Wastewater Quality Estimation Through Spectrophotometry-Based Statistical Models

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 Pl...

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Main Authors: Daniel Carreres-Prieto, Juan T. García, Fernando Cerdán-Cartagena, Juan Suardiaz-Muro
Format: Article
Language:English
Published: MDPI AG 2020-10-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/20/19/5631
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spelling doaj-f230f20775a947e7afdc525c3e2c659a2020-11-25T03:42:59ZengMDPI AGSensors1424-82202020-10-01205631563110.3390/s20195631Wastewater Quality Estimation Through Spectrophotometry-Based Statistical ModelsDaniel Carreres-Prieto0Juan T. García1Fernando Cerdán-Cartagena2Juan Suardiaz-Muro3Department of Mining and Civil Engineering, Universidad Politécnica de Cartagena, 30202 Cartagena, SpainDepartment of Mining and Civil Engineering, Universidad Politécnica de Cartagena, 30202 Cartagena, SpainDepartment of Information and Communications Technologies, Universidad Politécnica de Cartagena, 30202 Cartagena, SpainDepartment of Electronic Technology, Universidad Politécnica de Cartagena, 30202 Cartagena, SpainLocal 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, BOD<sub>5</sub>, TSS, P, TN and NO<sub>3</sub><sup>−</sup>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 NO<sub>3</sub><sup>−</sup>N), including both raw and treated wastewater, with an error rate below 4%. In the case of the multilinear regression models, these are limited to raw water and the estimate is limited to COD and TSS, with less than a 0.5% error rate.https://www.mdpi.com/1424-8220/20/19/5631LED spectrophotometerwastewater pollutant characterizationorganic mattersuspended solidsnutrients
collection DOAJ
language English
format Article
sources DOAJ
author Daniel Carreres-Prieto
Juan T. García
Fernando Cerdán-Cartagena
Juan Suardiaz-Muro
spellingShingle Daniel Carreres-Prieto
Juan T. García
Fernando Cerdán-Cartagena
Juan Suardiaz-Muro
Wastewater Quality Estimation Through Spectrophotometry-Based Statistical Models
Sensors
LED spectrophotometer
wastewater pollutant characterization
organic matter
suspended solids
nutrients
author_facet Daniel Carreres-Prieto
Juan T. García
Fernando Cerdán-Cartagena
Juan Suardiaz-Muro
author_sort Daniel Carreres-Prieto
title Wastewater Quality Estimation Through Spectrophotometry-Based Statistical Models
title_short Wastewater Quality Estimation Through Spectrophotometry-Based Statistical Models
title_full Wastewater Quality Estimation Through Spectrophotometry-Based Statistical Models
title_fullStr Wastewater Quality Estimation Through Spectrophotometry-Based Statistical Models
title_full_unstemmed Wastewater Quality Estimation Through Spectrophotometry-Based Statistical Models
title_sort wastewater quality estimation through spectrophotometry-based statistical models
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2020-10-01
description 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, BOD<sub>5</sub>, TSS, P, TN and NO<sub>3</sub><sup>−</sup>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 NO<sub>3</sub><sup>−</sup>N), including both raw and treated wastewater, with an error rate below 4%. In the case of the multilinear regression models, these are limited to raw water and the estimate is limited to COD and TSS, with less than a 0.5% error rate.
topic LED spectrophotometer
wastewater pollutant characterization
organic matter
suspended solids
nutrients
url https://www.mdpi.com/1424-8220/20/19/5631
work_keys_str_mv AT danielcarreresprieto wastewaterqualityestimationthroughspectrophotometrybasedstatisticalmodels
AT juantgarcia wastewaterqualityestimationthroughspectrophotometrybasedstatisticalmodels
AT fernandocerdancartagena wastewaterqualityestimationthroughspectrophotometrybasedstatisticalmodels
AT juansuardiazmuro wastewaterqualityestimationthroughspectrophotometrybasedstatisticalmodels
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