0
Article ? AI-assigned paper type based on the abstract. Classification may not be perfect — flag errors using the feedback button. Tier 2 ? Original research — experimental, observational, or case-control study. Direct primary evidence. Sign in to save

Balancing nutrition gains and microplastic exposure reduction in global bivalve consumption

Zenodo (CERN European Organization for Nuclear Research) 2026
Haifeng Zhou, Jinlong Cui, Qiumeng Zhong, Sai Liang

Summary

Bivalves like mussels, oysters, and clams are packed with nutrients, but eating them also means ingesting tiny plastic particles that have made their way into oceans and, eventually, our seafood. This research provides the data and tools to analyze how microplastic exposure from bivalves varies across countries and income levels, and tests strategies, like cleaning up pollution sources, changing trade patterns, or adjusting what types of shellfish people eat, that could help reduce plastic exposure while still letting people enjoy the health benefits of these foods. In short, it's a toolkit for figuring out how to keep the nutritional ups

This record provides the input data and executable Python scripts required to reproduce the main computational results of the study on global bivalve-associated microplastic exposure, nutritional benefits and exposure intensity. The package corresponds to the analysis and contains three self-contained modules: 1. Result 1 reproduces 2023 country-level and global/income-group results for microplastic exposure, nutritional benefits and exposure intensity. 2. Result 2 reproduces the factor decomposition of exposure intensity from 2013 to 2023 at the country level and for global and income-group aggregates. 3. Result 3 reproduces the 2023-baseline optimization scenarios, including concentration control (S1), trade-structure optimization (S2), bivalve consumption-structure adjustment (S3) and country-specific best-feasible strategy selection (S4). Each module contains the required input data in CSV format and executable Python code. The scripts use relative paths and do not depend on local computer directories. Generated results are not included in this record and can be recreated by running the corresponding scripts.

Share this paper