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.


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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.
Published: 2017-05-05
Environmental Engineering