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

Abstract

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. 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 tendency 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.