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Molecular Evidence from FT-ICR MS and Bayesian Mixing Model Reveals Hillslope Soils as Dominant DOM Contributors during Rainfall

土壤水分 环境科学 混合(物理) 水文学(农业) 贝叶斯概率 土壤科学 贝叶斯推理 大气科学 地质学 模型验证 水文模型
作者
Xiaodie Hu,Wanying Yan,Silin Ma,Jinsong Zhao,Jian Wang,Haibing Xiao,Zhihua Shi
出处
期刊:Environmental Science & Technology [American Chemical Society]
卷期号:60 (19): 14171-14180
标识
DOI:10.1021/acs.est.6c02514
摘要

Rainfall-driven migration of dissolved organic matter (DOM) from various sources regulates terrestrial carbon cycling and pollutant fate, yet its spatiotemporal source dynamics remains unresolved due to methodological limitations. Here, we conducted high-frequency runoff sampling across 15 rainfall events with potential DOM source samples including hillslope soil, riparian soil, groundwater, and rainwater. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) was integrated with the Bayesian mixing model to quantify source-specific DOM contributions at high temporal resolution. Results showed significantly higher humic-like component proportions in soil and groundwater than in rainwater, whereas rainwater had the highest protein-like component. Furthermore, DOM aromaticity and unsaturated degree in runoff reached a maximum at peak discharge during rainfall, indicating the input of abundant terrestrial materials. The Bayesian mixing model revealed soils as the dominant source of runoff DOM (49.2% hillslope soil and 30.3% riparian soil), followed by groundwater (13.1%) and rainwater (7.5%). Specifically, the average contribution of hillslope soil to runoff DOM increased from 42.4% to 57.6% during rainfall events, and the highest average proportion of riparian soil (35.6%) was observed at the beginning of rainfall. Our finding that hillslope soil is a key contributor to runoff DOM during rainfall underscores the need to prioritize upland management in catchment strategies.
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