Kai Wang

Southern University of Science and Technology

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Ding He

Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)

Yuanbi Yi

Hong Kong University of Science and Technology

Julian Merder

Carnegie Institution for Science

Si‐Liang Li

Tianjin University

Penghui Li

Sun Yat-sen University

Sheng Xu

Collaborative Innovation Center of Advanced Microstructures

Cong‐Qiang Liu

Tianjin University

Hongyan Bao

Xiamen University

Xinyue Xu

National University of Defense Technology

Lixin Zhang

Harbin Medical University

Cooperative Institutions

Tianjin University

Chinese Academy of Sciences

Hong Kong University of Science and Technology

University of Hong Kong

Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)

Nanjing University

Carl von Ossietzky Universität Oldenburg

Scottish Universities Environmental Research Centre

Tsinghua University

Unité Matériaux et Transformations

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Machine Learning Models for Evaluating Biological Reactivity Within Molecular Fingerprints of Dissolved Organic Matter Over Time

Geophysical Research Letters (2024)

Chen Zhao Kai Wang Qianji Jiao Xinyue Xu Yuanbi Yi

Abstract Reservoirs exert a profound influence on the cycling of dissolved organic matter (DOM) in inland waters by altering flow regimes. Biological incubations can help to disentangle the role that microbial processing plays in the DOM cycling within reservoirs. However, the complex DOM composition poses a great challenge to the analysis of such data. Here we tested if the interpretable machine learning (ML) methodologies can contribute to capturing the relationships between molecular reactivity and composition. We developed time‐specific ML models based on 7‐day and 30‐day incubations to simulate the biogeochemical processes in the Three Gorges Reservoir over shorter and longer water retention periods, respectively. Results showed that the extended water retention time likely allows the successive microbial degradation of molecules, with stochasticity exerting a non‐negligible effect on the molecular composition at the initial stage of the incubation. This study highlights the potential of ML in enhancing our interpretation of DOM dynamics over time.

Biogeochemical Cycle

Reactivity

Cycling

10.1029/2024gl108794

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Assessing the Impacts of Reservoirs on Riverine Dissolved Organic Matter: Insights From the Largest Reservoir in the Pearl River

Journal of Geophysical Research Biogeosciences (2024)

Yuanbi Yi Si‐Liang Li Jun Zhong Kai Wang Julian Merder

Abstract Extensive reservoir construction has fragmented more than 70% of the world's rivers, significantly impacting river connectivity and carbon cycling. However, the response of riverine dissolved organic matter (DOM) to reservoir influence and its potential downstream effects remains unclear. In this study, we employed multiple analytical techniques, including Fourier transform ion cyclotron resonance mass spectrometry, radiocarbon dating, and environmental factor analysis, to investigate the dynamic changes in DOM and its controlling factors under different hydrological management regimes in the LongTan Reservoir, the largest reservoir in the Pearl River, which is the second largest river in China by water discharge. Our results indicate that the molecular diversity of riverine DOM is reduced in the reservoir. Oxygen‐rich and heteroatomic compounds, such as those containing nitrogen, sulfur, and phosphorus, are preferentially removed through enhanced photo‐ and biodegradation processes in the reservoir, particularly during the storage period. This leads to DOM that is enriched with oxygen‐poor compounds and shows a biodegraded Δ 14 C value downstream. This study highlights that the composition of riverine DOM is significantly altered by the reservoir, but these effects could potentially be mitigated by optimizing the outlet location.

10.1029/2024jg008199

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Severe flood modulates the sources and age of dissolved organic carbon in the Yangtze River Estuary

Environmental Research (2024)

Zhao Liang Chen Yuanbi Yi Wenjing Fu Wenzhao Liang Penghui Li

Yangtze river

10.1016/j.envres.2024.119040

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Citations (2)