Preprints
https://doi.org/10.5194/essd-2025-17
https://doi.org/10.5194/essd-2025-17
03 Feb 2025
 | 03 Feb 2025
Status: this preprint is currently under review for the journal ESSD.

Mapping the global distribution of lead and its isotopes in seawater with explainable machine learning

Arianna Olivelli, Rossella Arcucci, Mark Rehkämper, and Tina van de Flierdt

Abstract. Lead (Pb) and its isotopes are a powerful tool to study the pathways of Pb pollution from land to sea and, simultaneously, investigate biogeochemical processes in the ocean. However, the scarcity and sparsity of in situ measurements of Pb concentrations and isotope compositions do not allow for a comprehensive understanding of Pb pollution pathways and biogeochemical cycling on a global scale. Here, we present three machine learning models developed to map seawater Pb concentrations and isotope compositions leveraging the global GEOTRACES dataset as well as historical data. The models use climatologies of oceanographic and atmospheric variables as features from which to predict Pb concentrations, 206Pb/207Pb, and 208Pb/207Pb. Using Shapley Additive Values (SHAP), we found that seawater temperature, atmospheric dust and black carbon, and salinity are the most important features for predicting Pb concentrations. Dissolved oxygen concentration, salinity, temperature, and atmospheric dust are the most important features for predicting 206Pb/207Pb, while atmospheric black carbon and dust, seawater temperature, and surface chlorophyll-a for 208Pb/207Pb. Our model outputs show that (i) the surface Indian Ocean has the highest levels of pollution, (ii) pollution from previous decades is sinking in the North Atlantic and Pacific Ocean, and (iii) waters characterised by a highly anthropogenic Pb isotope fingerprint are spreading from the Southern Ocean throughout the Southern Hemisphere at intermediate depths. By analysing the uncertainty associated with our maps, we identified the Southern Ocean as the key area to prioritise in future sampling campaigns. Our datasets, models and their outputs, in the form of Pb concentration, 206Pb/207Pb, and 208Pb/207Pb climatologies, are made freely available to the community at Olivelli et al. (2024a, https://doi.org/10.5281/zenodo.14261154) and https://github.com/OlivelliAri/Pb-ML_GEOTRACES

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Arianna Olivelli, Rossella Arcucci, Mark Rehkämper, and Tina van de Flierdt

Status: open (until 21 Mar 2025)

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Arianna Olivelli, Rossella Arcucci, Mark Rehkämper, and Tina van de Flierdt

Data sets

Data for: Mapping the global distribution of lead and its isotopes in seawater with explainable machine learning Arianna Olivelli, Rossella Arcucci, Mark Rehkämper, and Tina van de Flierdt https://doi.org/10.5281/zenodo.14261154

Model code and software

Pb-ML_GEOTRACES Arianna Olivelli https://github.com/OlivelliAri/Pb-ML_GEOTRACES

Interactive computing environment

Pb-ML_GEOTRACES Arianna Olivelli https://github.com/OlivelliAri/Pb-ML_GEOTRACES

Arianna Olivelli, Rossella Arcucci, Mark Rehkämper, and Tina van de Flierdt

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Short summary
In this study, we used machine learning models to produce the first global maps of Pb concentrations and isotope compositions in the ocean. We found that (i) the surface Indian Ocean has the highest levels of pollution, (ii) pollution from previous decades is sinking in the North Atlantic and Pacific Ocean, and (iii) waters carrying Pb pollution are spreading from the Southern Ocean throughout the Southern Hemisphere at intermediate depths.
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