Interpretable Machine Learning and Reactomics Assisted Isotopically Labeled FT-ICR-MS for Exploring the Reactivity and Transformation of Natural Organic Matter during Ultraviolet Photolysis-Reference-Cited by-同舟云学术

Interpretable Machine Learning and Reactomics Assisted Isotopically Labeled FT-ICR-MS for Exploring the Reactivity and Transformation of Natural Organic Matter during Ultraviolet Photolysis

Published:2023-12-19 Issue:1 Volume:58 Page:816-825
ISSN:0013-936X
Container-title:Environmental Science & Technology
language:en
Short-container-title:Environ. Sci. Technol.

Author:

Dwinandha Dhimas¹,Elsamadony Mohamed²³,Gao Rongjun¹,Fu Qing-Long⁴^ORCID,Liu Jibao¹⁵,Fujii Manabu¹^ORCID

Affiliation:

1. Department of Civil and Environmental Engineering, Tokyo Institute of Technology, Meguro-ku, Tokyo 152-8552, Japan

2. Department of Mechanical Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia

3. Center for Refining and Advanced Chemicals, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia

4. School of Environmental Studies, China University of Geosciences, Wuhan 430074, China

5. Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China

Funder

Japan Society for the Promotion of Science

Publisher

American Chemical Society (ACS)

Subject

Environmental Chemistry,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.est.3c05213

Reference76 articles.

1. Health Canada. Guidance on Natural Organic Matter in Drinking Water. Government of Canada, 2019; 73.

2. Spectrofluorometric characterization of dissolved organic matter for indication of precursor organic material and aromaticity

3. Lakes and reservoirs as regulators of carbon cycling and climate

4. Multivariate Statistical Approaches for the Characterization of Dissolved Organic Matter Analyzed by Ultrahigh Resolution Mass Spectrometry

5. Sources and Age of Aquatic Humus

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