Using machine learning to quantify sources of light-absorbing water-soluble humic-like substances (HULISws) in Northeast China

• Published in: Atmospheric environment (1994) Vol. 291; p. 119371
• Main Authors: Hong, Yihang, Cao, Fang, Fan, Mei-Yi, Lin, Yu-Chi, Bao, Mengying, Xue, Yongwen, Wu, Jiyan, Yu, Mingyuan, Wu, Xia, Zhang, Yan-Lin
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.12.2022
• Subjects: absorbance; absorption; algorithms; biomass; China; climate change; combustion; environment; FLEXPART model; fossil fuels; fossils; heat; Heating season; human health; Humic-like substances; Machine learning algorithm; Optical properties; Source apportionment; water solubility; China; Humic-like substances; Heating season; Source apportionment; FLEXPART model; Optical properties; Machine learning algorithm

Water-soluble humic-like substances (HULISws) are a group of heterogeneous organic compounds in the atmosphere, greatly impacting climate change and human health. As the most significant biomass burning (BB) and fossil fuel burning polluted area in China, Northeast China Plain could generate more li...