永久冻土
泥炭
Mercury(编程语言)
沉积(地质)
环境科学
自然地理学
沼泽
水文学(农业)
海洋学
地质学
考古
地理
地貌学
岩土工程
沉积物
计算机科学
程序设计语言
作者
Scott Zolkos,Benjamin M. Geyman,Stefano Potter,Michael Moubarak,Brendan M. Rogers,Natalie Baillargeon,Sagnik Dey,S. Ludwig,Sierra Melton,Edauri Navarro-Pérez,Ann McElvein,Prentiss H. Balcom,Susan M. Natali,Seeta A. Sistla,Elsie M. Sunderland
标识
DOI:10.1021/acs.est.4c08765
摘要
Increasing wildfire activity at high northern latitudes has the potential to mobilize large amounts of terrestrial mercury (Hg). However, understanding implications for Hg cycling and ecosystems is hindered by sparse research on peatland wildfire Hg emissions. In this study, we used measurements of soil organic carbon (SOC) and Hg, burn depth, and environmental indices derived from satellite remote sensing to develop machine learning models for predicting Hg emissions from major wildfires in the permafrost peatland of the Yukon-Kuskokwim Delta (YKD) in southwestern Alaska. Wildfire Hg emissions during summer 2015─estimated as the product of Hg:SOC (0.38 ± 0.17 ng Hg g C
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