Nitrogen‐Containing Functional Groups Dominate the Molecular Absorption of Water‐Soluble Humic‐Like Substances in Air From Nanjing, China Revealed by the Machine Learning Combined FT‐ICR‐MS Technique

Abstract The light absorption capacity of water‐soluble humic‐like substances (HULIS WS ) at the molecular level is crucial for reducing the uncertainties in modeling the radiative forcing. This study proposed a machine learning approach to allocate the light absorption coefficient at 365 nm (Abs 365 ) of HULIS WS into 8084 Fourier transform‐ion cyclotron resonance mass spectrometry (FT‐ICR‐MS) detached molecular markers and their potential functional groups. The ML model showed an acceptable uncertainty (<5%) to the whole Abs 365 value based on the prediction errors. The results showed that five critical light‐absorbing molecules (C 4 H 6 O 4 NS, C 8 H 6 O 4 NS, C 11 H 15 O 3 N 2 , C 12 H 15 O 3 N 2 , and C 19 H 21 O 6 ) could explain 74% (±3%) of the variation of Abs 365 in the winter, whereas no crucial light‐absorbing molecules were found in the summer. Besides, the nitrogen‐containing functional groups were found to dominate (61% ± 8%) the molecular absorption near the 365 nm of the spectrum. This work illustrated how functional groups affect the absorption of HULIS WS , providing critical information for future research of HULIS WS on the molecular level.