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 (HULISWS) 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 (Abs365) of HULISWS 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 Abs365 value based on the prediction errors. The results showed that five critical light-absorbing molecules (C4H6O4NS, C8H6O4NS, C11H15O3N2, C12H15O3N2, and C19H21O6) could explain 74% (±3%) of the variation of Abs365 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 HULISWS, providing critical information for future research of HULISWS on the molecular level.