Wastewater quality estimation through spectrophotometry-based statistical models
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AuthorCarreres Prieto, Daniel; García Bermejo, Juan Tomás; Cerdán Cartagena, José Fernando; Suardíaz Muro, Juan
Knowledge AreaTecnología Electrónica
SponsorsThe authors wish to thank the financial support received from the Seneca Foundation of the Región de Murcia (Spain) through the program devoted to training novel researchers in areas of specific interest for the industry and with a high capacity to transfer the results of the research generated, entitled: “Subprograma Regional de Contratos de Formación de Personal Investigador en Universidades y OPIs” (Mod. B, Ref. 20320/FPI/17).
Bibliographic CitationCarreres-Prieto, D.; García, J.T.; Cerdán-Cartagena, F.; Suardiaz-Muro, J. Wastewater Quality Estimation through Spectrophotometry-Based Statistical Models. Sensors 2020, 20, 5631. https://doi.org/10.3390/s20195631
Wastewater pollutant characterization
Local administrations are increasingly demanding real-time continuous monitoring of pollution in the sanitation system to improve and optimize its operation, to comply with EU environmental policies and to reach European Green Deal targets. The present work shows a full-scale Wastewater Treatment Plant field-sampling campaign to estimate COD, BOD5, TSS, P, TN and NO3-N in both influent and effluent, in the absence of pre-treatment or chemicals addition to the samples, resulting in a reduction of the duration and cost of analysis. Different regression models were developed to estimate the pollution load of sewage systems from the spectral response of wastewater samples measured at 380-700 nm through multivariate linear regressions and machine learning genetic algorithms. The tests carried out concluded that the models calculated by means of genetic algorithms can estimate the levels of five of the pollutants under study (COD, BOD5, TSS, TN and NO3-N), including both raw and treated ...
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