Modeling groundwater redox conditions at national scale through integration of sediment color and water chemistry in a machine learning framework.

Autor: Koch J; Geological Survey of Denmark and Greenland, Department of Hydrology, Copenhagen, Denmark. Electronic address: juko@geus.dk., Kim H; Geological Survey of Denmark and Greenland, Department of Geochemistry, Copenhagen, Denmark., Tirado-Conde J; Geological Survey of Denmark and Greenland, Department of Hydrology, Copenhagen, Denmark., Hansen B; Geological Survey of Denmark and Greenland, Department of Geochemistry, Copenhagen, Denmark., Møller I; Geological Survey of Denmark and Greenland, Department of Near Surface Land and Marine Geology, Århus, Denmark., Thorling L; Geological Survey of Denmark and Greenland, Department of Geochemistry, Copenhagen, Denmark., Troldborg L; Geological Survey of Denmark and Greenland, Department of Hydrology, Copenhagen, Denmark., Voutchkova D; Geological Survey of Denmark and Greenland, Department of Geochemistry, Copenhagen, Denmark., Højberg AL; Geological Survey of Denmark and Greenland, Department of Hydrology, Copenhagen, Denmark.
Jazyk: angličtina
Zdroj: The Science of the total environment [Sci Total Environ] 2024 Oct 15; Vol. 947, pp. 174533. Date of Electronic Publication: 2024 Jul 05.
DOI: 10.1016/j.scitotenv.2024.174533
Abstrakt: Redox conditions play a crucial role in determining the fate of many contaminants in groundwater, impacting ecosystem services vital for both the aquatic environment and human water supply. Geospatial machine learning has previously successfully modelled large-scale redox conditions. This study is the first to consolidate the complementary information provided by sediment color and water chemistry to enhance our understanding of redox conditions in Denmark. In the first step, the depth to the first redox interface is modelled using sediment color from 27,042 boreholes. In the second step, the depth of the first redox interface is compared against water chemistry data at 22,198 wells to classify redox complexity. The absence of nitrate containing water below the first redox interface is referred to as continuous redox conditions. In contrast, discontinuous redox conditions are identified by the presence of nitrate below the first redox interface. Both models are built using 20 covariate maps, encompassing diverse hydrologically relevant information. The first redox interface is modelled with a mean error of 0.0 m and a root-mean-squared error of 8.0 m. The redox complexity model attains an accuracy of 69.8 %. Results indicate a mean depth to the first redox interface of 8.6 m and a standard deviation of 6.5 m. 60 % of Denmark is classified as discontinuous, indicating complex redox conditions, predominantly collocated in clay rich glacial landscapes. Both maps, i.e., first redox interface and redox complexity are largely driven by the water table and hydrogeology. The developed maps contribute to our understanding of subsurface redox processes, supporting national-scale land-use and water management.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024. Published by Elsevier B.V.)
Databáze: MEDLINE
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