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Interpretable machine learning for predicting the fate and transport of pentachlorophenol in groundwater
SMHI, Research Department, Hydrology.ORCID iD: 0000-0001-8363-8672
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2024 (English)In: Environmental Pollution, ISSN 0269-7491, E-ISSN 1873-6424, Vol. 345, article id 123449Article in journal (Refereed) Published
Place, publisher, year, edition, pages
2024. Vol. 345, article id 123449
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Oceanography, Hydrology and Water Resources
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Hydrology
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URN: urn:nbn:se:smhi:diva-6597DOI: 10.1016/j.envpol.2024.123449ISI: 001175351200001PubMedID: 38278404OAI: oai:DiVA.org:smhi-6597DiVA, id: diva2:1848211
Available from: 2024-04-02 Created: 2024-04-02 Last updated: 2025-06-12Bibliographically approved

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Interpretable machine learning for predicting the fate and transport of pentachlorophenol in groundwater(12823 kB)4454 downloads
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McKnight, Ursula

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