Identification of water pollution sources in wastewater collecting systems using electromagnetic spectra in the UV -VIS range

نویسنده

  • João Pedro Ferreira de Almeida
چکیده

In the context of urban drainage, the protection of surface waters from the effects of wastewater discharges has assumed increasing importance. As a result of human activity, there is sometimes contamination of water resources due not only to the phenomenon of diffuse pollution, but also as a result of specific effluent discharges (such as those from different industries or farming and by some discharges of insufficiently treated urban wastewater. Currently, wastewater quality evaluation is based on physicochemical and microbiological parameters (contaminants indicators). However, these parameters do not allow the identification of the source of contamination in the effluents. On the other hand, these tests are time-consuming analysis and do not provide real-time results, therefore they cannot be used as a warning system in bathing areas or at the entrance of a wastewater treatment plant (WWTP). The present study evaluates whether the principal component analysis (PCA) applied to the spectra in the range Ultraviolet-Visible (UV-Vis) allows the real time identification of wastewater quality variations or types, in particular due to the increase of organic load. The results show that the UV-Vis spectra obtained from wastewater samples includes information that can be used to monitor wastewater quality variations in urban drainage systems. The application of PCA presents the potential to monitor wastewater quality using UV-Vis range spectra, since this approach allows identification of anomalies or changes in effluent quality matrix those results from the discharge of industrial effluents. The suitability of the model for PCA anomaly detection has been demonstrated for two types of industrial effluents. From the results obtained it is believed that, if the spectra of known industrial effluents are included in the PCA model, the scores obtained in this model can be used to identify, in real time, this type of discharges.

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تاریخ انتشار 2016