Publicação
Geochemical contamination signatures: Insights from information theory and cokriging— a compositional approach
| datacite.subject.fos | Ciências Naturais | |
| datacite.subject.sdg | 13:Ação Climática | |
| datacite.subject.sdg | 15:Proteger a Vida Terrestre | |
| dc.contributor.author | Pazo, María | |
| dc.contributor.author | Albuquerque, M.T.D. | |
| dc.contributor.author | Roque, Natália | |
| dc.contributor.author | Rita Fonseca | |
| dc.date.accessioned | 2026-04-15T16:02:37Z | |
| dc.date.available | 2026-04-15T16:02:37Z | |
| dc.date.issued | 2026 | |
| dc.description.abstract | Potentially toxic elements (PTEs) such as arsenic (As) and mercury (Hg) are among the most critical pollutants globally, threatening ecosystem integrity and human health. The Trimpancho mining system in the Iberian Pyrite Belt (W Spain) is one such hotspot, where centuries of activity have left a legacy of acid mine drainage and heavy metal dispersion. This study employs an integrated compositional, probabilistic, and spatial modeling framework to characterize and map contamination dynamics in this area with quantified uncertainty. A total of 31 water samples were collected during 2022 and 2023 from surface streams and tributaries. Concentration data were transformed using isometric log-ratio (ilr) techniques to preserve their compositional nature and avoid spurious correlations. Bayesian Networks (BNs), combined with information-theoretic metrics, were then applied to identify latent geochemical contamination patterns and quantify both aleatory and epistemic uncertainties. The key drivers identified were incorporated into a co-kriging framework, enabling spatial interpolation that accounted for over 90% of total variance and reduced epistemic uncertainty by 22.7% compared to raw-data models. The resulting spatial–temporal maps revealed distinct As– Hg contamination signatures, influenced by hydrological variability and mining legacy sources. In conclusion, this integrated approach provides a robust, uncertainty-aware methodology for detecting, interpreting, and mapping contamination patterns, offering actionable insights for environmental risk assessment and remediation planning in mining-impacted watersheds. | eng |
| dc.identifier.citation | PAZO, M. [et al.] (2026) - Geochemical contamination signatures: Insights from information theory and cokriging—a compositional approach. Water Air Soil Pollut. 237, 709. DOI: 10.1007/s11270-026-09389-1 | |
| dc.identifier.doi | 10.1007/s11270-026-09389-1 | |
| dc.identifier.uri | http://hdl.handle.net/10400.11/10841 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | Springer | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Pyritic Belt · Bayesian network · CoDA · Spatial modeling · Stream sediment | |
| dc.subject | Bayesian network | |
| dc.subject | CoDA | |
| dc.subject | Spatial modeling | |
| dc.subject | Uncertainty | |
| dc.title | Geochemical contamination signatures: Insights from information theory and cokriging— a compositional approach | eng |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 709 | |
| oaire.citation.startPage | 237 | |
| oaire.citation.title | Water Air Soil Pollution | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| person.familyName | Albuquerque | |
| person.familyName | Martins Roque | |
| person.givenName | Maria Teresa | |
| person.givenName | Natália | |
| person.identifier | Natália Martins Roque | |
| person.identifier.ciencia-id | 5A1C-8956-4C0A | |
| person.identifier.ciencia-id | 451A-332D-0798 | |
| person.identifier.orcid | 0000-0002-8782-6133 | |
| person.identifier.orcid | 0000-0001-8859-4365 | |
| person.identifier.rid | B-1536-2013 | |
| person.identifier.scopus-author-id | 55507421600 | |
| person.identifier.scopus-author-id | 57200227653 | |
| relation.isAuthorOfPublication | e2c2d171-e148-4c23-9cf8-0eb6d810c15e | |
| relation.isAuthorOfPublication | faca1743-7a98-4404-acef-ab73a684d3c2 | |
| relation.isAuthorOfPublication.latestForDiscovery | e2c2d171-e148-4c23-9cf8-0eb6d810c15e |