全球汞数据集,预测1995-2022年期间海产品中甲基汞浓度。

IF 7.2 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-02-11 DOI:10.1038/s41597-025-04570-3
Haifeng Zhou, Yumeng Li, Qiumeng Zhong, Xiaohui Wu, Sai Liang
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引用次数: 0

摘要

汞接触对人类健康构成重大威胁,特别是其有机形式甲基汞。饮食是人类接触甲基汞的主要途径,尤其是通过食用海鲜。在此背景下,许多研究已经建立了海鲜甲基汞浓度数据集,以评估海鲜消费中与甲基汞相关的健康风险。然而,现有数据集仅限于特定区域和短期观测,因此难以支持对全球汞相关健康风险的持续和动态评估。本研究采用自下而上的方法构建了1995-2022年全球海产品甲基汞浓度数据集。首先,基于现有文献和机器学习方法报道的海产品汞浓度,编制了海产品甲基汞浓度的长期时间序列海洋尺度数据集。随后,本研究以每个国家在不同海域的海产品捕获量作为权重,估计了全国范围内海产品的甲基汞浓度。该数据集可为《关于汞的水俣公约》第12条和第19条所述汞及其化合物的环境影响评估提供必要的数据支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Global mercury dataset with predicted methylmercury concentrations in seafoods during 1995-2022.

Mercury exposure poses significant threats to human health, particularly in its organic form, methylmercury (MeHg). Diet is the main pathway for human MeHg exposure, especially through seafood consumption. In this context, numerous studies have established seafood MeHg concentration datasets to assess MeHg-related health risks from seafood consumption. However, existing datasets are limited to specific regions and short-term observations, making it difficult to support continuous and dynamic assessments of global MeHg-related health risks. This study takes a bottom-up approach to construct a global seafood MeHg concentration dataset during 1995-2022. Firstly, it compiles a long-term time series marine-scale dataset of seafood MeHg concentrations, based on the reported seafood mercury concentrations from existing literature and machine learning methods. Subsequently, this study used the seafood catch volumes of each nation in different marine areas as weights to estimate the national-scale seafood MeHg concentrations. This dataset can provide essential data support for environmental impact assessment of mercury and its compounds as mentioned in Articles 12 and 19 of the Minamata Convention on Mercury.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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