Biogeosciences
Biogeosciences
BG
Articles | Volume 22, issue 15
Articles | Volume 22, issue 15
https://doi.org/10.5194/bg-22-3769-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-22-3769-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 22, issue 15
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

Viewed

Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.

Total article views: 1,686 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,553 90 43 1,686 46 93 95
  • HTML: 1,553
  • PDF: 90
  • XML: 43
  • Total: 1,686
  • Supplement: 46
  • BibTeX: 93
  • EndNote: 95
Views and downloads (calculated since 19 Jun 2024)
Cumulative views and downloads (calculated since 19 Jun 2024)

Viewed (geographical distribution)

Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.

Total article views: 1,686 (including HTML, PDF, and XML) Thereof 1,668 with geography defined and 18 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 Dec 2025
Download
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.
Read more
Share
Altmetrics
Final-revised paper
Preprint