Quantitative image analysis as a robust tool to assess effluent quality from an aerobic granular sludge system treating industrial wastewater

Detalhes bibliográficos
Autor(a) principal: Costa, Joana Sofia Gomes
Data de Publicação: 2022
Outros Autores: Paulo, Ana M. S., Amorim, Catarina L., Amaral, A. Luís, Castro, Paula M. L., Ferreira, Eugénio C., Mesquita, D. P.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: https://hdl.handle.net/1822/75629
Resumo: Supplementary data to this article can be found online at https://doi.org/10.1016/j.chemosphere.2021.132773.
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spelling Quantitative image analysis as a robust tool to assess effluent quality from an aerobic granular sludge system treating industrial wastewaterAerobic granular sludgeQuantitative image analysisPartial least squaresSalinityEffluent quality parametersFood industry wastewaterSuspended and granular biomass fractionsCiências Naturais::Ciências BiológicasScience & TechnologySupplementary data to this article can be found online at https://doi.org/10.1016/j.chemosphere.2021.132773.Quantitative image analysis (QIA) is a simple and automated method for process monitoring, complementary to chemical analysis, that when coupled to mathematical modelling allows associating changes in the biomass to several operational parameters. The majority of the research regarding the use of QIA has been carried out using synthetic wastewater and applied to activated sludge systems, while there is still a lack of knowledge regarding the application of QIA in the monitoring of aerobic granular sludge (AGS) systems. In this work, chemical oxygen demand (COD), ammonium (NNH4+), nitrite (NNO2-), nitrate (NNO3-), salinity (Cl), and total suspended solids (TSS) levels present in the effluent of an AGS system treating fish canning wastewater were successfully associated to QIA data, from both suspended and granular biomass fractions by partial least squares models. The correlation between physical-chemical parameters and QIA data allowed obtaining good assessment results for COD (R2 of 0.94), NNH4+ (R2 of 0.98), NNO2- (R2 of 0.96), NNO3- (R2 of 0.95), Cl (R2 of 0.98), and TSS (R2 of 0.94). While the COD and NNO2- assessment models were mostly correlated to the granular fraction QIA data, the suspended fraction was highly relevant for NNH4+ assessment. The NNO3-, Cl and TSS assessment benefited from the use of both biomass fractions (suspended and granular) QIA data, indicating the importance of the balance between the suspended and granular fractions in AGS systems and its analysis. This study provides a complementary approach to assess effluent quality parameters which can improve wastewater treatment plants monitoring and control, with a more cost-effective and environmentally friendly procedure, while avoiding daily physical-chemical analysis.The authors thank the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding to the research units CEB (UIDB/04469/2020) and CBQF (UIDB/50016/2020) and the project AGeNT - PTDC/BTA-BTA/31264/2017 (POCI-01-0145-FEDER-031264). The authors wish to thank the company Aguas do Tejo Atlantico, S.A. for supplying the granules. Daniela P. Mesquita and Catarina L. Amorim thank FCT for funding through program DL 57/2016 – Norma transitoria.info:eu-repo/semantics/publishedVersionElsevierUniversidade do MinhoCosta, Joana Sofia GomesPaulo, Ana M. S.Amorim, Catarina L.Amaral, A. LuísCastro, Paula M. L.Ferreira, Eugénio C.Mesquita, D. P.2022-032022-03-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/75629engCosta, J. G., Paulo, A. M. S., Amorim, C. L., Amaral, A. L., Castro, P. M. L., Ferreira, E. C., & Mesquita, D. P. (2022, March). Quantitative image analysis as a robust tool to assess effluent quality from an aerobic granular sludge system treating industrial wastewater. Chemosphere. Elsevier BV. http://doi.org/10.1016/j.chemosphere.2021.1327730045-653510.1016/j.chemosphere.2021.13277334742770132773https://www.sciencedirect.com/science/article/abs/pii/S0045653521032458info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-04-12T04:55:01Zoai:repositorium.sdum.uminho.pt:1822/75629Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T15:47:11.456723Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Quantitative image analysis as a robust tool to assess effluent quality from an aerobic granular sludge system treating industrial wastewater
title Quantitative image analysis as a robust tool to assess effluent quality from an aerobic granular sludge system treating industrial wastewater
spellingShingle Quantitative image analysis as a robust tool to assess effluent quality from an aerobic granular sludge system treating industrial wastewater
Costa, Joana Sofia Gomes
Aerobic granular sludge
Quantitative image analysis
Partial least squares
Salinity
Effluent quality parameters
Food industry wastewater
Suspended and granular biomass fractions
Ciências Naturais::Ciências Biológicas
Science & Technology
title_short Quantitative image analysis as a robust tool to assess effluent quality from an aerobic granular sludge system treating industrial wastewater
title_full Quantitative image analysis as a robust tool to assess effluent quality from an aerobic granular sludge system treating industrial wastewater
title_fullStr Quantitative image analysis as a robust tool to assess effluent quality from an aerobic granular sludge system treating industrial wastewater
title_full_unstemmed Quantitative image analysis as a robust tool to assess effluent quality from an aerobic granular sludge system treating industrial wastewater
title_sort Quantitative image analysis as a robust tool to assess effluent quality from an aerobic granular sludge system treating industrial wastewater
author Costa, Joana Sofia Gomes
author_facet Costa, Joana Sofia Gomes
Paulo, Ana M. S.
Amorim, Catarina L.
Amaral, A. Luís
Castro, Paula M. L.
Ferreira, Eugénio C.
Mesquita, D. P.
author_role author
author2 Paulo, Ana M. S.
Amorim, Catarina L.
Amaral, A. Luís
Castro, Paula M. L.
Ferreira, Eugénio C.
Mesquita, D. P.
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Costa, Joana Sofia Gomes
Paulo, Ana M. S.
Amorim, Catarina L.
Amaral, A. Luís
Castro, Paula M. L.
Ferreira, Eugénio C.
Mesquita, D. P.
dc.subject.por.fl_str_mv Aerobic granular sludge
Quantitative image analysis
Partial least squares
Salinity
Effluent quality parameters
Food industry wastewater
Suspended and granular biomass fractions
Ciências Naturais::Ciências Biológicas
Science & Technology
topic Aerobic granular sludge
Quantitative image analysis
Partial least squares
Salinity
Effluent quality parameters
Food industry wastewater
Suspended and granular biomass fractions
Ciências Naturais::Ciências Biológicas
Science & Technology
description Supplementary data to this article can be found online at https://doi.org/10.1016/j.chemosphere.2021.132773.
publishDate 2022
dc.date.none.fl_str_mv 2022-03
2022-03-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://hdl.handle.net/1822/75629
url https://hdl.handle.net/1822/75629
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Costa, J. G., Paulo, A. M. S., Amorim, C. L., Amaral, A. L., Castro, P. M. L., Ferreira, E. C., & Mesquita, D. P. (2022, March). Quantitative image analysis as a robust tool to assess effluent quality from an aerobic granular sludge system treating industrial wastewater. Chemosphere. Elsevier BV. http://doi.org/10.1016/j.chemosphere.2021.132773
0045-6535
10.1016/j.chemosphere.2021.132773
34742770
132773
https://www.sciencedirect.com/science/article/abs/pii/S0045653521032458
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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