Are bottled mineral waters and groundwater for human supply different?

[EN] Increasingly, bottled natural mineral water (NMW) is proposed as a healthy and safe alternative to supply water. However, tap supply water often comes from aquifers (TGW), even from the same aquifers as NMW, sharing the exact formation mechanisms and mineralization processes. Therefore, it is h...

ver descrição completa

Detalhes bibliográficos
Autores: Moreno Merino, Luis, Aguilera Alonso, Héctor, Losa Román, Almudena de la
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/377175
Acesso em linha:http://hdl.handle.net/10261/377175
https://api.elsevier.com/content/abstract/scopus_id/85129243393
Access Level:acceso abierto
Palavra-chave:Machine learning
Bottled mineral water
Explainable AI
Groundwater
Hydrochemistry
http://metadata.un.org/sdg/6
Ensure availability and sustainable management of water and sanitation for all
id ES_f40fca339c727a11ba330e99df2be5dc
oai_identifier_str oai:digital.csic.es:10261/377175
network_acronym_str ES
network_name_str España
repository_id_str
spelling Are bottled mineral waters and groundwater for human supply different?Moreno Merino, LuisAguilera Alonso, HéctorLosa Román, Almudena de laMachine learningBottled mineral waterExplainable AIGroundwaterHydrochemistryhttp://metadata.un.org/sdg/6Ensure availability and sustainable management of water and sanitation for all[EN] Increasingly, bottled natural mineral water (NMW) is proposed as a healthy and safe alternative to supply water. However, tap supply water often comes from aquifers (TGW), even from the same aquifers as NMW, sharing the exact formation mechanisms and mineralization processes. Therefore, it is hypothesized that NMW and TGW cannot be distinguished. The chemical composition of TGW and NMW samples in Spain has been compared using five criteria: expert judgment, hydrochemistry, legal regulations, statistical analysis, and machine learning (ML). Hydrochemical criteria included all the NMW samples in the TGW group, as did the legal criterion, whereas classical statistical analysis could not find significant differences between the two groups. Although experts could correctly differentiate a small subsample of both types of water with an accuracy of 0.67, ML-based classification with Extreme Gradient Boosting yielded a balanced accuracy of 0.92 on an extremely imbalanced data set. Shapley Additive Explanations (SHAP) analysis identified pH, SiO2, E, K+, Ca2+, K+/Na+ and NO3- as the most relevant variables for water type discrimination. The overall consistency and generalization ability of the ML classifier has been proven by the spatial distribution of hits and misses, where the few cases of indistinguishable waters seem to be related to proximity to nature reserves (i.e., land use) more than to geological characteristics. Therefore, it can be concluded that NMW and TGW are indeed different and that only ML could find the hidden structure in the chemical data that determines the differences. This structure originates in how the market and consumers decide which water is ultimately bottled. The results can help on future choices of TGW and NMW in a context of water scarcity.This work is part of the activities of the IGME-2896 project funded by the YEI (Youth Employment Initiative) and the European Social Fund (ESF) through the National System of Youth Guarantee (PEJ2018-002477) supported by the Spanish Ministry of Science and Innovation.Peer reviewedElsevierEuropean CommissionMinisterio de Ciencia e Innovación (España)Moreno Merino, Luis [0000-0002-9984-6035]Aguilera Alonso, Héctor [0000-0001-9046-5837]Losa Román, Almudena de la [0000-0002-2493-7098]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252022info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/377175https://api.elsevier.com/content/abstract/scopus_id/85129243393reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/EC//PEJ2018-002477https://doi.org/10.1016/j.scitotenv.2022.155554Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3771752026-05-22T06:33:51Z
dc.title.none.fl_str_mv Are bottled mineral waters and groundwater for human supply different?
title Are bottled mineral waters and groundwater for human supply different?
spellingShingle Are bottled mineral waters and groundwater for human supply different?
Moreno Merino, Luis
Machine learning
Bottled mineral water
Explainable AI
Groundwater
Hydrochemistry
http://metadata.un.org/sdg/6
Ensure availability and sustainable management of water and sanitation for all
title_short Are bottled mineral waters and groundwater for human supply different?
title_full Are bottled mineral waters and groundwater for human supply different?
title_fullStr Are bottled mineral waters and groundwater for human supply different?
title_full_unstemmed Are bottled mineral waters and groundwater for human supply different?
title_sort Are bottled mineral waters and groundwater for human supply different?
dc.creator.none.fl_str_mv Moreno Merino, Luis
Aguilera Alonso, Héctor
Losa Román, Almudena de la
author Moreno Merino, Luis
author_facet Moreno Merino, Luis
Aguilera Alonso, Héctor
Losa Román, Almudena de la
author_role author
author2 Aguilera Alonso, Héctor
Losa Román, Almudena de la
author2_role author
author
dc.contributor.none.fl_str_mv European Commission
Ministerio de Ciencia e Innovación (España)
Moreno Merino, Luis [0000-0002-9984-6035]
Aguilera Alonso, Héctor [0000-0001-9046-5837]
Losa Román, Almudena de la [0000-0002-2493-7098]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Machine learning
Bottled mineral water
Explainable AI
Groundwater
Hydrochemistry
http://metadata.un.org/sdg/6
Ensure availability and sustainable management of water and sanitation for all
topic Machine learning
Bottled mineral water
Explainable AI
Groundwater
Hydrochemistry
http://metadata.un.org/sdg/6
Ensure availability and sustainable management of water and sanitation for all
description [EN] Increasingly, bottled natural mineral water (NMW) is proposed as a healthy and safe alternative to supply water. However, tap supply water often comes from aquifers (TGW), even from the same aquifers as NMW, sharing the exact formation mechanisms and mineralization processes. Therefore, it is hypothesized that NMW and TGW cannot be distinguished. The chemical composition of TGW and NMW samples in Spain has been compared using five criteria: expert judgment, hydrochemistry, legal regulations, statistical analysis, and machine learning (ML). Hydrochemical criteria included all the NMW samples in the TGW group, as did the legal criterion, whereas classical statistical analysis could not find significant differences between the two groups. Although experts could correctly differentiate a small subsample of both types of water with an accuracy of 0.67, ML-based classification with Extreme Gradient Boosting yielded a balanced accuracy of 0.92 on an extremely imbalanced data set. Shapley Additive Explanations (SHAP) analysis identified pH, SiO2, E, K+, Ca2+, K+/Na+ and NO3- as the most relevant variables for water type discrimination. The overall consistency and generalization ability of the ML classifier has been proven by the spatial distribution of hits and misses, where the few cases of indistinguishable waters seem to be related to proximity to nature reserves (i.e., land use) more than to geological characteristics. Therefore, it can be concluded that NMW and TGW are indeed different and that only ML could find the hidden structure in the chemical data that determines the differences. This structure originates in how the market and consumers decide which water is ultimately bottled. The results can help on future choices of TGW and NMW in a context of water scarcity.
publishDate 2022
dc.date.none.fl_str_mv 2022
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/377175
https://api.elsevier.com/content/abstract/scopus_id/85129243393
url http://hdl.handle.net/10261/377175
https://api.elsevier.com/content/abstract/scopus_id/85129243393
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/EC//PEJ2018-002477
https://doi.org/10.1016/j.scitotenv.2022.155554
Sí
dc.rights.none.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:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869424436460912640
score 15,811543

Registos relacionados