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A unified geostatistical machine learning framework for predicting and attributing arsenic contamination in southwestern Ghana.

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Title: A unified geostatistical machine learning framework for predicting and attributing arsenic contamination in southwestern Ghana.
Authors: Bosson-Amedenu, Senyefia1 (AUTHOR) senyefia.bosson-amedenu@ttu.edu.gh, Ayitey, Emmanuel1 (AUTHOR) emmanuel.ayitey@ttu.edu.gh, Borbor, Bridget Sena2 (AUTHOR) bsborbor@st.umat.edu.gh
Source: Discover Environment. 5/21/2026, Vol. 4 Issue 1, p1-48. 48p.
Database: Environment Complete
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Header DbId: eih
DbLabel: Environment Complete
An: 193949899
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PubType: Academic Journal
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  Data: <searchLink fieldCode="JN" term="%22Discover+Environment%22">Discover Environment</searchLink>. 5/21/2026, Vol. 4 Issue 1, p1-48. 48p.
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1007/s44274-026-00754-9
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      – Code: eng
        Text: English
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        PageCount: 48
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      – TitleFull: A unified geostatistical machine learning framework for predicting and attributing arsenic contamination in southwestern Ghana.
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            NameFull: Bosson-Amedenu, Senyefia
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            NameFull: Ayitey, Emmanuel
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              Text: 5/21/2026
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              Y: 2026
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