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

Data sets

Complete ERA5 from 1940: Fifth generation of ECMWF atmospheric reanalyses of the global climate H. Hersbach et al. https://doi.org/10.24381/cds.143582cf

Dataset - 12138-1: Scottish Coastal Observatory Dataset - 12138-1 Scottish Coastal Observatory https://doi.org/10.7489/12138-1

Dataset - 610-1: Scottish Coastal Observatory Dataset - 610-1 Scottish Coastal Observatory https://doi.org/10.7489/610-1

Dataset - 948-1: Scottish Coastal Observatory Dataset - 948-1 Scottish Coastal Observatory https://doi.org/10.7489/948-1

Dataset – 952-1: Scottish Coastal Observatory Dataset - 952-1 Scottish Coastal Observatory https://doi.org/10.7489/953-1

Model code and software

Neural Network Model code Deep S. Banerjee https://github.com/dsbanerjee90/neccton_algo_bgcnn

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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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