
light intensity was measured by recording the Raman peak
area for DI water at an excitation wavelength of 349 nm
(emission wavelength range: 376e420 nm) each day the in-
strument was used. Relative standard deviation (RSD) for this
parameter was below 5% for the period of analysis. As an
additional quality control for the spectrophotometer oper-
ation, a quinine sulfate solution (7 mg/L in 0.1 M H2SO4) was
excited at 310 nm and fluorescence intensity was recorded at
450 nm each day prior to the analysis of the corresponding set
of samples. RSD for the intensity of this solutionwas below 5%
during the period of analysis. Regions of elastic and inelastic
scatter (first and second order Rayleigh and Raman scatter) in
the EEMs were replaced with missing values covering 20 nm
beyond the limit of the scatter area. Results of the Raman peak
wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 6 7 9e1 6 9 01682
area for the day of analysis of the sample were used to nor-
malize the results of the fluorescence intensity and therefore
all fluorescence results in this paper are reported as Raman
Units (R.U).
2.4. PARAFAC application
N-way v.3.00 Toolbox (Andersson and Bro, 2000) and DOM-
Fluor v.1.7 Toolbox (Stedmon and Bro, 2008) for MATLAB were
used to fit four different and independent PARAFAC models
over a data set comprising 688 water samples. MATLAB
R2009a (Mathworks, USA) was used to perform the modeling
task.
The first and second models were fit exclusively over the
raw (individual-raw model-IR) and treated water (individual-
treated model-IT ) samples respectively. A third model was
generated from the composite data set comprising the raw
and treated water samples (composite model-CM ). A fourth
model was fit on the set of differential fluorescence matrices,
representing the portion of the fluorescence signal being
removed by the coagulation-filtration process (individual-
differential fluorescence model-IDF ). Excitation wavelengths
below 224 nmwere removed from themodel, considering that
wavelengths lower than 220 nm are usually associated with
high levels of noise and do not contribute relevant fluo-
rescence information (Stedmon et al., 2003; Yamashita and
Tanoue, 2003). A triangle of zeros was included in the region
of higher excitation and lower emission to increase the speed
of the calculation (Stedmon et al., 2003; Thygesen et al., 2004).
The number of samples in the initial data sets, the outliers
and the final number of samples used in each model after
outliers were removed are presented in Table 1. Samples with
unusual fluorescence signal (e.g., extremely high or low in-
tensity and atypical contour plots when compared with his-
torical data) were classified as outliers and removed from the
data set. Further examination of the residual variance and
leverage values after preliminary PARAFAC application was
also conducted for determining potential outliers.
One to six components were retained for each PARAFAC
model. Non-negativity constraints to the excitation, emission
and samples modes were applied. Random values and sin-
gular value decomposition (SVD) were used for initialization
of each model. The convergence to a unique solution for
multiple runs was examined in each case and used as
Table 1 eWater samples included in each data set for the fittinOctober 2009 to July 2012.
Model Number of samples-initi
Individual-raw (IR) 344
Individual-treated (IT) 344
Composite (CM) 688
Individual-differential fluorescence (IDF) 344
a Only 29 samples (8 raw and 21 treated) were identified as outliers in the e
these outliers were also removed from the data set before fitting the CM
fluorescence removal. As samples for two dates were common outliers i
excluded.
b Outliers removed from the IDF model corresponded to 27 pairs of raw
Total number of samples excluded was 54.
a criterion showing the adequacy of the model and its con-
vergence to a global minimum. Model validation based on
split half analysis (Harshman and DeSarbo, 1984) was con-
ducted. Each data set was divided in two sub-datasets (first
and second randomhalves) and an independentmodel was fit
over each new group of samples. Concordance of the excita-
tion and emission loadings for the sub-datasets indicates that
true components pertaining to the entire group of samples are
being retained and that they are not a product of noise but
systematic variation in the data set (Andersen and Bro, 2003;
Harshman and DeSarbo, 1984; Stedmon et al., 2003).
Core consistency diagnostic (CORCONDIA) (Bro, 1998) was
used as an additional criterion to select the appropriate
number of components to be included in the differentmodels.
CORCONDIA evaluates the degree of trilinearity of the PAR-
AFAC loadings by comparison of the least squares Tucker3
(Tucker, 1966) core calculated for these and a superdiagonal
core of ones according to the assumption that the model can
be represented as a constrained Tucker3 model with the core
corresponding to a superdiagonal of ones (Andersen and Bro,
2003; Bro and Kiers, 2003). Core consistency, expressed as
a percentage, indicates the degree of fitting of the Tucker3
core with respect to the assumption of the model (Bro and
Kiers, 2003). Core consistency generally decreases as the
number of components in themodel increases, being 100% for
a model including one component and exhibiting a significant
reduction when a component is added after the appropriate
number of constituents has been reached (Bro and Kiers,
2003). As final criteria, the examination of the meaningful-
ness of the resulting excitation and emission loadings and the
evaluation of the sum of squared error (SSE) of the integrated
spectra on the excitation and emission side (Stedmon and
Markager, 2005) were used to select the number of compo-
nents to be retained in each model.
3. Results and discussion
3.1. PARAFAC components
Fig. 1 presents the explained variance and core consistency
values determined for the IR, IT, CM and IDF models varying
from one to six components. Explained variance ranged
g of independent PARAFACmodels. Samples collected from
al Removed outliers/samples Number of samples-final
8 336
21 323
54a 634
27b 317
ntire data set. However, the respective treated/rawwater samples for
model in order to have consistent pairs of samples for calculation of
n the raw and treated water data sets, 54 instead of 58 samples were
and respective treated water samples (54 samples removed in CM).
1 2 3 4 5 60
25
50
75
100
1 2 3 4 5 60
25
50
75
100
1 2 3 4 5 60
25
50
75
100
Number of components
Exp
lain
ed v
aria
nce/
Cor
e co
nsis
tenc
y (%
)
1 2 3 4 5 60
25
50
75
100
Explained varianceCore consistency
a)
d)
b)
c)
Fig. 1 e Explained variance and core consistency for PARAFAC models with different number of components fitted to the
individual and composite data sets. (a) Individual-raw water (IR model), (b) individual-treated water (IT model), (c) combined
raw-treated water (CM model), (d) individual-differential fluorescence (IDF model).
0
0.2
0.4
0.6
0.8
Excitation/Emission wavelength (nm)
0
0.2
0.4
0.6
0.8
0
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0.8
Loa
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0
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0.8
300 400 5000
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0.8
300 400 5000
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0.8
Entire data set
First half
Second half
a) b)
C1
C2
C3
C4
C1
C3
C4
C2
Fig. 2 e Split half validation of the four components
obtained through PARAFAC application for (a) composite
data set (CM model) and (b) individual-differential
fluorescence data set (IDF model). Excitation and emission
loadings located to the left and right side respectively.
wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 6 7 9e1 6 9 0 1683
between 97.8 and 99.8%, 97.5 and 99.7%, 97.6 and 99.8% and
95.6 and 99.3% for the individual-raw, individual-treated,
composite and individual-differential fluorescence model
respectively. The inclusion of a fifth component caused
a reduction in the core consistency from 77.4 to 21.5%, 83.2 to
23.3%, 83.1 to 13.1% and 89.5 to 36.8% for the IR, IT, CM and IDF
model respectively. As shown in Fig. 1, for all models the
largest decrease in core consistency occurred when adding
a fifth component. The model convergence to a unique solu-
tion, an explained variance over 99.5% for most of the data
sets and the significant reduction in the core consistency
when a fifth component is included in the models, indicated
that four components are adequate for explaining these spe-
cific data sets. SSE results, obtained when the integrated
excitation and emission spectra were evaluated for models
containing from 2 to 6 components, support the inclusion of
only four components in the independently generated PAR-
AFAC models.
From a validation perspective, models includingmore than
four components could not be validated through the split half
technique for any of the data sets, indicating that as shown by
the core consistency analysis, four components adequately
explain the variability in each set of samples. Fig. 2 presents
the results of the split half analysis validation for the CM and
IDFmodels. Overlapping of the spectra for the first and second
half and the entire data set demonstrates that the four com-
ponents are valid explaining the fluorescence signal for the
samples and correspond to real constituents. Spectral char-
acteristics of the retained components include a broad emis-
sion spectrawith a singlemaximum, and an excitation spectra
with multiple maxima as exhibited by NOM fluorophores
reported by previous studies (Hall et al., 2005; Stedmon and
Markager, 2005; Stedmon et al., 2003; Yamashita and Tanoue,
2003). Similar validation results were obtained for each of the
fourmodels. Four components were retained for each data set
because core consistency, explained variance, algorithm con-
vergence to a repeated single output, meaning of the spectral
wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 6 7 9e1 6 9 01684
loadings and SSE of the integrated spectra supported the se-
lection of this number of constituents. Core consistency and
explained variance were 77.4 and 99.8, 83.2 and 99.6, 83.1 and
99.7 and 89.5 and 99.0% for the IR, IT, CM and IDF models
respectively.
Fig. 3 compares the contour plots of the excitation emis-
sion matrices representing each one of the components
retained for the individual and composite data sets models,
and Fig. 4 presents the contour plots of the components
retained for the individual-differential fluorescence model.
Although a visual comparison is possible (Figs. 3 and 4), no
definite conclusions can be drawn regarding the degree of
similarity of the EEM for the retained components in each data
set until a quantitative assessment is conducted. Uncorrected
Matrix Correlation (Burdick and Tu, 1989) has been previously
used as a measure of the spectral overlapping of extracted
and reference components in multicomponent systems; to
examine the effect of micellar media and solvents on the
fluorescence analysis of coal liquids and to compare the effect
of parameters such as the concentration, pH and ionic
strength on the fluorescence analysis of humic substances
(Burdick and Tu, 1989; Hertz and McGown, 1992; Millican and
McGown, 1989; Mobed et al., 1996). The UMC calculation and
its mathematical formulation have been presented in detail
elsewhere (Burdick and Tu, 1989). Complete coincidence of
two spectra under comparison corresponds to a UMC of 1 with
a lower limit of 0 when the overlap is null. Table 2 presents the
inter-model comparison for the components retained for each
data set. UMC values above 0.980 were observed for compo-
nents one to four (C1 to C4) in most of the models, indicating
a high degree of overlapping and only minor variations in the
3D scans representing the components obtained after fitting
Excitation w
250 300 350 400300
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400
450
500
550
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350
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450
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550
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Em
issi
on w
avel
engt
h (n
m)
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450
500
550
IR ModelUMC=0.970
UMC=0.993
UMC=0.994
CM Model
IT Model
C1
C3 UMC=0.999
UMC=0.987
UMC=0.998
Fig. 3 e Contour plots and uncorrected matrix correlation (UMC)
the individual-raw model (IR), individual-treated model (IT) and
of the independentmodels. As presented in Table 2, an overall
inter-model comparison for each component shows an aver-
age UMC above 0.980 with RSD not exceeding 2% in any case.
UMC values corresponding to the comparison between IR and
ITmodels were above 0.993 for C1 to C4 showing a high degree
of resemblance between the independently identified com-
ponents in these data sets. Lower UMC levels were noticed
when C4 in the IDF model was compared with the last com-
ponent retained in the IR, IT and CMmodels. Considering that
C4 is located in a region of short wavelengths, some instru-
mental noise is likely included in this component and sup-
pressed when the differential EEMs are obtained, which could
explain the moderate UMC values. Assessment of the degree
of variability of the contribution of C4 to the fluorescence in
the raw and treatedwater during the period ofmonitoring will
provide additional support for this observation (Section 3.2).
3.2. Treatment process effect on fluorophores
Visual examination of Figs. 3 and 4 and the low UMC values
obtainedwhencomponents 1 and2 (C1 andC2) in the IR, IT and
CM models are compared with the IDF model (Table 2) in-
dicates a change in the order of these components for this last
model (i.e., C1 and C2 in IR, IT and CM are C2 and C1 in IDF
respectively). Considering that components are ordered ac-
cording to their contribution to the explained variance of the
fluorescence signal in the data set (e.g., C1 has the maximum
contribution to the overall variance in the set of samples),
Component 2 in the rawwater appears as themain constituent
in the data set explaining the fluorescence portion that is
removed by the treatment process (IDF model). However,
a higher contribution to the explained variance and higher
avelength (nm)
250 300 350 400300
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550
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250 300 350 400300
350
400
450
500
550
250 300 350 400300
350
400
450
500
550
250 300 350 400300
350
400
450
500
550
UMC=0.994
UMC=0.993
C2
C4
UMC=0.987
UMC=0.999
UMC=0.990
UMC=0.998
based comparison of PARAFAC components determined in
composite model (CM).
250 300 350 400
300
350
400
450
500
550
250 300 350 400
300
350
400
450
500
550
Excitation wavelength (nm)
Em
issi
on
wav
elen
gth
(nm
)
250 300 350 400
300
350
400
450
500
550
250 300 350 400
300
350
400
450
500
550
C1
C3 C4
C2
Fig. 4 e Contour plots of the components identified through
PARAFAC application to the individual-differential
fluorescence data set (IDF model).
wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 6 7 9e1 6 9 0 1685
sample loadings do not necessarily imply that the first com-
ponent presents the highest concentration in the sample,
considering that a calibration procedure has not been per-
formed. As the sample loadings can not be compared on an
inter-component basis, a more certain measure of the distri-
bution of the components in the differential EEMs can be
obtained using the volume of the components in the sample
and its relative distribution. The volume under the 3D surface
(i.e., EEM) corresponding to each component in a specific
Table 3 e Spectral characteristics of the components retained
Component Excitation wavelengthof maximum intensity (nm)a
1 224 (314)
2 <224 (344)
3 <224 (289)
4 <224(279)
a Values in parentheses show the secondary excitation maxima.
Table 2 e Uncorrected Matrix Correlation (UMC) for the compoand composite data sets.
Component Model compariso
IR/IT IR/CM IR/IDF IT/CM
1 0.993 0.970 0.639 (0.971)b 0.987
2 0.993 0.987 0.421 (0.980)c 0.990
3 0.994 0.999 0.988 0.998
4 0.994 0.999 0.971 0.998
a IR, IT, IDF and CM refer to the models fit over the raw water, treated wa
CM as an example, presents the UMC obtained by comparison of the PAR
b Value in parentheses shows the comparison between component 1 in
c Value in parentheses shows the comparison between component 2 in
sample was calculated using a Riemann sum algorithm. Once
the individual volumes for the components were calculated,
their relative distribution was established for each sample.
Volume based distribution of the PARAFAC components in the
IDF model showed that C1, C2, C3 and C4 correspond to an
average of 53.9, 33.9, 10.8 and 1.40% respectively for the 32
month sampling period. This indicates that C2 in the IR, IT and
CM models (C1 in the IDF model) is the most amenable to be
removed through the specific coagulation treatment. Although
itwouldbepossible todraw the sameconclusionby comparing
the average of the samples loadings for C1 and C2 in the
samples of the entire data set, the PARAFAC application to the
differential EEMarrangement offers a novel approachdiffering
from a sample by sample basis and allowing insight on the
composition of the portion of the DOM being removed during
treatment.
Fitting of the individual-differential fluorescence model
(IDF) also offers a visual perspective that demonstrates the
fact that all four components identified in the raw water data
set are being removed by the treatment process following the
order C2 > C1 > C3 > C4. The absence of one or more of the
components identified in the rawwater in the IDFmodel could
indicate a highly recalcitrant component(s) to be removed
through coagulation.
Table 3 presents the spectral features of the retained
components and their character compared toprevious studies.
Components 1 and 2 have a single emissionpeakwithmaxima
above 398 nm and two excitation maxima. The first compo-
nent is comparable to humic-like components previously
reported by Stedmon and colleagues, corresponding to com-
ponent 4 (Stedmon et al., 2003) and resembling components 3
and 6 in Stedmon and Markager (2005) particularly in the
after application of PARAFAC to the composite data set.
Emission wavelengthof maximum intensity (nm)
Character
398 Humic like
466 Humic like-
terrestrial origin
344 Protein like
294 Protein like
nents determined by PARAFAC application over individual
na Average RSD (%)
IT/IDF IDF/CM
0.574 (0.981)b 0.447 (0.994)b 0.983 1.07
0.476 (0.978)c 0.470 (0.997)c 0.987 0.75
0.984 0.988 0.992 0.61
0.960 0.964 0.981 1.83
ter, differential fluorescence and composite data sets respectively. IR/
AFAC component 1 retained in the IR and CM models.
the IR, IT and CM models and component 2 in the IDF model.
the IR, IT and CM models and component 1 in the IDF model.
Table
4e
Distributionofth
ePARAFACco
mponents
inth
era
wandtreatedwateraccord
ingto
CM
modelresu
lts.
a,b
Com
ponent
Average
F max(R.U
)Average
F maxrem
oval(%
)Averageco
mponent
distributione
F maxbasis(%
)Average
volum
e(�
10�3)(nm
)Averageco
mponent
distributionevolum
ebasis(%
)Average
volum
ereduction(%
)
Raw
water
Treatedwater
Raw
water
Treatedwater
Raw
water
Treatedwater
Raw
water
Treatedwater
10.538�
0.047
0.334�
0.082
37.9
�12.7
37.3
�5.2
38.4
�7.8
3.45�
0.30
2.14�
0.52
39.4
�1.7
43.0
�2.7
37.9
�12.7
20.386�
0.047
0.193�
0.045
50.0
�9.9
26.8
�4.9
22.2
�5.0
3.94�
0.48
1.97�
0.46
44.9
�3.1
39.5
�3.3
50.0
�9.9
30.323�
0.071
0.204�
0.075
36.9
�19.3
22.4
�3.9
23.4
�6.3
1.28�
0.28
0.81�
0.29
14.6
�2.8
16.2
�4.3
36.9
�19.3
40.194�
0.192
0.139�
0.129
28.3
�26.5
13.5
�10.2
16.0
�13.0
0.090�
0.088
0.064�
0.060
1.02�
0.97
1.29�
1.2
28.3
�26.5
Total
1.44
0.870
39.5
100
100
8.76
4.98
100
100
43.1
aCM
refers
toth
ePARAFACmodelfitonth
eraw-treatedwaterco
mbineddata
set(n
¼634watersa
mples).
bResu
ltsco
rresp
ondto
averagevaluesforth
e32month
samplingperiod.Associatedstandard
deviationis
includedforeach
value.
wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 6 7 9e1 6 9 01686
emission side. Component 1 also mimics a fulvic-like compo-
nent reported previously for Occoquanwatershed in Northern
Virginia (Holbrook et al., 2006) and humic-like components
found in previous studies in anOhio reservoir (Johnstone et al.,
2009) and lakes in the Yungui Plateau in China (Zhang et al.,
2010). Component 2 shows high resemblance with terres-
trially derived humic acid components reported previously
(Holbrook et al., 2006; Stedmon et al., 2003; Stedmon et al.,
2007; Zhang et al., 2011; Zhang et al., 2010) and has some cor-
respondence with the fulvic acid fluorophore group found by
Stedmon and Markager (2005), although the emission and
excitation maxima are blue shifted. Components 3 and 4 are
located in the simple protein region according to the bound-
aries defined by Chen and colleagues (Chen et al., 2003).
Component 3 resembles protein like compounds, specifically
tryptophan, reported in previous studies (Stedmon and
Markager, 2005; Stedmon et al., 2003; Yamashita and Jaffe,
2008; Yamashita and Tanoue, 2003). Component 4 shows
agreement with tyrosine like components previously reported
(Coble, 1996; Hall et al., 2005; Stedmon and Markager, 2005;
Yamashita and Tanoue, 2003; Zhang et al., 2010).
Component distribution in the raw and treated water was
evaluated in terms of the fluorescence maximum intensities
(Fmax) and using a new volumetric approach. Fmax-based
analysis uses the point of maximum intensity for each com-
ponent in a sample in order to establish the relative con-
tribution of the PARAFAC fluorophore moieties; however, it
does not involve a thorough evaluation of the component
configuration (i.e., a higher Fmax does not necessarily imply
a major presence of the component in the sample). A more
comprehensive approach to determine the predominant
components in the raw water and the variation in the distri-
bution after treatment might be offered by a volume based
quantification. Analysis of the volume under the matrix that
represents each component allows determining its specific
contribution in a particular sample, through examination of
the total volume of the component and not only of a specific
location on it. This approach offers a new basis to overcome
the non-comparability of the sample loadings in an inter-
component basis. Table 4 presents the average component
distribution based on Fmax and on a volumetric basis for the 32
month sampling period. Similar apportionment of the com-
ponents in the raw and treated water was observed. Average
Fmax based distribution corresponded to 64.1% and 60.6% of
humic character components (C1 and C2) in the raw and trea-
ted water respectively, while protein like components (C3 and
C4) represented 35.9 and39.4% in the rawwater andwater after
coagulation-filtration respectively. The average volumetric
based distribution showed that the aggregate contribution of
C1 and C2 was 84.3 and 82.5% in the raw and treated water
respectively. Protein-like components (C3 and C4) constituted
an average of 15.6 and 17.5% in the water before and after
treatment respectively. Thevariability in thedistributionof the
components in the raw and treated water during the period of
monitoring is included in Table 4. Standard deviation asso-
ciated to the component distribution indicates minor varia-
bility for the humic-like substances in the raw water,
particularly in terms of the volumetric contribution. Higher
levels of fluctuationwere observed for C1 and C2 in the treated
water, while fluorophores with protein character, specifically
wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 6 7 9e1 6 9 0 1687
C4, exhibited the highest variation in their contribution to the
water before and after coagulationefiltration.
The volume based distribution indicates that C2 is the
predominant component in the raw water, while C1 consti-
tutes the major portion in the treated water. This differs from
what can be observed based on the Fmax results, which show
that C1 is the principal component in the raw and treated
water. According to the Fmax-based distribution, C3 in the
treated water presents a higher contribution to the fluo-
rescence when compared with C2. However, the volumetric
results reveal a predominance of C2 when compared with C3
in this data set. Volumetric results are in accordance with the
output of the IDF model regarding the predominant removal
of C2 in the treatment process.
Average reduction in Fmax and component volume after
treatment corresponded to 38.0, 50.0, 36.9 and 28.3% for C1 to
C4 respectively. As Fmax and component volumes are exten-
sions of the sample loadings, it is expected that the results in
terms of removal should be in agreement. These removal
levels concur with the order of the components observed in
the IDF model, although the outcome of this model should be
interpreted in terms of removed fluorescence and not on
a percentage basis. Results indicate that C2, a componentwith
humic character and terrestrial origin is themost amenable to
be removed through coagulation (50%) followed by another
humic-like component (C1) whose fluorescence signal
decreased approximately 38%. Protein-like components were
impacted the least during the treatment process with removal
levels of 36.9% and 28.3% for C3 and C4 respectively. The
preferential removal of humic-like components was expected
and concurs with the results of previous coagulation studies
based on PARAFAC application (Baghoth et al., 2011; Beggs,
2010; Beggs and Summers, 2011).
Establishing that independently identified components in
the raw and treated water data sets are similar (Table 2), in-
dicates that minimal transformation beyond physical removal
is taking place in the coagulationefiltration process. New
PARAFAC components are not being formed and the four
components present in the raw water without any pre-
treatment are amenable to be removed by coagulation-
filtration according to the IDF model results. The high degree
of similarity observed for the PARAFAC components in the in-
dependent data sets of raw and treated water was a generally
untested but presumed condition in studies using PARAFAC
(i.e., Fmax) for the evaluation of the NOM behavior in a drinking
water treatment plant. An important contribution of this study
is that this assumption has been proved quantitatively using
a large set of samples collected in a frequency that reflects the
seasonalvariabilityof thewatersourceand thepossiblevarying
conditions in the treatment process. Average values for the
fluorescenceremovalanddistributionof thecomponents in the
raw and treated water during the sampling period have been
presented in thispaper.A furtherand thoughtful analysis of the
temporal variability of the coagulation efficiency and its asso-
ciationwith the seasonal dynamics of the DOMfluorescence in
the raw water will be included in a future publication.
Previous studies have reported a change in the molecular
composition of the remaining NOM after coagulation reflected
in the variation of the fluorescence index (ratio between in-
tensities at 450 and 500 nm at an excitation wavelength of
370 nm) (Beggs et al., 2009) and observed by electrospray ion-
ization Fourier-transform ion cyclotron resonance mass
spectroscopywhenAl and Fe salts were used as coagulants for
a peat leachate (Riedel et al., 2012). FromaPARAFACviewpoint,
a change in the distribution of the components before and
after treatment is established, but only minor variations (as
reflected by the UMC values) are noted in the components
coming from independently fit models (IR and IT models). The
lackof specificity of PARAFAC to reveal the changes in theNOM
observed by Riedel and Bister (2012) might be due to the fact
that this technique decomposes the fluorescence signal in
components representing common fluorophores groups and
therefore gives a more global approach to the NOM constitu-
ents. Also, the fact that the fluorescent moieties only con-
stitute a fraction of the aromatic structures in the NOM must
be considered. Molecular changes in the NOM resulting in new
fluorescence moieties being formed should be reflected in the
IT model in two different ways: (i) the identification of sub-
stantially different components from those retained for the
rawwater data set and (ii) the necessity of inclusion of a higher
number of validated components to explain the variability in
the set comprising the treated water samples. However, as
indicatedby theUMCvalueswhencomponents in the IRand IT
models were compared (Table 2) and according to the final
validation of 4 components in the raw and treated water data
sets, neither a different number of components was identified
in the IT model nor the character of the components in the IT
and IR data sets showed significant differences.
According to these results, PARAFAC might offer a valid
alternative to assess the dynamics and efficiency of the
coagulation process in a full scale drinking water facility. The
feasibility of the use of composite models based on a com-
bined data set (i.e., raw and treated water after coagulation-
filtration) to evaluate the removal levels attained in the
treatment process has been supported by the findings repor-
ted in this paper. However, the application of PARAFAC to
combined data sets in order to determine levels of removal in
systems involving changes in the character of the DOM has to
be further studied.
3.3. Association with the overall fluorescence intensity(OFI)
Alternative approaches to the analysis of the 3D fluorescence
matrices such as the FRI technique could also represent an
option for the evaluation of the coagulation process, as they
allow obtaining a quantitative measure of the EEM for the raw
and treated water. The OFI (Beggs et al., 2009), which corre-
sponds to the total summation of the fluorescence intensities
in an EEM expressed as Raman units (R.U), was used to com-
pare the representativeness of the PARAFAC and FRI ap-
proaches as descriptors of the fluorescence signal in the data
set under analysis.
TheFRI approachpresentedbyChenet al. (2003)wasused to
calculate the total normalized excitation-emission area vol-
ume (FT,n) for the EEMmatrices. This volume is the result of the
summationof theareanormalizedvolumes (Fi,n) of five regions
within operationally defined boundaries that correspond to
different excitation and emission areas within the EEM. The
EEMs used for the calculation of Fi,n are obtained for samples
5 10 15 20 25 30 35
600
800
1000
1200
OFI
(R
.U)
PS
0 0.2 0.4 0.6 0.8 1
600
800
1000
1200
5 10 15 20 25 30 35
200
400
600
800
1000
PS
0 0.2 0.4 0.6 0.8 1
200
400
600
800
1000
R =0.95
R =0.98 R =0.91
R =0.72
b)
c)
a)Raw water Raw water
d)Treated water Treated water
T,n*
T,n* (x 10 ) (nm)-5
(x 10 ) (nm)-5
2 2
22
Φ
Φ
Fig. 5 e Comparison of the representativeness of the summation of the PARAFAC sample loadings (PS)-(a) and (c) and the
total normalized excitation-emission area volume (FT,n*)-(b) and (d) in the description of the overall fluorescence intensity
(OFI) for raw and treated water samples (n[ 317 in each data set). FT,n* is calculated after normalization to Raman units and
at the actual DOC level of the sample.
wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 6 7 9e1 6 9 01688
normalized to DOC¼ 1mg/L and therefore, FT,n is expressed in
units of AU-nm2[mg/L C]�1. As the EEMs of our samples were
determined at the actual DOC levels and fluorescence signal
was normalized to Raman units (nm�1) before calculation of
Fi,n, the reported values of FT,n differ from those that would be
obtained by normalizing the DOC to 1 mg/L and using AU for
the fluorescence intensity. The total normalized excitation-
emission area volume as determined in this paper, has been
labeled as FT,n* and is reported in nm (i.e., R.U-nm2).
The summation of the PARAFAC sample loadings (PS)
(Beggs et al., 2009) for C1 to C4 (i.e., summation of the scores of
all the components present in a sample) was calculated for
each sample as a measure of the PARAFAC findings for the
data set. The linear relationships between the OFI andFT,n* are
presented in Fig. 5b and d) for the raw and treated water
respectively. Fig. 5a) presents the linear association between
the OFI and the PS for the set of raw water samples, while the
respective results for treated water are depicted in Fig. 5c).
PARAFAC sample loadings exhibited better agreement with
the total OFI in each data set. Coefficients of determination
corresponding to 0.95 and 0.98 were observed for the sample
scores summation in the raw and treated water, while lower
levels of association (R2 ¼ 0.72 and 0.91 respectively) were
identified for the correlations including the FRI volumes. This
observation strengthens the idea that when compared with
different approaches, PARAFAC application results better to
explain the characteristic fluorescence signal of samples with
different DOC content in a drinking water treatment train.
4. Conclusions
A long-term monitoring based assessment of the PARAFAC
components before and after coagulation-filtration treatment
in a DWTP has been presented. The generation of different
PARAFAC models, the analysis of the distribution of the
components before and after treatment and the evaluation of
the coagulation effect on the identified fluorophore moieties
lead to the following conclusions:
� Four components are present in each independently fitted
PARAFAC model. Two components of humic character
(C1 and C2) and two components with protein-like nature
(C3 and C4) were found to explain the raw, treated, com-
bined raw-treated and differential fluorescence data sets.
Humic-like constituents were predominant in the raw and
treated water, constituting 84.3 and 82.5% of the samples on
a volumetric basis respectively.
� UMC based analysis demonstrated that from a PARAFAC
perspective, no changes in themolecular composition of the
fluorophores occur and no new components are being
generated in the coagulation process. UMC values also
indicated the feasibility of the use of PARAFACmodels fit on
composite data sets to determine the NOM fate after
coagulationefiltration.
� The PARAFAC models fitted on the differential excita-
tioneemission matrices constitute a valuable tool to study
the effect of the coagulation process on the DOM present in
the raw water. The results of this model allow: (i) deter-
mining the most amenable component to be removed in
a specific treatment process, (ii) establishing the order of
preferred removal and (iii) identifying recalcitrant compo-
nents to be removed in a particular treatment stage.
� The volumetric approach to analyze the contribution of the
PARAFAC components in the raw and treated water, offers
a more comprehensive evaluation of the fluorophore dis-
tribution in the sample than that obtained based on Fmax.
The use of a volumetric analysis might overcome the non-
wat e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 6 7 9e1 6 9 0 1689
comparable character of the sample loadings when differ-
ent components are analyzed.
� The most amenable component to be removed through the
specific coagulation process based on FeCl3 was a humic-
like fluorophore with allochthonous origin (C2), while
protein-like components exhibited the lowest removal
levels.
� The sample loadings summation showed to be an effective
parameter to describe the OFI in the EEMs when compared
with the FRI approach. Higher coefficients of determination
were observed for the PS particularly regarding the raw
water data set.
Appendix A. Supplementary data
Supplementary data related to this article can be found at
http://dx.doi.org/10.1016/j.watres.2012.12.032.
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