ABSTRACT 1. INTRODUCTION

Transcription

1 Eleventh LACCEI Latn Amercan and Carbbean Conference for Engneerng and Technology (LACCEI 2013) Innovaton n Engneerng, Technology and Educaton for Compettveness and Prosperty August 14-16, 2013 Cancun, Mexco. Proposal For Recurrence, Level Of Importance And Qualty Detecton Of Uv-Vs Spectra And Target Pollutant Dataset An Omar Rayo s esthetcs nspraton for UV-Vs graphcal representaton Davd Zamora Grupo de Investgacón Cenca e Ingenería del Agua y el Ambente, Facultad de Ingenería, Pontfca Unversdad Javerana, Carrera 7 No , Bogotá, Colomba, davd.zamora@javerana.edu.co Andrés Torres Grupo de Investgacón Cenca e Ingenería del Agua y el Ambente, Facultad de Ingenería, Pontfca Unversdad Javerana, Carrera 7 No , Bogotá, Colomba, andres.torres@javerana.edu.co ABSTRACT In stu UV-Vsble spectrometrc probes offers the opportunty to follow the behavor of water pollutants through the use of mathematcs models. Hence, the results of these models depend on the qualty of the nput data and ts sgnfcance wll be framed by how representatve they are of the water qualty of the system: absorbance spectrum and pollutants concentratons obtaned from standard laboratory analyss. Therefore, a new method s beng developed and presented n ths paper that enables to detect, n a multvarate way, the wavelengths most closely related to the target pollutants, and hence that can represent n a better way the nteractons of the water system wth the beam n the spectroscopc process, carred out by the measurement nstrument. In the case study of ths research, absorbance spectra and TSS, COD, and fltered COD concentratons of grab samples obtaned n the affluent and effluent of San Fernando WWTP (Medellín, Colomba) were used. These results allow dentfyng the qualty of the data beforehand and establshng the low predctablty of some regressve models. Keywords: Wavelengths n UV-Vsble Spectra, Pollutants, Affnty, Data Qualty. 1. INTRODUCTION Informaton s the raw materal to meet the smplcty or complexty of a process n the dfferent felds of knowledge. Therefore, quantfy and qualfy the water qualty s a task that has allowed vsualze roughly the dynamcs of the dfferent water systems and try to understand the nteracton of water wth both natural and artfcal systems. Then, n the specfc case of wastewater treatment plants (WWTP) where the matrx of both nfluent and effluent are hghly complex and wth a hgh tme-space varablty, hgh frequency representatve nformaton of the dynamcs related to pollutant flows wll be requred, even more when the plant nfluent correspond to a combned sewer system and on whch waste waters of ndustral processes generate large fluctuatons characterzed by volumetrc flow rates, temperatures and pollutant loads (Hoppe et al., 2009). Aganst ths requrement, measurng nstruments n stu and contnuously become a metrology tool that allows a near-real-tme observaton of the pollutants dynamcs: UV-Vsble spectrometry probes help track the fngerprnt and quantfy, as well as qualfy, the presence of contamnants of nterest through chemometrc models. These models formalze the process of correlatng the physcal, chemcal or bologcal propertes present n the spectra. In order to do ths, spectra are measured by establshng a tranng set of samples of those values that are wanted to have several propertes (from standard or reference values obtaned n the laboratory) whch are known to have been measured by conventonal means (DFoggo, 2000). Takng nto account ths nformaton and usng 1

2 regresson functons, a calbrated equaton s created whch wll determne and quantfy the propertes of the sample to be represented across the spectrum. Fnally, the value of property can be predcted n an unknown sample by assessng the same spectrum usng the mathematcal calbrated functon. Therefore, the results of these models depend on the ntrnsc qualty of the nput data and ts sgnfcance wll be framed by the representatveness of the water qualty of the system: absorbance spectra and concentratons of contamnants derved from laboratory analyss. But t s a narrow scope to use the spectral rchness only n the estmaton of a set of smple or compound pollutants (NO 2 or Chemcal Oxygen Demand respectvely), due to the fact that, wth ths real-tme nformaton, alert and alarm states can be generated (e.g. Gruber et al., 2004; Langergraber et al., 2004; Reger et al., 2008)), as well as control rules and why not the predcton of the dgtal footprnt and thus the pollutants matrx n the near future. In order to develop these actvtes usng the spectral nformaton t s necessary to know whch wavelengths n the UV-Vsble have a sgnfcant relatonshp wth the property or pollutant of nterest over tme and whch affnty wll allow model results to be accurate. Due to the fact that the wastewater montorng has to deal wth a matrx of many dssolved and suspended compounds, the superposton of many substances n ndvdual absorbances even at tmes wth supermposed peaks may cause cross senstvty and lead to poor performance of the sensor (Langergraber et al., 2003). Normally, n order to establsh such relatonshp, a correlaton coeffcent R 2 s used, assumng lnearty between pollutant concentraton and the absorbance values. However, some drect chemometrc models may be used only f the spectra of all the components are known and t can be sad that the measured spectrum wll be the sum of the hypothetcal ndvdual spectra of each of the sample consttuents, makng the Lambert-Beer s law vald, whch s not satsfed n the case of waste water, where a large number of unknown compounds are present (Langergraber et al., 2003; Escalas et al., 2003; Vargas and Butrón, 2006). In addton, by usng lnear correlatons, many local solutons wth smlar performances can be found, makng dffcult to dentfy only one wavelength wth the hghest relevance (Lorenz et al., 2002). In the present study a method to detect n a multvarate way the wavelengths most closely related wth the pollutant concentratons and to assess the qualty of the data, takng nto account ssues such as cross-senstvty (senstvty to a substance that predsposes the sample to be senstve to other substances lsted by ther chemcal structure (Fleschmann et al., 2001)) and nonlnearty between absorbance values and pollutant concentratons was developed. 2. MATERIALS AND METHODS 2.1 METHOD OF SELECTION OF THE WAVELENGTHS MOST CLOSELY CORRELATED WITH THE PARAMETER ANALYZED ZATO The selecton of the wavelengths most closely related wth a target pollutant s crucal for assessment of equvalent concentratons based on fngerprnts. Statstcal regresson models allow to transform the absorbances of multple wavelengths n equvalent concentraton values when carryng out a local calbraton, whch s able to determne the specfc wastewater composton and the possble effects that they generate (Gruber et al., 2006). In the present study, a method to detect wavelengths related to pollutant concentratons was developed. The method, called ZATO, consders fve bvarate functons n order to calbrate regressve models: lnear, polynomal of second and thrd degree, logarthmc and power (see Eq. 1 to 5). The varable to be estmated s the concentraton of the target pollutant based on absorbance values for a partcular wavelength of the spectra. Therefore, t would generate the same number of models for each regresson functon as the number of wavelengths wth absorbance values. A B x( nm) k 1 Equaton 1: Lnear model yˆ 2

3 yˆ yˆ 2 A B x( nm) B x ( nm) k 1 Equaton 2: Polynomal of second degree 2 3 A B x( nm) C x ( nm) D x ( nm) k 1 Equaton 3: Polynomal of thrd degree A B log( x( nm)) k 1 Equaton 4: Logarthmc model yˆ F E x ( nm) k 1 Equaton 5: Power model yˆ In Equatons 1 to 5, and are the coeffcents and exponent respectvely, calbrated n each of the executons, are the absorbances of each wavelength of the analzed samples and are the set of equvalent concentratons of a contamnant estmated by each model n each executon. Then, sum of squared errors (SSE) between reference laboratory concentratons and equvalent concentratons obtaned from each model are computed accordng to Eq. [6]. SSE n y yˆ 1 Equaton 6: Sum of Squared Errors After havng SSE values for each regressve model, each wavelength s ordered based on the followng crteron: for a partcular regressve model, wavelengths wth low SSE values are consdered more relevant than SSE wth hgher ones. Based on the above procedure used to calculate the predcton errors, an Importance Factor (IF) s proposed, whch explans the affnty between the wavelengths and the target pollutant. In order to avod bas because of the presence of possble outlers n the data, a random selecton of a fracton (e.g. 67 % of the data) of the dataset number of tmes s proposed. Wth ths method, the recurrence of a wavelength to occupy a certan poston out of 220 possble postons, based on IF ndcator, can be computed. Ths recurrence, analyzed graphcally for now, s used for two purposes: () to establsh the level of mportance of a wavelength on the target pollutant; () to assess the qualty of the data, snce a greater dsperson of recurrences at dfferent levels of mportance of many wavelengths can confrm the low affnty between laboratory concentratons obtaned and the UV-Vs spectra related. In fact, these possble benefts were confrmed numercally (not shown n ths paper) for both selecton of wavelengths to construct chemometrc models and outlers detecton. 2.2 ARTISTIC SPECTRAL REPRESENTATION Fnally, the desgn of the graphs for the results representaton s nspred on the work of Colomban geometrcoptc artst Omar Rayo ( ). The method of spectral analyss and graphs that represent t were developed and programmed nto the platform R (R Development Core Team, 2012). 2 3

4 Fgure 1: Absorbance spectra for nfluent (left) and effluent (rght.) of San Fernando WWTP Fgure 2: TSS, COD and CODf concentratons for nfluent (left) and effluent (rght.) of San Fernando WWTP 3. CASE STUDY In the case study of ths research, absorbance spectra and Chemcal Oxygen Demand (COD), fltered COD and Total Suspended Solds (TSS) concentratons of grab samples obtaned n the affluent and effluent of San Fernando wastewater treatment plant (WWTP) (Medellín, Colomba) were analyzed. These samples were orgnally taken n order to acheve a local calbraton of the probes spectro::lyser used n nfluent and effluent of the WWTP. In Fgures 1 and 2 the concentraton data and spectra s shown, these last two probes measured wth spectro::lyser of dfferent pathlengths: 2 mm for nfluent and 5 mm for effluent. 4

5 4. RESULTS AND DISCUSSION Fgures 3 to 8 were obtaned usng ZATO method. From these fgures, t can be observed that the wavelengths belongng the UV-vsble spectrum range are located n x-axs, whereas n y-axs t can be observed the level of mportance of the relatonshp of the contamnant wth each of the wavelengths of the spectrum. Fnally, the color bar on the rght of the fgures allows to vsualze the number of tmes where n the Monte Carlo smulatons, fewer errors are generated n the predcton of a pollutant concentraton. Therefore, these last two elements determne the affnty, defnng ths as the ablty of the specfc propertes of the absorbance at multple wavelengths to be related to the presence of a contamnant n a water matrx. In the case of the TSS from the affluent (Fg. 3), there s a very close range of wavelengths (725 nm 750 nm) where the recurrence of random executons (selecton of 67 % of the nput data) s over 800. In ths fgure, the dust data represent the degree of affnty of some of the couples spectra-concentraton whch must be detected and defned as outlers. On the other sde, n the effluent (Fg. 4) t can be observed lower recurrence values n the dagonal for the vsble part of the spectrum (400 nm-750 nm) and the dust data are present n all the fgure, from whch can be nferred that a very mportant part of the dataset has low qualty. Notwthstandng, the recurrence and the levels of mportance are stll present n the vsble part of the spectrum, whch suggests that a physcal representaton of the spectroscopc process for some couples spectra-analyte stll exsts. Fgure 3: Rayo Affnty: recurrence, level of mportance and qualty of the data on the relatonshp spectrum-concentraton for nfluent-tss A smlar behavor observed for the TSS occurs also for the COD graphs for both nfluent and effluent. In Fgure 5 t can be observed how a greater number of wavelengths allow to valdate the presence of the pollutant n the sample, relatng absorbance values n the UV range, as well as n the Vsble. Ths allows to confrm the physcal nfluence of suspended solds on the UV range of the spectrum and wth ths, major fractons of organc carbon that demands dssolved or suspended oxygen, whch absorb lght n the Vsble range, and are related to the presence of COD (Vallant et al., 2002). However, t s mportant to remember that other major fractons of organc carbon present do not show absorpton n the UV-Vs spectrum, such as short chan fatty acds, sugars and starch and therefore t s not possble to quantfy the total COD by a spectrophotometrc method, as a standard laboratory analyss can do t (Ojeda and Rojas, 2009). In Fgure 6 t can be observed that a range of wavelengths could be assocated a pror wth COD, n a smlar way that the one observed n Fgure 4 (effluent-tss), but wth a lower recurrence. Ths shows that the data used have a low qualty and suggests a lack of affnty between the pollutant concentraton and the spectrum. It also could even suggest that the matrx of the treated wastewater compound n the effluent s less complex and therefore the organc or norganc substances that demand oxygen are less, then there should be a smaller number of wavelengths assocated wth the pollutant and wth a hgher recurrence. 5

6 Fgure 4: Rayo Affnty: recurrence, level of mportance and qualty of the data on the relatonshp spectrum-concentraton for effluent-tss Fgure 5: Rayo Affnty: recurrence, level of mportance and qualty of the data on the relatonshp spectrum-concentraton for nfluent-cod Fgure 6: Rayo Affnty: recurrence, level of mportance and qualty of the data on the relatonshp spectrum-concentraton for effluent-cod 6

7 Fnally, the fltered COD s the only pollutant analyzed for whch the nfluent data present qualty problems (Fg. 7), due to the fact that the presence of dust dspersed around the dagonal represents a lower degree of relatonshp between the absorbance and the concentraton values of the samples. For ths case t can be assumed that the data from the laboratory tests are responsble of the low affnty between the wavelengths and the CODf concentratons n the nfluent. However, the assumpton that the spectra do not represent the fngerprnt sample qualty s not vald from the vsual analyss, because when comparng the results obtaned for the same spectra related to COD and TSS concentratons, hgh recurrence values could be determned n ranges of more defned wavelengths (Fg. 3 and 4). It mght even be defned that the wrong quantfcaton of the CODf n the nfluent s lnked to a poor laboratory procedure or problems related to the elements used n the fltraton of the suspended materal, because after the 400 nm n the Vsble range, there are mportant recurrences and others of less magntude around the dagonal. Ths suggests an nfluence of suspended materal on the soluble part of the COD, whch should have been removed durng the fltraton procedure. What was stated before can be compared wth the CODf results obtaned for the effluent, where there are more recurrent wavelengths from the UV part of the spectrum, n a smlar range to that reported by Reger et al. (2004). Fgure 7: Rayo Affnty: recurrence, level of mportance and qualty of the data on the relatonshp spectrum-concentraton for nfluent-codf Fgure 8: Rayo Affnty: recurrence, level of mportance and qualty of the data on the relatonshp spectrum-concentraton for effluent-codf 7

8 CONCLUSIONS The method presented n ths paper can be useful to determne the degree of affnty between the absorbance values at sngle or multple wavelengths n the spectrum and the pollutant concentratons. Ths method allows to dentfy beforehand the qualty of the data and to establsh the degree of predctablty of the regressve models. On the other hand, the detecton of whch and how many wavelengths have a hgher affnty wth the pollutant wll reduce computaton tmes used n the predcton of concentratons when generatng Monte Carlo smulatons and cross valdaton processes. Fnally, t s mportant to mplement mage analyses methods to explot the nformaton presented n ths paper, as well as to compare the results wth other methods such as the correlaton coeffcent (R) or the Maxmal Informaton Coeffcent (MIC). Fnally, t s expected to consoldate the method and apply t to dfferent databases n other felds of knowledge. REFERENCES DFoggo, R. (2000). Gudelnes for applyng chemometrcs to spectra: feasblty and error propagaton. Appled Spectroscopy. Vol. 54(3). pp. 94A 114A. Escalas, A., Droguet, M., Guadayol, J. M., and Caxach, J. (2003). Estmatng DOC regme n a wastewater treatment plant by UV deconvoluton. Water Research. Vol. 37(11). pp Fleschmann, N., Langergraber, G., Wengartner, A., Hofstaedter, F., Nusch, S. and Maurer, P. On-lne and n-stu measurement of turbdty and COD n wastewater usng UV/VIS spectrometry. Proceedngs of the 2nd IWA World Water Congress. Berln, Germany Paper No. B1375. Gruber, G., Bertrand-Krajewsk, J.-L., De Benedts, J., Hochedlnger, M., and Lettl, W. (2006). Practcal aspects, experences and strateges by usng UV/VIS sensors for long-term sewer montorng. Water Practce and Technology. Vol. 1(1). pp Gruber, G., Wnkler, S., and Pressl, A. (2004). Quantfcaton of polluton loads from CSOs nto surface water bodes by means of onlne technques. Water Scence and Technology. Vol. 50(11). pp Hochedlnger, M. (2005). Assessment of combned sewer overflow emssons. PhD thess: Faculty of Cvl Engneerng, Unversty of Technology Graz (Austra), June 2005, 174 p. þ annexes. Hoppe, H., Messmann, S., Gga, A., and Grünng, H. (2009). Optons and lmts of quanttatve and qualtatve onlne-montorng of ndustral dscharges nto muncpal sewage systems. Water Scence and Technology. Vol. 60(4). pp Langergraber, G., Fleschmann, N, and Hofstädter, F. (2003). A multvarate calbraton procedure for UV/VIS spectrometrc quantfcaton of organc matter and ntrate n wastewater. Water Scence and Technology. Vol. 47(2). pp Langergraber, G., Wengartner, A., and Fleschmann, N (2004). Tme-resolved delta spectrometry: a method to defne alarm parameters from spectral data. Water Scence and Technology. Vol. 50(11). pp Langergraber, G., Fleschmann, N., Hofstaedter, F., y Wengartner, A. (2004b) Montorng of a paper mll wastewater treatment plant usng UV/VIS spectroscopy. Trends n Sustanable Producton. Vol. 49(1). pp Lorenz, U., Fleschmann, Nkolaus, and Dettmar, J. (2002). Adaptaton of a new onlne probe for qualtatve measurement to combned sewer systems (W. C. (eds) In: Strcker, E.W., Huber, ed.)., Ojeda, C. and Rojas, F. (2009). Process analytcal chemstry: applcatons of ultravolet/vsble spectrometry n envronmental analyss: an overvew. Appled Spectroscopy Revews. Vol. 44(3). pp R Development Core Team (2012). R: A language and envronment for statstcal computng. R Foundaton for Statstcal Computng, Venna, Austra. ISBN , URL Reger, L., Vanrolleghem, P. a, Langergraber, G., Kaeln, D., and Segrst, H. (2008). Long-term evaluaton of a spectral sensor for ntrte and ntrate. Water Scence and Technology. Vol. 57(10). pp Vallant, S., Pouet, M. and Thomas, O. (2002). Basc handlng of UV spectra for urban water qualty montorng. Urban Water. Vol. 4(3). pp

9 Vargas, A. and Butrón, G. (2006). On-lne concentraton measurements n wastewater usng nonlnear deconvoluton and partal least squares of spectrophotometrc data. Water Scence and Technology. Vol. 53(4-5). pp Wnkler, S., Saracevc, E., Bertrand-Krajewsk, J.-L., and Torres, A. (2008). Benefts, lmtatons and uncertanty of n stu spectrometry. Water Scence and Technology. Vol. 57(10). pp Authorzaton and Dsclamer Authors authorze LACCEI to publsh the paper n the conference proceedngs. Nether LACCEI nor the edtors are responsble ether for the content or for the mplcatons of what s expressed n the paper. 9