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Global Distribution of Mercury in Foliage Predicted by Machine Learning

  • Long Chen
  • , Jun Zhou
  • , Long Guo*
  • , Xinyu Bian
  • , Zeng Xu
  • , Qinzheng Chen
  • , Shu Hai Wen
  • , Kang Wang
  • , Yu Rong Liu*
  • *Corresponding author for this work
  • Ministry of Natural Resources of the People's Republic of China
  • Chinese Academy of Sciences
  • Huazhong Agricultural University
  • East China Normal University

Research output: Contribution to journalArticlepeer-review

Abstract

Foliar assimilation of elemental mercury (Hg0) from the atmosphere plays a critical role in the global Hg biogeochemical cycle, leading to atmospheric Hg removal and soil Hg insertion. Recent studies have estimated global foliar Hg assimilation; however, large uncertainties remained due to coarse accounting of observed foliar Hg concentrations, posing a substantial challenge in constraining the global Hg budget. Here, we integrated a comprehensive observation database of foliar Hg concentrations and machine learning algorithms to predict the first spatial distribution of foliar Hg concentrations on a global scale, contributing to the first estimate of global Hg pools in foliage. The global average of foliar Hg concentrations was estimated to be 24.0 ng g-1 (7.5-56.5 ng g-1), and the global total in foliar Hg pools reached 4561.3 Mg (1455.2-9062.8 Mg). The spatial distribution showed the hotspots in tropical regions, including the Amazon, Central Africa, and Southeast Asia. A range of 2268.5-2727.0 Mg yr-1 was estimated for annual foliar Hg assimilation accounting for the perennial continuous assimilation by evergreen vegetation foliage. The first spatial maps of foliar Hg concentrations and Hg pools may aid in understanding the global biogeochemical cycling of Hg, especially in the context of climate change and global vegetation greening.

Original languageEnglish
Pages (from-to)15629-15637
Number of pages9
JournalEnvironmental Science and Technology
Volume58
Issue number35
DOIs
StatePublished - 3 Sep 2024
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 13 - Climate Action
    SDG 13 Climate Action

Keywords

  • foliage
  • machine learning
  • mercury (Hg)
  • spatial map

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