Estimación Espectral de Series de Tiempo de Absorbancia Uv-Vis para el Monitoreo de Calidad de Aguas

Spectral Estimation of UV-Vis Absorbance Time Series for Water Quality Monitoring

  • Leonardo Plazas-Nossa Universidad Distrital Francisco José de Caldas.
  • Miguel Antonio Ávila Angulo Universidad Distrital Francisco José de Caldas.
  • Andres Torres Pontificia Universidad Javeriana Bogotá.
Keywords: Box-Cox Transformation, Periodogram, Principal Components Analysis, Power spectral density, Stationarity, UV-Vis sensor (en_US)
Keywords: Análisis por Componentes Principales, Captor UV-Vis, Densidad espectral de potencia, Estacionariedad, Periodograma, Transformación Box-Cox. (es_ES)

Abstract (es_ES)

Contexto: las señales registradas como de series de tiempo por sensores de espectrometría UV-Vis en diferen-tes instalaciones de saneamiento urbano, pueden ser no estacionarias, lo cual complica el análisis del monito-reo de la calidad del agua. Este trabajo propone realizar la estimación espectral aplicando la transformación de Box-Cox y la diferenciación con el fin de obtener series de tiempo estacionarias en sentido amplio y reducir su dimensionalidad con base en el análisis de componentes principales (PCA, por sus siglas en inglés).


Método: la metodología se aplica a series de tiempo de absorbancia UV-Vis para tres sitios de estudio en Co-lombia: (i) Planta para el tratamiento de agua residual (PTAR) El-Salitre (Bogotá); (ii) Estación de Bombeo Gi-braltar (EBG) (Bogotá); y (iii) Planta para el tratamiento de agua residual (PTAR) San-Fernando (Itagüí). Las series de tiempo de absorbancia UV-Vis se registran con igual tamaño de 5705 muestras. Se realiza la estimación de la densidad espectral mediante el promedio de periodogramas modificados con ventana rectangular con un traslape del 50%, utilizando en el procedimiento DFT (Discrete Fourier Transform) e IFFT (Inverse Fast Fourier Transform) con los veinte armónicos más relevantes.


Resultados: al reducir la dimensionalidad de las series de tiempo de absorbancia con PCA, se obtienen para cada sitio de estudio seis, ocho y siete componentes principales respectivamente, explicando en conjunto más del 97% de la variabilidad. Para los tres sitios de estudio, se obtuvieron valores de diferencias inferiores al 30% para el rango UV, mientras que para el rango visible se obtiene máximas diferencias de: (i) 35% para El-Salitre; (ii) 61% para EBG; y (iii) 75% para San-Fernando.


Conclusiones: con la transformación de Box-Cox y el proceso de diferenciación aplicado a tres series de tiempo de absorbancia UV-Vis para los sitios propuestos de estudio, se logra reducir la varianza y se elimina la tenden-cia de las series de tiempo. Se recomienda realizar un pre-procesamiento de las series de tiempo de absorban-cia UV-Vis para detectar y remover los valores extremos y posteriormente aplicar el proceso propuesto para la estimación espectral.

Idioma: Español

 

Abstract (en_US)

Context: Signals recorded as multivariate time series by UV-Vis absorbance captors installed in urban sewer systems, can be non-stationary, yielding complications in the analysis of water quality monitoring. This work proposes to perform spectral estimation using the Box-Cox transformation and differentiation in order to obtain stationary multivariate time series in a wide sense. Additionally, Principal Component Analysis (PCA) is applied to reduce their dimensionality.


Method: Three different UV-Vis absorbance time series for different Colombian locations were studied: (i) El-Salitre Wastewater Treatment Plant (WWTP) in Bogotá; (ii) Gibraltar Pumping Station (GPS) in Bogotá; and (iii) San-Fernando WWTP in Itagüí. Each UV-Vis absorbance time series had equal sample number (5705). The esti-mation of the spectral power density is obtained using the average of modified periodograms with rectangular window and an overlap of 50%, with the 20 most important harmonics from the Discrete Fourier Transform (DFT) and Inverse Fast Fourier Transform (IFFT).


Results: Absorbance time series dimensionality reduction using PCA, resulted in 6, 8 and 7 principal components for each study site respectively, altogether explaining more than 97% of their variability. Values of differences below 30% for the UV range were obtained for the three study sites, while for the visible range the maximum differences obtained were: (i) 35% for El-Salitre WWTP; (ii) 61% for GPS; and (iii) 75% for San-Fernando WWTP.


Conclusions: The Box-Cox transformation and the differentiation process applied to the UV-Vis absorbance time series for the study sites (El-Salitre, GPS and San-Fernando), allowed to reduce variance and to eliminate ten-dency of the time series. A pre-processing of UV-Vis absorbance time series is recommended to detect and remove outliers and then apply the proposed process for spectral estimation.

Language: Spanish.

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Author Biographies

Leonardo Plazas-Nossa, Universidad Distrital Francisco José de Caldas.
Ingeniero Electrónico, Magister en Teleinformática, Docente Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas.
Miguel Antonio Ávila Angulo, Universidad Distrital Francisco José de Caldas.
Ingeniero Catastral y Geodesta, Magister en Teleinformática, Docente Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas.
Andres Torres, Pontificia Universidad Javeriana Bogotá.
Ingeniero Civil, Especialización en Sistemas Gerenciales de Ingeniería, Maestría en Ingeniería Civil, Doctorado en Ingeniería Civil, Grupo de Investigación Ciencia e Ingeniería del Agua y el Ambiente, Facultad de Ingeniería, Pontificia Universidad Javeriana.

References

Poch, M., Cortés, U., Comas, J., Rodríguez-Roda, I. and Sànchez-Marrè, M., “Decisions on urban water systems: some support”. University of Girona, First Edition, Servei de Publicacions, Spain 2012.

Garcia, X., Barceló, D., Comas, J., Hadjimichael, A., Page, T. and Acuña, V., “Placing ecosystem services at the heart of urban water systems management”. Science of the Total Environment 563–564, 2016, pp. 1078–1085.

Morales-Torres, A., Escuder-Bueno, I., Andrés-Doménech, I. and Perales-Momparler, S., “Decision Support Tool for energy-efficient, sustainable and integrated urban stormwater management”. Environmental Modelling & Software 84, 2016, pp. 518-528.

Akan, A. and Houghtalen, R., Urban Hydrology, Hydraulics, and Stormwater Quality: Engineering applications and computer modelling. John Wiley &Sons, Inc. Hoboken, USA. 2003.

Dirckx, G, van Daele, S. and Hellinck, N., “Groundwater Infiltration Potential (GWIP) as an aid to determining the cause of dilution of waste water”. Journal of Hydrology, 542, 2016, pp. 474-486.

Marsalek, J., Jiménez-Cisneros, B., Malmquist, P.-A., Karamouz, M., Goldenfum, J. and Chocat, B., “Urban water cycle processes and interactions”. Pub-lished by the International Hydrological Programme (IHP) of the United Nations Educational, Scientific and Cultural Organization (UNESCO). Paris 2006.

Gosset, A., Ferro, Y. and Durrieu, C., “Methods for evaluating the pollution impact of urban wet weather discharges on biocenosis: a review”. Water Research, 89, 2016, pp. 330–354.

Palla, A. and Gnecco, I., “Hydrologic modelling of Low Impact Development systems at the urban catchment scale”. Journal of Hydrology, 528, 2015, pp. 361-368.

Fazi, S., Bandla, A., Pizzetti, I. and Swarup, S., “Microbial biofilms as one of the key elements in modulating ecohydrological processes in both natural and urban water corridors”. Ecohydrology & Hydrobiology 16, 2016, pp. 33-38.

von Sperling, M. and de Lemos., C., Wastewater Characteristics, Treatment and Disposal. First Edition. IWA Publishing. UK 2007.

Francois, C., Gondran, N., Nicolas, J.-P. and Parsons, D. “Environmental assessment of urban mobility: Combing life cycle assessment with land-use and transport interaction modelling-Application to Lyon (France)”. Ecological Indicators, 72, 2017, pp. 597-604.

van den Broeke, J., “On-line and In-situ UV/Vis Spectroscopy: Real time multi parameter measurements with a single instrument”. AWE International, 2007, pp. 55-59.

Gruber, G., Bertrand-Krajewski, J.-L., De Beneditis, J., Hochedlinger, M., & Lettl, W., “Practical aspects, experiences and strategies by using UVVIS sensors for long-term sewer monitoring”. Water Practice & Technology, 2006, pp. 1-8.

Rieger, L., Langergraber, G., Thomann, M., Fleischmann, N., and Siegrist, H. “Spectral in-situ analysis of NO2, NO3, COD, DOC and TSS in the effluent of a WWTP”. Proccedings of AutMoNet – 2nd IWA Conference on Automation in Water Quality Monitoring, Vienna, 2004, pp. 29-36.

Matsumoto, T. and Sánchez, I. "Desempeño de la Planta de Tratamiento de Aguas Residuales de São João de Iracema (Brasil)”. Revista Ingeniería 21(2), 2016, pp. 176-186.

Salgado, R., Pinheiro, H. M., Ferreira, F., Saldanha, J., & Louren, N. “In situ UV-Vis spectroscopy to estimate COD and TSS in wastewater drainage sys-tems”. Urban Water Journal, 2013, pp. 1-12.

Torres, A., Lepot, M., & Bertrand-Krajewski, J.-L. “Local calibration for a UV/Vis spectrometer: PLS vs. SVM. A case study in a WWTP”. Proccedings of 7th International Conference on Sewer Processes & Networks, 28 - 30 August – 2013, Sheffield, UK.

Lepot M., Torres A., Hofer T., Caradot N., Gruber G., Aubin J.-B., Bertrand-Krajewski J.-L. “Calibration of UV/Vis spectrophotometers: A review and comparison of different methods to estimate TSS and total and dissolved COD concentrations in sewers, WWTPs and rivers”. Water Research 101, 2016, pp. 519–534.

Fei, W., & Bai, L. “Auto-Regressive Models of Non-stationary time series with finite length”. Tsinghua Science and Technology 10(2), 2005, pp. 162-168.

Kamarzarrin, M., Hosseini, S., Mehdi, M. and Kamarzarrin, M. “Designing and implementing of improved cryptographic algorithm using modular arith-metic theory”. Journal of Electrical Systems and Information Technology, 2, 2015, pp. 14–17.

Plazas-Nossa, L., Avila, M. and Torres, Andres. "Detection of Outliers and Imputing of Missing Values for Water Quality UV-VIS Absorbance Time Series”. Revista Ingeniería, 2016, In press.

Salcedo, G., Porto, R., and Morettin, P. “Comparing non-stationary and irregularly spaced time series”. Computational Statistics and Data Analysis 56, 2012, pp. 3921-3934.

Huang, J., Kobayashi, M., and McAleer, M. “Testing for the Box–Cox parameter for an integrated process”. Mathematics and Computers in Simulation, 83, 2012, pp. 1-9.

Bicego, M. and Baldo, S. "Properties of the Box–Cox transformation for pattern classification”. Neurocomputing, 218, 2016, pp. 390-400.

Tsiotas, G. “On the use of the Box–Cox transformation on conditional variance models”. Finance Research Letters, 4, 2007, pp. 28-32.

Proietti, T., and Lütkepohl, H. “Does the Box–Cox transformation help in forecasting macroeconomic time series?”. International Journal of Forecasting 29, 2013, pp. 88-99.

Langergraber, G., Fleischmann, N., Hofstaedter, F., & Weingartner, A. “Monitoring of a paper mill wastewater treatment plant using UV/VIS spectros-copy”. IWA Water Science and Technology, 49(1), 2004, pp. 9-14.

s::can. “Manual ana::pro Version 5.3 September 2006 Release”. Messtechnik GmbH, Vienna, Austria 2006.

Zamora, D., Métodos Machine Learning aplicados para estimar la concentración de los contaminantes de la DQO y de los SST en hidrosistemas de saneamiento urbano a partir de espectrometría UV-Visible. Tesis de Maestría, 2013, Pontificia Universidad Javeriana, Bogotá-Colombia.

Winkler, S., Saracevic, E., Bertrand-Krajewski, J.-L. and Torres, A., “Benefits, limitations and uncertainty of in situ spectrometry”. Water science and technology 57(10), 2008, pp. 1651–8.

Harjula, I., Hekkala, A., Matinmikko, M., and Mustonen, M., “Performance Evaluation of Spectrum Sensing Using Welch Periodogram for OFDM Signals”. IEEE 73rd Vehicular Technology Conference (VTC Spring), 2011, pp. 1-5.

Zhang, S., "Adaptive spectral estimation for nonstationary multivariate time series”. Computational Statistics and Data Analysis 103, 2016, pp. 330–349.

Chong, T.-L., “Estimating the differencing parameter via the partial autocorrelation function”. Journal of Econometrics, 97, 2000, pp. 365-381.

Hassler, U., “Persistence under temporal aggregation and differencing”. Economics Letters, 124, 2014, pp. 318-322.

Shen, Ch. “A comparison of principal components using TPCA and nonstationary principal component analysis on daily air-pollutant concentration series”. Physica A, 467, 2017, pp. 453-464.

Stavropoulos, C., and Fassois, S., “Non-stationary functional series modeling and analysis of hardware reliability series: a comparative study using rail vehicle interfailure times”. Reliability Engineering and System Safety, 68, 2000, pp. 169-183.

Shlens, J., “A Tutorial on Principal Component Analysis”. La Jolla, California, USA: Salk Institute for Biological Studies, 2009, pp. 1-13.

Barbour, A., and Parker, R., “psd: Adaptive,sine multitaper power spectral density estimation for R”. Computers and Geosciences, 63, 2014, pp. 1-8.

Bogdan,I. and Istrate, C. “The analysis of the principal components of the financial reporting in the case of Romanian listed companies”. Procedia Economics and Finance, 20, 2015, pp. 553–561.

Plazas-Nossa, L., Ávila, M. and Moncada, G. “Estimación del Exponente de Hurst y Dimensión Fractal para el análisis de series de tiempo de Absorbancia UV-VIS”. Ciencia e Ingeniería Neogranadina, 42(2), 2014, pp. 133-143.

Lee, D., and Baldick, R., “Future Wind Power Scenario Synthesis Through Power Spectral Density Analysis”. IEEE Transactions on Smart Grid, 5(1), 2014, pp. 490-500.

Bach, F. and Jordan, M., “Learning Graphical Models for Stationary Time Series”. IEEE Transactions on Signal Processing, 52(8), 2004, pp. 2189-2199.

Lv, P., and Yue, L., “Short-Term Wind Speed Forecasting Based on Non-stationary Time Series analysis and ARCH model”. Proccedings of International Conference on Multimedia Technology (ICMT), Hangzhou, China, 2011, pp. 2549-2553.

Proakis, J., and Manolakis, D. “Digital signal processing principles, algorithms, and applications”. Fourth Edition. Pearson Prentice Hall, New Jersey, USA 2007.

Tuffner, F., Pierre, J., and Kubichek, R. “Computationally Efficient Updating of a Weighted Welch Periodogram for Nonstationary Signals”. 51st Midwest Symposium on Circuits and Systems MWSCAS-2008, pp. 799-802.

Diebold, F., “Elements of Forecasting”. Second Edition. Thomson/South Western Publishing an ITP Company. Department of Economics University of Pennsylvania, 2001.

Gujarati, D. and Porter, D., “Basic Econometrics”. 5th Edition. McGraw-Hill Higer Education/Irwin. New York-USA. 2008.

How to Cite
Plazas-Nossa, L., Ávila Angulo, M. A., & Torres, A. (2017). Spectral Estimation of UV-Vis Absorbance Time Series for Water Quality Monitoring. Ingeniería, 22(2), 211-225. https://doi.org/10.14483/udistrital.jour.reving.2017.2.a03
Published: 2017-05-05
Section
Environmental Engineering