2010年09月-2014年07月，东北林业大学，建筑环境与能源应用工程专业，工学学士

2014年08月-2016年05月，科罗拉多大学博尔德分校，机械工程，硕士

2016年08月-2020年05月，俄亥俄州立大学，环境工程专业，博士

2026-08至今，清华大学环境学院，副研究员

2022年08月-2026年06月，圣地亚哥州立大学 (美国)，助理教授

2020年06月-2022年06月，加州大学戴维斯分校 (美国)，博士后

从事大气污染物的高分辨率监测、污染特征识别与风险评估。具体方向包括：

1）大气污染物监测仪器的开发、评估与现场应用

2）大气气溶胶的准确测量及形成演化机制

3）基于机器学习的大气污染数据分析

1. 2025-2027年，After the Flames in Post-Fire WUI Homes: Characterization of Toxic Emissions, Environmental Concentrations, and Exposures，美国国家科学基金会（NSF），负责人

2. 2024-2027年，Improvement of the Toxic-Metal Aerosol Real-Time Analysis (TARTA) Instrument for Field Deployment，加州空气资源委员会（CARB），负责人

3. 2024-2026年，Toxic Metals Monitoring Regional Network: High time resolution community-based monitoring in California’s Imperial Valley，美国环保署（US EPA），负责人

1. 2026年，杰出工程教育工作者，圣地亚哥工程理事会

2. 2024年，“40位40岁以下杰出青年”，美国环境工程与科学学会

期刊文章：

1. Goftari, E., London, J., Carias, J., Gittings, E., Fulford, L., Capps, S., Louie, B., Regalado, S., Ruiz, E., Torres, C., and Li, H.* (2026), Building Capacity for Advanced Community Air Monitoring in California’s Overburdened Communities. Journal of Environmental Justice, 19394071261474817.

2. Goftari, E., Carias, J., Fulford, L., & 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, 3(1).

3. Goftari, E., & Li, H.* (2025). A Review on the Development of Airborne Particulate Metals Detection Technologies. Environmental Science & Technology Air, 2(11).

4. Li, H.*, Mazzei, L., Wallis, C., & Wexler, A. S. (2024). Detection of atmospheric particulate metals in near real-time: tunnel, urban and rural environments. Aerosol & Air Quality Research, 24(10).

5. Li, H., & May, A. A. (2022). Estimating absorption cross-section of ambient black carbon aerosols: theoretical, empirical, and machine learning models. Aerosol Science & Technology, 56(11).

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, 56(11).

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, 159.

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, 264.

9. 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, 13(5).

10. Li, H., & 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, 11(11).

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, 206.