Atmospheric organic aerosol contains thousands of compounds spanning broad ranges of concentration, volatility, polarity, molecular weight, and chemical functionality. We develop and apply complementary liquid- and gas-chromatographic methods coupled with ultrahigh-resolution mass spectrometry, including LC-Orbitrap MS and GC-Orbitrap MS, for targeted and nontargeted molecular characterization. Advanced sample-preparation methods further improve the detection of trace, volatile, and otherwise overlooked organic compounds. These approaches reveal molecular constituents, isomers, source markers, chromophores, and atmospheric transformation products in urban and marine aerosol.
We also integrate molecular measurements with machine learning to identify relationships between chemical structure and aerosol properties. Current applications use molecular descriptors and chromophore structures to predict the optical properties of brown carbon and support the identification of previously unrecognized light-absorbing compounds. More broadly, data-driven approaches are used to interpret high-dimensional aerosol datasets and predict aerosol composition and physicochemical properties, with particular attention to model interpretability, data imbalance, and uncertainty.
Representative publications
1. Wang, T., Zhou, L. Y., Cao, W. J., Cui, H. T., Liu, Y., Yuan, W., Guo, J., Jing, M., Zhang, L. S., Xu, W., Hoffmann, T., Huang, R. J.*: Capturing elusive volatile organics in atmospheric particles via dual-mode solid-phase microextraction coupled with gas chromatography-Orbitrap mass spectrometry, Anal. Chem., 98, 18215-18224, 2026.
2. Wang, T., Huang, R. J.*, Jing, M., Che, J. S., Xing, J. T., Yang, L., Yuan, W., Wang, Y., Guo, J., Zhong, H. B., Huang, D. D., Huang, C., Xu, W.: Overlooked trace molecules in organic aerosol revealed by gas chromatography-Orbitrap mass spectrometry, Environ. Sci. Technol., 58, 18264-18272, 2024.
3. Wang, Y., Huang, R. J.*, Zhong, H. B., Wang, T., Yang, L., Yuan, W., Xu, W., An, Z. S.: Predictions of the optical properties of brown carbon by machine learning with typical chromophores, Environ. Sci. Technol., 58, 20588-20597, 2024.
4. Wang, Y., Xu, W., Zhong, H. B., Chen, B. H., Yang, L., Wang, L., Ren, J. Y., Duan, J., Lin, C. S., Huang, R. J.*: Advancing aerosol chemistry with machine learning: A short review, ACS ES&T Air, 2, 2323-2341, 2025.
5. Zhan, Y. N., Huang, R. J.*, Wang, T., Jing, M., Zhong, H. B., Xu, W., Zeng, Y., Chen, S. J.: Nontarget analysis of organic aerosol over the South China Sea by gas chromatography-Orbitrap mass spectrometry, ACS Earth Space Chem., 8, 1924-1932, 2024.
