Determination of Nitrate in Municipal Waste Water by UV Spectroscopy

8/13/2019 Determination of Nitrate in Municipal Waste Water by UV Spectroscopy

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ANALYTICACHIMICA

CTAnalytica Chimica Acta 312 (1995) 107-113

Determination of nitrate in municipal waste water by UVspectroscopy

Mikael Karlsson a* * Bo Karlberg b, Ralf J.O. Olsson aa Department of Chemical Engineering and Technology, Royal Instirute of Technology, S-100 44 Stockholm, Sweden

b Department of Analytical Chemistry, Stockholm University S-106 91 Stockholm, Sweden

Received 11 November 1994; revised 3 March 1995; accepted 29 March 1995

Abstract

A method is proposed in which diode-array UV-visible spectroscopy and multivariate data analysis are applied todetermine nitrate in municipal waste water. No filtering of the samples is required and no reagents are added. The workingrange for the nitrate determination is 0.5-13.7 mg/l (0.008-0.22 mM). The relative standard deviation was found to be3.4%. Other constituents, notably total phosphorus, total nitrogen, ammonium nitrogen and iron can be determinedsimultaneously with the same method. The developed method can also be used to classify samples. The described methodconcept is well suited for in-line monitoring.

Keywords: UV-Visible spectrophotometry; Nitrate; Waste water; Chemometrics; Waters

1 Introduction

Nitrate is the most abundant form of inorganicnitrogen. It is formed during the nitrification stepwhen municipal waste water is aerated. Continuousmonitoring of nitrate is desired during both the nitri-fication and the denitrification steps. Many methods

have been proposed for nitrate measurement, forinstance methods involving ion selective electrodes,direct UV spectroscopy, and wet chemistry methods,the latter methods being automated using either air-segmented flow or flow injection analysis principles.

Most commercial nitrate selective electrodes arebased on incorporating an ion exchange material inPVC membranes [l]. The dynamic range for the

* Corresponding author.

nitrate activity is typically 10-l to 10m5 M. How-ever, several anions interfere, chloride ions in partic-ular, which means that the electrode needs to berecalibrated frequently. The life-time of the mem-brane is limited due to the continuous leaching of theion exchanger.

The nitrate ion absorbs strongly in the UV range

with a maximum absorbance at 205 nm and methodsbased on this property have been developed previ-ously [2,3]. When such a sensor is used in municipalwaste water samples, a filtering step has to be in-cluded before the UV measurement can be per-formed to account for turbidity. Furthermore, if themethod is employed for continuous monitoring, theoptical parts immersed in this type of sample mustbe regularly cleaned due to biofilm formation. Tocompensate for variations in sample turbidity, mea-surements are usually performed at two wavelengths,

0003-2670/95/ 09.50 0 1995 Elsevier Science B.V. All rights reservedSSDI 0003-2670(95)00179-4


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108 ht arlsson et al. Analytica Chimica Acta 312 I 995) 107-l 13

predominantly at 220 and 275 nm. The absorbanceregistered at the latter wavelength is subtracted fromthe absorbance value observed at 220 nm in accor-dance with the proposed manual method [4].

The commonly applied official method for deter-mination of nitrate is based on reduction of nitrate tonitrite in a cadmium column followed by addition ofsulphanilamide and N-1-naphthylethylenediaminehydrochloride (NED) [4-71. Again, the sample solu-tions must be filtered. Automated versions of thisbatch nitrate method exist either developed for air-segmented flow systems or for flow injection sys-tems. The automated versions can be used for moni-toring purpose but they involve use of toxic reagentsand they require frequent recalibrations. In order to

avoid cadmium, hydrazine has been suggested as areducing agent for nitrate. This reduction is slow atroom temperature which means that heating andtemperature control are required in order to obtaindesired sensitivity [7,8].

Thomas et al. [9,10] have developed a method fornitrate based on UV absorbance measurements atseveral wavelengths. The calculation of the nitrateconcentration was made using a multilinear regres-sion method.

The method presented in this paper comprises

steps of absorbance measurements in the spectralrange 180-820 nm directly on unfiltered samples,comprehensive calibration using samples with knownnitrate concentrations and chemometric data treat-ment (PLS, partial least squares).

preserved and had to be analyzed within 8 h ofacquisition. Altogether 114 samples are included inthis study and they were treated as follows. Anaccredited laboratory, Stockholm Vatten, performed

determination of nitrate according to a referencemethod based on measurement of absorbance at 220nm and at 275 nm [4]. The absorbance differencewas calculated and used in a traditional calibrationand evaluation procedure. Total phosphorus, totalnitrogen, ammonium nitrogen, suspended solids,COD, BOD, alkalinity, and iron were determinedaccording to their respective official methods [4-71by the same accredited laboratory being a part of theauthority approved control program for the wastewater plants in Stockholm. For determination of

ammonium nitrogen, nitrate, alkalinity and iron allsamples were filtered through a 0.45 pm membranefilter; in all other tests unfiltered samples were used.In addition to these determinations, but simultane-ously, an unfiltered portion of each sample wassubjected to the proposed method: the sample por-tions were shaken and then directly transferred to a10 mm quartz cuvette. A spectrum in the wavelengthrange 190-820 nm was recorded consisting of 316absorbance values (one absorbance value at everysecond nm) for all 114 samples by using two differ-

ent HP 8452A instruments in random order. Thesoftware used in this study was MATLAB@ Version4.2 [ll].

3. Theory

2. Experimental 3.1. Selection of multivariate calibration technique

Samples were taken daily in three different mu-nicipal waste water treatment plants in the Stock-

holm area during a period of six months to be able tocover seasonal and occasional variations. For eachplant, three different locations were selected for sam-ple acquisition, namely the incoming water site, thebasin holding water that just had been processed inthe chemical step, and, finally, the effluent watersite; in all, nine sampling sites.

Week samples consisted of aliquots collecteddaily during one week and these samples were pre-served by adding concentrated sulphuric acid (1 mlacid/100 ml sample). Day samples were not

Due to the shape of spectra obtained for unfil-tered, municipal waste water samples containing ni-

trate, see Fig. 1, it is impossible to correlate ab-sorbance values at just one wavelength to the con-centration of nitrate. An interfering phenomenon suchas light scattering due to turbidity is observed. Ab-sorbance maxima occur in the region 200-220 nm asexpected for samples containing nitrate. However,the presence of UV absorbing species other thannitrate cannot be excluded which adds to the com-plexity of calibration and evaluation of results. Con-sequently, a multivariate data analysis approach isrequired using as many absorbance values as possi-


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M. Karlsson et al. Analytica Chimica Acta 312 1995) 107-l 13 109

Fig. 1. Absorbance spectra of 114 unfiltered municipal waste

water samples.

ble in a spectral scan. Several different approacheswere initially tested. Classical multiple linear regres-sion (MLR) using all wavelengths fails completelyas a calibration technique. However, by using aniterative selection of five wavelengths for classicaland inverse MLR useful results can be obtained, seeTable 1. In the same table results from applying thepartial least squares (PLSl) method are given. TheMLR models were made with absorbance values atevery single wavelength and the wavelength thatresulted in the best model, i.e., the model that yieldedthe lowest RMSEP value, was selected. The selectedwavelength was combined with all other wave-lengths and the best combination was selected, thiswas performed until in all five wavelengths had beenestablished. When comparing the MLR models withthe PLSl model the latter gives the best predictionsof nitrate and it offers the useful additional advan-tages of being self-diagnostic and being able to

detect outliers. Consequently, the PLSl model be-came the model of choice for all further work. ThePLSl model is based on the NIPALS algorithm [12].

It is an obvious advantage if one common calibra-

tion model can be used for any sample in a wastewater plant. A large concentration range of the ana-lyte can then be covered since all samples are in-cluded in the model. The regression model willfurthermore be more robust if a large concentrationrange is covered. The samples used for calibrationpurposes were collected over a period of at least sixmonths and the concentration ranges for the variousanalytes were rarely exceeded during more than oneyears time judging from analytical data produced bythe accredited laboratory (Stockholm Vatten). How-

ever, if a dramatic change of the conditions in thesewage plants would occur an extended calibrationmust be performed. When the PLSl method is usedsuch a change would be detected with the presentcalibration model.

3.2. Selection of pretreatment method for the spec-tral data

The assumptions leading to Beers law are notfulfilled when light scatter effects are present. Theseeffects are caused by the physical properties of thesample, e.g., turbidity variations. Various spectralpretreatment methods and theories have been devel-oped to improve the accuracy in the PLS regressionamong which the following three methods have beenused in this paper:. the multiplicative scatter correction (MSC) method

in which each spectrum is corrected in bothoffset and slope by comparison with the meanspectrum of the total dataset [13];

Table 1Comparison of different multivariate calibration models for nitrate

Sample origin PLSl using Classical MLR usingall wavelengths 5 wavelengths

RMSEP Slope/Int. RMSEP Slope/Int.

Basin 1 0.3041 0.99/0.05 1.9550 4.67/ - 37.5Basin 2 0.2359 0.86/0.40 0.2988 1.23/ - 0.61Basin 3 0.1518 0.98/0.01 0.2502 0.59/0.30All samples 0.3619 0.99/0.01 5.5322 0.70/ - 1.15

Inverse MLR using5 wavelengths

RMSEP Slope/bit.

0.4215 0.99/0.010.2676 o.so/o.ss0.1573 O.SS/O.lO0.3880 0.99/0.01


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110 M. Karlsson et al. Analytica Chimica Acta 312 1995) 107-113

the 2nd derivative method -the absorbance valuesforming a spectrum are derived twice which re-sults in a transformed spectrum consisting only ofthe relative changes between absorbance values

obtained at adjacent wavelengths. Derived peakintensities tend to change more linearly with ana-lyte concentration than corresponding raw in-tensities [14]. The 2nd derivative was performedaccording to the Savitzky-Golay convolutionderivation [151;the auto-scaling method (or z-transform) is a com-bination of mean-centering and normalization [161.The normalization was done by dividing eachvariable by its standard deviation (this gives eachvariable unit variance).

The MSC was performed according toR,-hi

A,= -ii 1)

where A, = transformed absorbance; R, = apparentabsorbance; Lii = least squares estimation of the in-tercept parameter; Ai = least squares estimation ofthe slope parameter; i = the sample spectrum avail-able (i = 1, 2, . . . , 114); k = the absorbance valuesat available wavelengths in a scan (k = 1, 2, . . . ,316).

A factorial design model [17] was used to estab-lish the optimal pretreatment or combination of pre-treatments (MSC, 2nd derivative, auto-scaling) andthus minimizing the prediction errors arising fromthe modeling of the data. In the factorial design thedifferent pretreatments have been modeled as vari-ables at an on/off state [18]. The design is a full 23design (see Table 2). This methodology was per-

formed separately for nitrate but also, separately, forother sample constituents.

In order to evaluate the influence of the differentpretreatments the root mean squared error of predic-

tion (RMSEP) [191 was calculated according to

RMSEP = (ei-ci) (2)I=

where IZ = the number of samples (114); ti =modelled descriptor value; ci = the traditionallymeasured descriptor value; i = the sample number(i = 1, 2, . . ., 114).

The RMSEP was then evaluated as a function of anumber of latent variables kept in the PLSl model.

The pretreatments yielding the smallest RMSEP forthe different descriptors were used in the subsequentPLS modelling. All evaluations were made withleave-one-out cross validation modelling [20] insteadof dividing the samples into a calibration set and avalidation set.

4. Results and discussion

The results from the evaluation of the differentspectral data pretreatments can be seen in Table 2: A - sign in the Pretreatment column stands for apretreatment that was not performed and a +sign for a performed pretreatment; the first columncorresponds to MSC, the second is the 2nd derivativeand the third column is auto-scaling.

The largest difference in model quality is depen-dent upon the use of auto-scaling, see Table 2, alone

Table 2

Results of the pretreatment optimization for nitrate

Pretreatment PCS

- - 15+ _ _ 15- + _ 13+ + _ 1.5_ + 14+ - + 12_ + + 7+ + + 8

Intercept

(ppm)

- 0.0200.0441

- 0.005-0.001

0.38520.37000.34800.3897

Slope r2

1.0001 0.99550.9960 0.99480.9991 0.99601.0009 0.99550.9969 0.99511.0025 0.99471.0051 0.99360.9985 0.9935

RMSEP

(ppm)

0.08430.09750.07430.08410.22970.24480.25570.2684


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M. Karlsson et al. Analytica Chimica Acta 312 1995) 107-113 111

or in combination with any of the other two datapretreatment methods. There is a significant differ-ence, a factor three reduction of precision whenusing auto-scaling.

The best results in this study are obtained whenthe 2nd derivative data pretreatment is used alone.The explanation for this might be that this pretreat-ment results in a spectrum consisting of only therelative changes between absorbance values at adja-cent wavelengths. In this situation light scatteringeffects due to particles in the sample are reducedsince they are mathematically filtered away. Themethod also decreases spectral noise since the trans-formed points are the evaluation of a polynomewhich has been fitted to the adjacent spectral points.

This means that the transformed spectra contain themain chemical information.

The MSC method, which also adjusts for lightscattering, does not improve the model. One proba-ble reason for this is that spectral data differs forsamples taken at various sites in the municipal wastewater treatment plants. When applying the MSCmethod all spectral data are used simultaneously inthe algorithm and problems may then arise if thespectra are too different. The method does not com-prise a noise reduction step.

Auto-scaling fails to improve the model since allvariables are given the same mathematical weight in

--0 2 4 6 0 10 I2 14

REFERENCe CONCWTRAITON ppn

Fig. 2. Predicted concentration vs. reference concentrations ofnitrate (114 waste water samples). Slope = 0.9991; intercept =- 0.005; RMSEP = 0.2727; r 2 = 0.9960.

25

Fig. 3. Histogram showing the residuals between predicted and

reference values. The normal distribution curve (m = 0.0429;s = 0.3101) is denoted by the full line.

spite of the fact that the chemical information is notequally distributed over all variables.

In Fig. 2 the predicted values of nitrate in 114samples are plotted against the nitrate values ob-tained with the reference method. Only the 2ndderivative data pretreatment method was applied inthis case.

Significantly, the prediction error is smaller thanthe error reported for the reference analysis. It mayseem peculiar that a second order analytical methodhas a smaller error than the reference method. How-ever, it can be explained as follows.

-0 16 7 6 9 10 1 12 13 I4 15 16

Fig. 4. Score plot (PC1 vs. PC2) showing waste water samplestaken at three different stages in the plants.


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112 IU. Kar lsson et al./A nalyti ca Chimica cta 312 1995) 107-113

Table 3Results of modelling for total phosphorus, total nitrogen, ammonium nitrogen and iron

Constituent r2 Range Slope

(ppm)

Intercept

(ppm)

RMSEP

(ppm)

Total phosphorus 0.6731 0.03-5.20 1.0137 0.0264 0.9162Total nitrogen 0.7643 15.0-39.0 1.0073 -0.2320 2.8378Ammonium nitrogen 0.8144 0.70-30.20 0.9917 0.1462 2.5742Iron 0.9124 0.30-21.20 1.0090 - 0.0370 2.1741

If:the predicted value, 2, equals the true value, Y,with a prediction error, 2, Eq. 3;the reference value equals the same true valuewith a reference analysis error, crep, Eq. 4

Y=Y+o (3)

Yref= Y+ %rer (4)

The reference errors will be normally distributedand centered around zero if no systematical errorswere introduced during the analysis, i.e.:

f E Nmd (5)

However, the prediction errors are most probablynot normally distributed since they are obtained froma regression model, i.e., the prediction model doesnot contain any random source; thus

E,,f +LWr,o) (6)

Consequently, when examining the distribution ofthe residuals for the difference between p and Yefone can determine the one of .? or &,,which willdominate since

Y- Yef = .Z E,,f (7)

In Fig. 3 the residuals for the difference betweenY and Yef for all 114 samples are shown in a

histogram. The residuals have zero mean and arenormally distributed. This indicates that the predic-tion error is smaller than the reference error, i.e.:

Eref> > B (8)

The same model was also used in an attempt topredict sample constituents other than nitrate. Themodel results for prediction of total phosphorus, totalnitrogen, ammonium nitrogen and iron can be seenin Table 3, where the model quality parameters forthe best model, as discussed above, are listed. To

improve the model for these parameters a compre-hensive calibration is required entailing samples withlarger matrix and concentration variations than thoseutilized in this study. Most likely, further parameterslike, for instance, suspended solids, BOD and COD

can be semiquantitatively predicted along with ni-trate, total phosphorus, total nitrogen, ammoniumnitrogen, and iron based on one single run of asample spectrum. This possibility is certainly verychallenging when considering unattended monitoringof waste water since no handling of reagents isneeded.

When the PLS algorithm is used to build a regres-sion model it is also possible to use the intermediatescores and loadings vectors to obtain qualitativemodels, e.g., classifications. In Fig. 4 the score

vector of the 1st and 2nd principal component plot-ted against each other and the samples forms threeclusters. The clusters represent samples from thethree different sites in the municipal waste waterplant. Thus, it is possible in this case to identify fromwhich site a sample is taken by the scores. Theclassification models can consist of n number ofscore vectors. It is also possible to identify samplesthat have been tampered with and abnormal samplescontaining unusual chemicals that can be harmful forthe biological activity in the plant. Samples spiked

with a UV absorbing compound such as nitroben-zene were identified as outliers by the model.

5 Conclusions

The developed concept has been shown to workwell for collected, real samples and it should alsohave a large potential for in-line monitoring. Sam-ples have been collected from three different waste


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M . Karl sson et al. An alytica Chi mica Acta 312 1995) 107-113 113

water plants at three different sites during a timeperiod of six months which means that seasonal andoccasional variations are accounted for in the calibra-tion model. By studying the laboratory reports over a

period of more than one year the conclusion is thatthe total concentration ranges of most of the analyteswere covered in the calibration. This does not mean,however, that the calibration model would be ade-quate for waste water samples collected in anothergeographic area with a different industrial structurethan that of Stockholm.

The selection of PLS as a multivariate calibrationtechnique was based on the obvious advantages ofthe outlier detection and the inherent self-diagnosticabilities of this technique. Samples can furthermore

be classified.The working range for the nitrate determination

was 0.5-13.7 mg/l and the relative standard devia-tion was 3.4%. Simultaneously, a quantitative deter-mination of analytes other than nitrate in the samplecan be performed, e.g., total phosphorus, total nitro-gen, ammonium nitrogen and iron.

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Determination of Nitrate in Municipal Waste Water by UV Spectroscopy