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.

The effect of quarantine policy on pollution emission and the usage of private transportation in urban areas

“…Here, three random forest (RF) models were built to decipher the complex non-linear function relation using the ranger package in the R programming language 53 for weather normalization and source apportionment 39 , 40 . Random forest is the suitable algorithm for conducting small dataset and has been widely used in source apportionment research previously 43 , 54 . Previous research indicated that anthropogenic emissions (e.g., transport and factory emissions), biomass burning emissions, and meteorological conditions are the three crucial factors of variation of BC in the atmosphere 55 .…”

“…After that, an attribution technique was used to decompose the normalized BC (BC emission) concentration to traffic and public emitted BC by assuming RF algorithem well learned the relationship shown in Eq. ( 3 ) 39 , 54 . The predicted BC shows a good correlation with observed BC (Fig.…”