Use of Spectra in the Visible and Near-Mid-Ultraviolet Rangewith Principal Component Analysis and Partial Least SquaresProcessing for Monitoring of Suspended Solids in MunicipalWastewater Treatment Plants

NIDIA D. LOURENCO,* FATIMA PAIXAO, HELENA M. PINHEIRO, andALEXANDRA SOUSAIBB—Institute for Biotechnology and Bioengineering, Centre for Biological and Chemical Engineering, Instituto Superior Tecnico, Technical

University of Lisbon, Av. Rovisco Pais 1049-001 Lisbon, Portugal (N.D.L., F.P., H.M.P.); and SMAS de Almada, Praceta Ricardo Jorge, 2-2A

Pragal, 2800-585 Almada, Portugal (A.S.)

The present work assesses the possibility of using spectrophotometry in

the near-mid-ultraviolet and visible wavelength ranges (282–790 nm) for

the direct monitoring of treatment performance in municipal wastewater

treatment plants (WWTPs). Principal component analysis (PCA) was

used to analyze spectral data from samples collected along three WWTP

process lines with different primary and secondary treatment units. The

clustering observed in PCA score plots was mainly attributed to the

suspended solids fraction present in the wastewater and highlighted

differences in solids quality between plants and along the treatment lines.

Thus, satisfactory partial least squares (PLS) calibration models to

estimate total suspended solids (TSS) values from the acquired spectra

could only be established per plant. The PLS models were established

using 1–2 factors, with root mean error of cross-validation and coefficient

of determination values in the 50–86 mg TSS L�1 and 82–95% ranges,

respectively.

Index Headings: Wastewater quality monitoring; Suspended solids;

Ultraviolet–visible spectroscopy; Principal component analysis; PCA;

Partial least squares; PLS.

INTRODUCTION

Wastewater treatment plant (WWTP) monitoring tradition-ally consists of the quantification of incoming and treatedwastewater quality parameters in order to assess the compli-ance to the legal requirements. However, in addition tosampling and sample storage problems, the standard analyticalmethods currently used are most often applied off-line and areretrospective and time-consuming, not allowing a real-timeknowledge of the parameters influencing plant performance.1

Moreover, the use of high-cost and/or toxic reagents and theproduction of wastes that require further treatment alsocontribute to a low frequency of analyses, preventing a realknowledge of the time-dependent wastewater characteristics.Thus, online sensors are fast becoming compulsory tools forwastewater quality monitoring.2 In spite of the remarkableimprovements in performance and reliability of online sensorsfor wastewater treatment monitoring, most of these are stillhigh priced, require frequent maintenance, and are limited tosingle parameter analysis.3,4

Real-time monitoring approaches based on spectroscopy andmultivariate analysis have received considerable attention indifferent contexts. Near-infrared (NIR) spectroscopy has beenextensively used online in the food and agricultural areas5 and

as an in-process analytical tool in pharmaceutical, fine, andindustrial chemistry6 and is also being tested for wastewatertreatment applications.7 Raman spectroscopy is an importantanalytical and research tool with a wide range of onlineapplications, including food, pharmaceutical, and medicalsciences.8 Ultraviolet–visible (UV-Vis) optical sensors are alsobecoming particularly suitable for in-line measurements due tothe availability of high-quality optical fibers and to thedevelopment of robust submersible spectrometers with auto-cleaning systems.9 The use of UV-Vis spectroscopic data forreal-time multivariate monitoring of wastewater treatmentprocesses is now a very active field of research.10

Qualitative exploitation of UV spectroscopic data has beenextensively reported11 and deconvolution methods have beenused for the quantification of spectral interferences correspond-ing to complex dissolved substances and to light scattering dueto the presence of heterogeneous materials.12 These methodsimply the pre-establishment of reference spectra correspondingto known physical-chemical wastewater components (e.g.,suspended solids, colloids, dissolved substances, nitrates, anddetergents) and their major advantage is the direct associationof the spectral information with the expected wastewatercharacteristics.11 However, the deconvolution methods presentthe disadvantage of being difficult to recalibrate when thephysical-chemical wastewater characteristics change and thecorresponding information is not included in the pre-estab-lished reference spectra.13 In order to overcome thesedifficulties, several chemometric procedures based on ad-vanced multivariate analysis have been employed to extractrelevant information from spectra without prior wastewatercomposition knowledge. Among these procedures, principalcomponent analysis (PCA) and partial least squares (PLS) arethe most commonly used. In fact, PCA of UV-Vis spectra ofwastewater samples revealed that the information contained inthese spectra can be extracted and used for quality monitor-ing.14 Furthermore, PLS calibration models for the estimationof organic matter (chemical oxygen demand and total organiccarbon) and nitrate have been successfully derived from UV-Vis spectra of wastewater samples.15,16

The performance of a wastewater treatment plant is highlydependent on the presence of suspended solids. The standardquantification of total suspended solids (TSS) involveswastewater sample collection, subsequent filtration of a knownvolume through a dried and pre-weighed filter (glass fiber,0.45-lm pore size), followed by filter drying and weighing.17

Since this gravimetric method is material and time consuming

Received 18 December 2009; accepted 16 June 2010.* Author to whom correspondence should be sent.E-mail: nidia.lourenco@ist.utl.pt.

Volume 64, Number 9, 2010 APPLIED SPECTROSCOPY 10610003-7028/10/6409-1061$2.00/0

� 2010 Society for Applied Spectroscopy

and its continuous analysis is not feasible in a WWTP, in situmeasurements such as turbidity have been widely used as asurrogate method for TSS estimation.18 However, turbidity is ameasure of the light-scattering properties of the wastewater anddepends not only on the suspended solids concentration butalso on particle size, shape, and composition and can beinfluenced by the presence of colloidal and even some coloreddissolved organic substances such as humic acids.18 Thus, itsuse should be limited to wastewater with constant suspendedsolids characteristics19 and a qualitative analysis of thosecharacteristics should be regularly performed. Furthermore,turbidity values are also dependent on the methods of the usedinstruments, namely on the wavelength used (visible: 550 nm,or infrared: 860 nm) and on the measuring type (attenuance ornephelometric).19 Therefore, measures performed with differ-ent turbidimeters might not be comparable and the use of awider wavelength range, including the visible and part of theUV, can provide further qualitative/quantitative insights intothe wastewater suspended fraction, complementing the turbi-dimetric measurements.

In the present work near-mid-ultraviolet and visible spectra,which include physical and chemical absorption phenomena,were coupled to chemometric techniques, namely PCA andPLS, to characterize process stream samples taken along threedifferent municipal WWTPs. The potential of the near-mid-UV-Vis wavelength range for suspended solids qualificationand quantification was examined, the latter in relation to theuse of the TSS test. The selected wavelength range (282–790nm) corresponds to the use of a current UV-Vis spectropho-tometer with a tungsten-halogen lamp as the only light source.The tungsten-halogen lamps are relatively inexpensive com-pared to other light sources (e.g., deuterium, laser systems),20

are highly stable, cover the entire visible spectra, and can alsocover the near-mid-UV region.21 On the other hand, the silica-based optical fibers used for in-line UV-Vis spectra acquisitionhave a solarization tendency due to UV illumination and, inspite of recent improvements, they still present light transmis-sion deficiencies in the UV region that is not covered by theused tungsten-halogen lamp (200–280 nm).22

EXPERIMENTAL

Municipal Wastewater Treatment Plants: Descriptionand Sampling Strategy. Three different municipal WWTPs(Municipality of Almada, Portugal) were studied in the presentwork and their main characteristics are summarized in Table I.The treatment process installed for the liquid phase of WWTPA is presented in Fig. 1A and includes a physical-chemicaltreatment followed by a biological treatment in activated sludgeaeration tanks (medium load) with secondary clarification. Partof the waste sludge arising from secondary clarification returns

to the aeration tanks and the treated effluent is disinfected withUV radiation prior to discharge. The liquid-phase treatmentprocess of WWTP B is presented in Fig. 1B and comprises apretreatment followed by a biological treatment with tricklingfilters (high load) and final clarifiers. The treatment processinstalled for the liquid phase of WWTP C is presented in Fig.1C and comprises a physical-chemical treatment with lamellarprimary sedimentation (DENSADEGTM units) followed by abiological treatment with aerated biofilters (BIOFORTM units).Part of the treated wastewater is directly discharged and part isdisinfected with UV radiation for reuse.

The sampling strategy adopted in the current work was theone previously established by each WWTP’s analytical qualitycontrol protocol and included three sampling points along thedifferent liquid treatment phases. The first point corresponds tosamples collected prior to primary sedimentation, the secondafter primary sedimentation, and the last after secondarytreatment, which allowed the analysis of suspended solids withdifferent characters occurring in each WWTP along thetreatment line. The three selected sampling points (1, 2, and3) in the line of each WWTP (A, B, and C) are indicated in Fig.1. Samples were either 24-hour composite (refrigerated) orgrab samples, taken 2 or 3 times a week. Spectra were acquiredfrom representative portions of the samples subsequentlyanalyzed for TSS, within 2 to 3 hours of their collection.

Spectra Acquisition and Analytical Data Set. Spectra inthe 190–790 nm wavelength range were acquired in a UV-VisCADAS 100 spectrophotometer (Dr. Lange, Germany) with aquartz cell of 10-mm path length against a blank of distilledwater, using only a tungsten-halogen lamp as light source. Anexample of the spectra obtained for raw samples from WWTPA collected at sampling point 3 is presented in Fig. 2,superimposed with the corresponding spectrum obtained withboth deuterium and tungsten-halogen illumination. The near-mid-UV-Vis spectral region selected for the current studycorresponds to the wavelength range where the two spectra arecoincident (282–790 nm). Near-mid-UV-Vis spectra of thecollected samples, raw and after coarse filtration usingqualitative paper filters (Macherey-Nagel, Germany, no. 4 orequivalent) to exclude roughly the particle fraction sized over10 lm, were thus acquired between 282 and 790 nm.

TSS and chemical oxygen demand (COD) values of thecollected samples were determined according to standardprocedures.17

The acquired spectral data and the corresponding analyticalreference data (TSS, COD) were exported to Matlab 6.5 (TheMathworks Inc., Natick, MA) extended with PLS_Toolbox 3.5(Eigenvector Research Inc., Wenatchee, WA) for PCA analysisand to Gramst32/AI software extended with the PLSplus/IQmodule (Thermo Galactic, Salem, MA) for PLS calibrationdevelopment.

TABLE I. Main characteristics of the three studied municipal WWTPs.

WWTPStartup

year

Treated loads

Primary treatment

Secondary treatment

Volume(m3/d)

BOD5

(tons O2/d) BioreactorSecondary

settling

A: Mutela 2003 26 000 8.9 Conventional settling Activated sludge ConventionalB: Quinta da Bomba 1994 61 000 15.0 Conventional settling Trickling filter ConventionalC: Portinho da Costa 2003 22 400 8.4 Compact unit with chemicals

addition and lamella-type settlingAerated biofilter Absent

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Principal Component Analysis. The main objectives ofPCA are the transformation of the original data’s co-ordinatesystem into a more relevant one and the reduction of theoriginal system’s dimension through the use of a limitednumber of principal components (PCs) that reflect the inherentstructure of the data. A principal component model is anapproximation to a given data matrix X, i.e., a model of X thatis used instead of the complete original matrix, and can berepresented as:

Xðn 3 pÞ ¼ Tðn 3 dÞLTðd 3 pÞ þ Eðn 3 pÞ ð1Þ

In Eq. 1, n represents the number of objects (samples), prepresents the number of variables (in the present case,wavelengths), and d is the number of PCs. T represents thescores matrix, L the loadings matrix, LT the transposedloadings matrix, and E the residuals matrix.

Since PCA identifies the major sources of correlatedvariance in a collection of data, these sources, once identified,can aid in the visualization of the major data trends. In thisway, the data collection can be reduced from a complicatedmultidimensional representation to a more easily visualizedtwo- or three-dimensional space (score plots) describing themain information present in the data.23

Partial Least Squares Model Development. The PLSregression method is the most widely used method formultivariate calibration and corresponds to a guided decom-

position model where the dependent variables, Y, intervenedirectly in the decomposition of the independent variables, X.The purpose of this method is to determine a small number oflatent factors that can predict Y (analytical data; in this case,TSS) using the data in X (in this case, spectra) as efficiently as

possible.

One essential requirement in establishing such a PLS modelis choosing the appropriate wavelengths because part of theinformation gathered in the full spectrum is redundant and thesignals measured at some wavelengths may be nonlinear,represent noise, or contain useless information for the intended

purpose.24 The spectral region used in the developed PLSmodels was here chosen by evaluation of the coefficient ofdetermination (R2) for each wavelength of the data set andselection of the region(s) with higher values for this coefficient.

Due to the limited number of samples available, a full cross-

validation (leave-one-out) procedure was adopted to evaluatethe predictive ability of the PLS models for the training set.Cross-validation was performed by running several sub-modelvalidations in which one object (sample) was left out of thedata set in each run. The average results of each run werecalculated and the quality of the results was evaluated by the

root mean squared error of cross-validation (RMSECV), givenby Eq. 2, where n is the number of samples in the training set,yi is the measured value of sample i, and y \ i is the predictedvalue of sample i using a model constructed without sample i.

FIG. 1. Simplified schemes of the treatment processes installed at WWTP A, B, and C with the three selected sampling points at each plant.

APPLIED SPECTROSCOPY 1063

RMSECV ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

X

n

i¼1

ðyi � y \ iÞ2

n� 1

v

u

u

u

t

ð2Þ

The optimal number of factors to retain in each PLS model wasestablished by the described cross-validation procedure.

RESULTS AND DISCUSSION

Qualification of Wastewater Samples using PrincipalComponent Analysis. PCA was performed using the mean-centered near-mid-UV-Vis spectra of all the 132 raw samplescollected at WWTP A, B, and C at the three sampling pointsselected for each plant (Fig. 1). The score plot resulting fromthis analysis is presented in Fig. 3. In this figure, each acquiredsample spectrum appears as a data point in the domainrepresented by the two first principal components (PC1 andPC2), which, together, capture over 99% of the original datasetvariance. The percentage of variance captured by each PC isgiven in the respective axis legend.

Figure 3 shows that the ranges of score values obtained forthe first two PCs is wide, particularly for PC1, capturing the

vast majority of the information contained in the spectra. Noclear grouping of the samples according to their origin can beobserved, hinting that the types of dissolved and suspendedcomponents of the examined samples are spectrally similar,being mainly present in different proportions in the differentsamples. However, trends can be identified, namely: areduction of spectra scattering in the score plot (representingquality variability) from the influent samples (squares), throughthe mid-treatment samples (triangles), to the treated effluentsamples (circles); a clear reduction of the score value on PC1for the same series of samples (i.e., influent to effluent). Thelatter trend is also visible, though much less clearly, on thescore values on PC2, a result consistent with the far lowerfraction of spectral variance (1.7%) represented by this PC.This score plot also allows a ready comparison of the treatedeffluent (sampling point 3) apparent quality and of itsconsistency among the three WWTPs. Namely, quality isgenerally highest and more consistent for WWTP C (clearcircles), consistently lowest for WWTP B (grey circles), andintermediate but most variable for WWTP A (black circles).

It should be noted that residual suspended solids quality insampling point 3 is expected to be different among WWTPs,thus possibly providing different light scattering and absorptionpatterns. Namely, the suspended solids expected at thedischarge of WWTP A correspond to activated sludge flocsgrown in the aeration tank, those at WWTP B are biofilmaggregates detached from the trickling filter media, and those atWWTP C are mainly dispersed biofilm particles (non-settling)released from the biomedia surface in the aerated biofilter.Azema et al.25 highlighted differences in wastewater UV-Visspectra arising from different suspended solids size fractions,and NIR response to particle size distribution characteristics ofsolid samples has also been well documented.26

In order to clarify the effect of suspended matter on spectralfeatures, new sample subset PCA models were determinedusing the mean-centered, near-mid-UV-Vis spectra of samples,raw and after qualitative filtration, from the three samplingpoints of each WWTP. The obtained score plots are presentedin Fig. 4, together with the one-vector loading plotscorresponding to PC1 and PC2 of each PCA model developed.A loading plot from a PCA model can be built from any pair ofloading vectors plotted against each other and gives informa-tion on the relationships between the original variables and thePCs, i.e., it represents the variables’ interrelationships. Inspectroscopy, however, the one-vector loading plots arenormally more useful in interpreting the PCA results becausethey often resemble spectral features.27 Thus, the spectralregions contributing the most for each PC can be identified.

In what concerns the spectra from raw samples, each plot ofFig. 4 essentially highlights what could be observed in Fig. 3,with a clearer definition of sampling locations in differentclusters. Comparing the filtered with the raw samples in Fig. 4,it follows that this clustering of raw samples is mainly due totheir suspended solids fraction, which is captured by thespectra essentially through the score values on PC1. Thissignificance of PC1 is also suggested by the nearly flat shape ofthe one-vector loadings corresponding to PC1 for each PCAmodel of Fig. 4, which is the typical shape of the TSS referencespectrum generally employed in the deconvolution methods,obtained from the difference between the spectrum of rawwastewater and the spectrum of that wastewater filtered with aparticle size cut off of 1 lm.25 For WWTP B the raw samples

FIG. 2. Comparison of UV-Vis spectra of a wastewater sample collected atsampling point 3 of WWTP A, acquired with a deuterium and a tungsten-halogen lamp as light sources (- - -) and with a tungsten-halogen lamp as theonly light source (–––).

FIG. 3. Score plot of the first two principal components from the PCAperformed using all the acquired near-mid-UV-Vis spectra of raw samplescollected at sampling points 1, 2, and 3 of WWTP A (black squares, triangles,and circles, respectively), WWTP B (gray squares, triangles, and circles,respectively), and WWTP C (open squares, triangles, and circles, respectively).Values in brackets at the axis labels indicate the percentage of the total datavariance captured by each PC.

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collected at sampling point 2 present higher variability andhigher score values on PC1 than the samples collected at point1, suggesting an efficiency problem in the primary clarifierduring the sampling period, possibly aggravated by therecycling of settled biomass from the secondary clarifier. Thisefficiency problem was confirmed by the TSS values of thecorresponding samples, determined by a standard gravimetricmethod. The plot of Fig. 3 already pointed to this problem,with influent and intermediate sample points (grey squares and

triangles) appearing grouped together, but the subset PCAanalysis of Fig. 4 shows it in a much clearer way. Also, it canbe confirmed that the dispersion along PC1 in the spectra oftreated effluent samples from WWTP A, already noted in Fig.3, is indeed due to its suspended matter content, since, in theplot of Fig. 4, this dispersion is only observed for raw samples(black triangles) being much less significant for the corre-sponding filtered samples (clear triangles).

On the other hand, taking only the filtered samples in the

FIG. 4. Score plots of the first two principal components from the PCA performed using the near-mid-UV-Vis spectra of samples collected at sampling points 1 (^,^), 2 (&, A) and 3 (m, D) of the studied municipal WWTP. The closed and open symbols represent raw and filtered samples, respectively. Values inbrackets at the axis labels indicate the percentage of the total data variance captured by each PC. The small graphs represent one-vector loading plotscorresponding to PC1 and PC2 for each PCA model.

APPLIED SPECTROSCOPY 1065

plots of Fig. 4, for which the relevant pollutant fractions are thedissolved and the colloidal, an evolution along the PC2 axis isobserved from sampling points 1–2 to 3. It can thus besuggested that PC2 captures information related to thedissolved/colloidal organic content of the wastewater samples,which is mostly removed in the secondary treatment stage (i.e.,between sampling points 2 and 3). This is consistent with theshape of the one-vector loadings corresponding to PC2 for eachPCA model presented in Fig. 4, with higher loading values(representing relative absorbance) towards the near-mid-UVspectral region.25 However, the irregular shape of the PC2 one-vector loadings for WWTPs B and C indicate that this PC issignificantly affected by spectral noise, consistent with the verylow fraction of data spectral variance it captures (0.5–0.8%, seeFig. 4).

The predictive capacity of scores on PC1 and PC2 forsuspended matter and dissolved/colloidal organics contents,respectively, in the wastewater samples was tentativelyexamined making use of the values of TSS and COD measuredfor the same samples (raw samples). Thus, the correlationcoefficients (R) between the sample scores on each PC and thecorresponding COD or TSS values were determined for thethree PCA models of Fig. 4 and are summarized in Table II.The results confirm that PC1 contains information mainlyrelated to TSS but that it also contains more information onCOD than PC2. This is probably due to the fact that the CODmeasurements were performed on unfiltered samples, asrequired by the discharge permit regulations (for treatedeffluent samples) and the internal monitoring plan. Thus,PC1 would be correlated to COD through the latter’ssuspended fraction. However, TSS and COD were poorlycorrelated with each other, with R values ranging from 0.20(WWTP B data subset) to 0.42 (WWTP A data subset),indicating that COD values had a major contribution fromdissolved matter. The poor correlation of COD to PC2therefore indicates that indeed the latter bears little information

on the dissolved/colloidal organic content of the wastewatersamples.

Finally, in an attempt to attenuate the effect of lightscattering due to suspended matter, the three PCA models ofFig. 4 were re-calculated using the second derivative of theacquired spectra. The correlation coefficient values between thescores on PC1 and TSS were indeed reduced to the 0.22–0.50range, but the correlations between both scores on PC1 andPC2 and the values of COD were only slightly increased forWWTPs A and C and even reduced for WWTP B. It cantherefore be concluded that the near-mid-UV-Vis spectralrange is not adequate for dissolved organic matter estimation inthe examined samples.

Partial Least Squares Models for Total Suspended SolidsEstimation. Following the results of the PCA modelingpresented above, the possibility of using near-mid-UV-Visspectral data to predict sample TSS values through PLScalibration was examined. This was first attempted using mean-centered spectra of all collected samples (sampling points 1, 2,and 3 of Fig. 1) from the three studied WWTPs (correspondingto the spectra on Fig. 2). However, a global model with usefulpredictive capacity could not be established, since themaximum obtained value for R2 was 0.4. This result pointsto inter-WWTP variability of the spectral character of thesuspended matter (e.g., different light-scattering and/or ab-sorption patterns), justifying the dispersion observed on Fig. 3.The second PLS calibration attempt used mean-centeredspectra from all the treated effluent samples (sampling point3 of Fig. 1). This second calibration model also could not beestablished (R2 � 0.5).

Three PLS calibration models were finally developed forTSS estimation, one for each WWTP in this study, using mean-centered near-UV-Vis spectra of samples from the threesampling points of Fig. 1 (corresponding to the spectra oneach graph of Fig. 4, closed symbols). The resulting PLScalibration models for TSS estimation are presented in Fig. 5and their details are summarized in Table III. The presentedcalibration models are highly satisfactory and can be readilyused for efficient supervision and online process control of thethree examined WWTPs. The wavelength range optimallyselected for each PLS calibration model can apparentlyminimize the influence of the particle size, shape, andcomposition in the light attenuated by the suspended solids,allowing the establishment of PLS models for the estimation ofTSS exhibiting different quality (as noted from the data pointdispersion in the score plots of Fig. 4). These models constitutean advantage to the alternative turbidity measurements for the

TABLE II. Correlation coefficient values between TSS or COD and thescore values of PC1 or PC2 of each PCA model presented in Fig. 4 (rawsamples).

Measured parameter

WWTP A WWTP B WWTP C

PC1 PC2 PC1 PC2 PC1 PC2

TSS (mg L�1) 0.89 �0.24 0.61 �0.01 0.82 �0.04COD (mg O2 L�1) 0.51 0.20 0.42 0.22 0.52 0.10

FIG. 5. Measured TSS (standard gravimetric procedure) against values predicted by the PLS calibration models developed for WWTP A, WWTP B, and WWTP Cusing near-mid-UV-Vis spectra of samples collected at the three sampling points of each WWTP.

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fast estimation of TSS values because turbidity is measured at asingle wavelength (860 nm or 550 nm according to the ISO7027 standard)28 and different wavelength choices exhibitdifferent sensitivity levels to varying particle size.19 In fact, theintensity of light scattered by a suspension decreases with theincrease of the incident radiation wavelength and smallparticles are best detected at short wavelengths.19 Thus, TSSestimation methods on the basis of multi-wavelength measure-ments such as those here proposed constitute a potentialalternative to turbidity measurements that could be applicableto a wider range of suspended solids characteristics.

CONCLUSION

The results of the present work showed that spectra ofaqueous sewage samples acquired in the visible and near-mid-UV region contain information that can be extracted and usedfor WWTP monitoring. For the wastewater samples from themunicipal WWTPs here examined the use of such spectra andtheir chemometric treatment through PCA and PLS modelsrevealed a high application potential as a fast, simple, and cost-effective method for wastewater quality monitoring, usableonline to provide real-time control data. TSS value estimationcould be carried out with a single PLS model for differentsampling locations at each WWTP, in spite of the apparentlyvarying character of suspended solids. Through PCA models,near-mid-UV-Vis spectra were able to directly provide usefulinformation on the evolution and variability of the wastewatercharacteristics along the treatment lines of the three WWTPs,mainly related to its suspended matter content. The latter is acritical parameter, often associated with poor performance of aWWTP.

ACKNOWLEDGMENT

N.D. Lourenco acknowledges the financial support from Fundacao para aCiencia e a Tecnologia (FCT, Portugal) through a post-doctoral research grant(SFRH/BPD/31497/2006, Portugal).

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18. G. S. Bilotta and R. E. Brazier, Water Res. 42, 2849 (2008).19. E. Huber and M. Frost, J Water SRT – Aqua 47, 87 (1998).20. http://www.msscientific.de/home.htm, accessed on 12 May 2010.21. L. Ylianttila, K. Jokela, and P. Karha, Metrologia 40, S120 (2003).22. M. Belz, F. A. Klein, H. S. Eckhardt, K.-F. Klein, D. Dinges, and K. T.V.

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TABLE III. Summary of the PLS calibration models developed for theestimation of TSS at each of the three studied municipal WWTPs.

WWTP Factors R2RMSECV(mg L�1)

Concentrationrange (mg L�1)

Spectralregion (nm)

A 2 0.87 50 14–485 402–790B 1 0.82 86 60–847 386–790C 1 0.95 80 2–1000 378–790

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