Articles | Volume 22, issue 15
https://doi.org/10.5194/bg-22-3769-2025
https://doi.org/10.5194/bg-22-3769-2025
Research article
 | 
05 Aug 2025
Research article |  | 05 Aug 2025

Improved understanding of nitrate trends, eutrophication indicators, and risk areas using machine learning

Deep S. Banerjee and Jozef Skákala

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Cited articles

Anderson, D. M., Cembella, A. D., and Hallegraeff, G. M.: Progress in understanding harmful algal blooms: paradigm shifts and new technologies for research, monitoring, and management, Annu. Rev. Mar. Sci., 4, 143–176, 2012. a
Axe, P., Clausen, U., Leujak, W., Malcolm, S., and Harvey, E.: Eutrophication status of the OSPAR maritime area, Third Integrated Report on the Eutrophication Status of the OSPAR Maritime Area, https://www.vliz.be/imisdocs/publications/308897.pdf (last access: 20 June 2025), 2017. a, b, c, d, e
Axe, P., Sonesten, L., and Skarbövik, E.: Inputs of Nutrients to the OSPAR Maritime Area, https://oap-cloudfront.ospar.org/media/filer_public/5d/10/5d10879f-0a4e-4c9a-b8b8-138b4215cd0b/p00930_inputs_of_nutrients_qsr2023.pdf (last access: 20 June 2025), 2022. a, b, c, d
Banerjee, D S. : Neural Network Model code, GitHub [code], https://github.com/dsbanerjee90/neccton_algo_bgcnn, last access: 20 June 2025. a
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Short summary
Nitrate is a crucial nutrient in oceans, whose excess can trigger uncontrolled algae growth that damages marine ecosystems. We used machine learning to generate skilled, gap-free, bi-decadal surface nitrate data from sparse observations, revealing areas on the North-West European Shelf that are more vulnerable to excess algae growth if nutrient pollution occurs. We also looked at bi-decadal trends in coastal nitrate and the impact of winter nitrate on spring phytoplankton blooms.
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