Publications

1. Goftari, E. (G - SDSU), and Li, H.* (2025), A Review on the Development of Airborne Particulate Metals Detection Technologies. Environmental Science & Technology Air. https://pubs.acs.org/doi/full/10.1021/acsestair.5c00134

2. Goftari, E. (G - SDSU), Carias, J. (UG - SDSU), Fulford, L. (UG - SDSU), and Li, H.* (2025), Post-Wildfire Indoor Pollution in WUI Areas Following the 2025 Los Angeles Fires — Part I: Establishing Baseline Contaminant Levels Prior to Home Reoccupation. Environmental Science & Technology Air. https://doi.org/10.1021/acsestair.5c00281

3. Li, H.*Goftari, E. (G - SDSU), Wexler, A.S. (2025), Near Real-Time Detection of Airborne Particulate Metals: Emerging Technologies and Applications. Special Issue on Air Quality for Environmental Engineer and Scientist 

4. Li, H., Mazzei, L., Wallis, C.D., Wexler, A.S. (2024). Detection of Atmospheric Particulate Metals in near Real-Time: Tunnel, Urban, and Rural Environments. Aerosol Air Qual. Res. https://doi.org/10.4209/aaqr.240117

5. † Li, H. and May, A. A. (2022), Estimating absorption cross-section of ambient black carbon aerosols: theoretical, empirical, and machine learning models. Aerosol Science & Technology. https://doi.org/10.1080/02786826.2022.2114311.

6. May, A.A. and Li, H. (2022), Application of machine learning approaches in the analysis of mass absorption cross-section of black carbon aerosols: sensitivity analyses and wavelength dependencies. Aerosol Science & Technology. https://doi.org/10.1080/02786826.2022.2114312.

7. Li, H.*, Mazzei, L., Wallis, C., & Wexler, A.S. (2022), Improving quantitative analysis of spark-induced breakdown spectroscopy: multivariate calibration of metal particles using machine learning. Journal of Aerosol Science. https://doi.org/10.1016/j.jaerosci.2021.105874

8. Li, H.*, Mazzei, L., Wallis, C., Davari, S.A., & Wexler, A.S. (2021), The performance of an inexpensive spark-induced breakdown spectroscopy instrument for near real-time analysis of heavy metal particles. Atmospheric Environment. https://doi.org/10.1016/j.atmosenv.2021.118666

9. †Li, H. and May, A.A. (2020), An exploratory approach using regression and machine learning in the analysis of mass absorption cross section of black carbon aerosols: model development and evaluation. Atmosphere. https://doi.org/10.3390/atmos11111185

10.  Li, H., McMeeking, G.R., & May, A.A. (2020), Development of a new correction algorithm applicable to any filter-based absorption photometer. Atmospheric Measurement Techniques. https://doi.org/10.5194/amt-13-2865-2020

11. Li, H., Lamb, K. D., Schwarz, J. P., Selimovic, V., Yokelson, R. J., McMeeking, G. R., &May, A. A. (2019), Inter-comparison of black carbon measurement methods for simulated open biomass burning emissions. Atmospheric Environment. https://doi.org/10.1016/j.atmosenv.2019.03.010