Exploring the Complexities of Dissolved Organic Matter Photochemistry from the Molecular Level by Using Machine Learning Approaches
- Chen ZhaoChen ZhaoDepartment of Ocean Science and Center for Ocean Research in Hong Kong and Macau, The Hong Kong University of Science and Technology, Hong Kong 999077, ChinaMore by Chen Zhao
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- Xinyue XuXinyue XuDepartment of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong 999077, ChinaMore by Xinyue Xu
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- Hongmei ChenHongmei ChenState Key Laboratory for Marine Environmental Science, Institute of Marine Microbes and Ecospheres, College of Ocean and Earth Sciences, College of the Environment and Ecology, Xiamen University, Xiamen 361000, ChinaMore by Hongmei Chen
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- Fengwen WangFengwen WangState Key Laboratory of Coal Mine Disaster Dynamics and Control, Department of Environmental Science, Chongqing University, Chongqing 400030, ChinaMore by Fengwen Wang
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- Penghui LiPenghui LiSchool of Marine Sciences, Sun Yat-sen University, Zhuhai 519082, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, ChinaGuangdong Provincial Key Laboratory of Marine Resources and Coastal Engineering, Zhuhai 519082, ChinaMore by Penghui Li
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- Chen HeChen HeState Key Laboratory of Heavy Oil Processing, China University of Petroleum, Changping District, Beijing 102249, ChinaMore by Chen He
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- Quan ShiQuan ShiState Key Laboratory of Heavy Oil Processing, China University of Petroleum, Changping District, Beijing 102249, ChinaMore by Quan Shi
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- Yuanbi YiYuanbi YiDepartment of Ocean Science and Center for Ocean Research in Hong Kong and Macau, The Hong Kong University of Science and Technology, Hong Kong 999077, ChinaMore by Yuanbi Yi
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- Xiaomeng LiXiaomeng LiDepartment of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong 999077, ChinaMore by Xiaomeng Li
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- Siliang LiSiliang LiInstitute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, ChinaMore by Siliang Li
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- Ding He*Ding He*E-mail: [email protected]Department of Ocean Science and Center for Ocean Research in Hong Kong and Macau, The Hong Kong University of Science and Technology, Hong Kong 999077, ChinaState Key Laboratory of Marine Pollution, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong 999077, ChinaMore by Ding He
Abstract
Dissolved organic matter (DOM) sustains a substantial part of the organic matter transported seaward, where photochemical reactions significantly affect its transformation and fate. The irradiation experiments can provide valuable information on the photochemical reactivity (photolabile, photoresistant, and photoproduct) of molecules. However, the inconsistency of the fate of irradiated molecules among different experiments curtailed our understanding of the roles the photochemical reactions have played, which cannot be properly addressed by traditional approaches. Here, we conducted irradiation experiments for samples from two large estuaries in China. Molecules that occurred in irradiation experiments were characterized by the Fourier transform ion cyclotron resonance mass spectrometry and assigned probabilistic labels to define their photochemical reactivity. These molecules with probabilistic labels were used to construct a learning database for establishing a suitable machine learning (ML) model. We further applied our well-trained ML model to “un-matched” (i.e., not detected in our irradiation experiments) molecules from five estuaries worldwide, to predict their photochemical reactivity. Results showed that numerous molecules with strong photolability can be captured solely by the ML model. Moreover, comparing DOM photochemical reactivity in five estuaries revealed that the riverine DOM chemistry largely determines their subsequent photochemical transformation. We offer an expandable and renewable approach based on ML to compatibly integrate existing irradiation experiments and shed insight into DOM transformation and degradation processes.
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