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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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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