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Data · dataset · 2026

Data from Uneven distribution of multi-pollutant hotspots worldwide: river exports to seas and their global change drivers in the future

Listed in DANS Data Station Physical and Technical Sciences

The study aims to identify future multi-pollutant hotspots of coastal waters and analyse their spatial distribution patterns linked to socio-economic drivers across global income levels.

Description

The research applies the established MARINA-Multi to quantify river exports of nutrients (nitrogen & phosphorus), plastics (micro & macro), and chemicals (triclosan & diclofenac) by sub-basin and source for 2010 and 2050. The study shows that global river exports of pollutants to coastal waters are projected to increase by 2050, though not uniformly across sub-basins or pollutant types, leading to unevenly distributed future multi-pollutant hotspots.

Rapid urbanization drives plastics pollution hotspots in high-income basins, and over 75% of the population in low-income basins is projected to live in future hotspots. These findings underscore the need to address income-based disparities in pollution management and to support basin-based policies for equitable and sustainable water governance. The csv files "Fig2_PDFs_2010_2050", "Fig3_Fig4_hotspot_approaches", "Fig5_hotspot_classified", and "Fig6_hotspot_D_values_per_driver" contain the main results of Micella et al. (XX).

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The model results are presented by river sub-basin for the years 2010 and 2050. These results are obtained by the VIC-MARINA-Multi modelling framework using forcing data from 5 global climate models for 2010 and 2050 under an economy-driven and high global warming scenario (SSP5-RCP8.5), following the methodology as described in the "Methods" section of the paper and in the "Supplementary Information" of the paper. The folder contains the "Fig2_codebook.csv", "Fig3_Fig4_codebook.csv", "Fig5_codebook.csv", and "Fig6_codebook.csv" files, which provide further descriptions of the parameters.

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Provenance · 1 source records, 9 field assertions
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DANS Data Station Physical and Technical Sciencesdoi:10.17026/PT/WVRTGX12 d agoJSON v1
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