Haebum Lee, PhD.
(Developer)

Postdoctoral Scholar

Jaffe Group,
School of STEM,
University of Washington (UW),
Bothell, WA, United States


Professional Summary

I am an Atmospheric and Data Scientist (Ph.D.) with 8+ years of experience blending atmospheric air quality modeling, machine/deep learning, and cloud-scale geospatial data analysis. I have a proven track record of bridging the gap between rigorous academic research and production-ready intelligence tools, synthesizing multi-hazard environmental parameters to drive data-informed decisions.

Beyond pure academic research, I am passionate about translating complex scientific data into real-world applications. I designed and deployed interactive wildfire smoke and ozone analytic applications, highlighted by architecting Smokelyze (https://smokelyze.org/), which evolved from an R shiny tool: PMO3smokeTool (https://westar.shinyapps.io/PMO3smokeTool/) to a scalable, cloud-native solution using JavaScript and Google Cloud (GCS, Cloud Run). These systems are widely utilized by regional air quality working groups and state environmental regulatory agencies to guide data-driven policy decisions and environmental analysis.

My scientific research centers on the multifaceted study of air quality and atmospheric phenomena, including the analysis of wildfire smoke on ozone, air pollutant monitoring, and the utilization of satellite and remote sensing data to assess atmospheric parameters. I have also deeply engaged in studying the wildfire smoke impacts on air quality, the physicochemical properties of atmospheric aerosols, the dynamics of new particle formation, and the development of predictive models using machine learning.


Research Interests & Core Expertise

  • Advanced Statistical/Machine Learning Modeling & GAM Expertise:
    Designing, evaluating, and validating complex Generalized Additive Models (GAM) to isolate and quantify meteorological and chemical drivers of air pollution, establishing rigorous industry-defensible standards for environmental multi-hazard analytics
  • Atmospheric Hazard & Wildfire Smoke Analytics:
    Spatiotemporal analysis of cross-border wildfire smoke transport, tracking physical risks, and modeling its downstream impacts on ozone (O3) and particulate matter (PM2.5)
  • Satellite Remote Sensing & Big Data Integration:
    Advanced utilization of next-generation satellite data (NOAA-HMS, TEMPO, TROPOMI, MODIS, NASA-OMI, GOES, etc.), global model data (MERRA-2, HRRR, ECMWF, etc.), and air-mass trajectory models (HYSPLIT) to track planetary and regional physical risks.
  • Applied Machine Learning in Atmospheric Sciences:
    Designing and deploying predictive and predictive-morphology models utilizing machine learning and deep learning (CNN) techniques to quantify atmospheric aerosol properties and new particle formation (NPF)
  • Advanced Aerosol Instrumentation & Environmental Monitoring:
    Physicochemical characterization of ambient nanoparticles and sub-micrometer aerosols across diverse environments (Arctic, urban, and agricultural sites) using real-time automated instrumentation and Laser-Induced Breakdown Spectroscopy (LIBS)

Technical skills

  • Geospatial & Data Engineering:
    Cloud-scale geospatial data processing, Spatiotemporal Analysis - Satellite Remote Sensing (NOAA-HMS, TEMPO, TROPOMI, MODIS, NASA-OMI, GOES, etc.), Global Model Data (MERRA-2, HRRR, ECMWF, etc.), and air-mass trajectory modeling (HYSPLIT)
  • Programming & Tools:
    R (Expert in statistical modeling & Shiny enterprise UI/UX), Python (Proficient in production-grade geospatial data pipelines & cross-translation), JavaScript (HTML5/CSS3 for interactive web integration), API-based automated data collection
  • Machine Learning / AI:
    Applied Machine Learning & Deep Learning (Convolutional Neural Networks for particle/image morphology classification and predictive modeling).
  • Analytical Instrumentation:
    Real-time atmospheric monitoring systems (DEG-SMPS, nano-SMPS, Aethalometer, LIBS, OC/EC analyzer)

Education & Experiences

  • Postdoc / 2023.04-present
    Jaffe Group, School of Science, Technology, Engineering & Mathematics (STEM),
    University of Washington (UW), Bothell, WA, United States
  • Postdoc / 2022.08-2023.03
    Center for PM2.5 monitoring research, School of Earth Science & Environmental Engineering,
    Gwangju Institute of Science & Technology (GIST), Gwangju, Republic of Korea
  • PhD / 2018.09-2022.08
    Aerosol Technology Monitoring Lab (ATML), School of Earth Science & Environmental Engineering,
    Gwangju Institute of Science & Technology (GIST), Gwangju, Republic of Korea
  • Researcher / 2018.03-2018.08
    Aerosol Technology Monitoring Lab (ATML), School of Earth Science & Environmental Engineering,
    Gwangju Institute of Science & Technology (GIST), Gwangju, Republic of Korea
  • MS / 2016.03-2018.02
    Aerosol Technology Monitoring Lab (ATML), School of Earth Science & Environmental Engineering,
    Gwangju Institute of Science & Technology (GIST), Gwangju, Republic of Korea
  • BS / 2010.03-2016.02
    Chemistry,
    Kyung Hee University, Seoul, Republic of Korea



Projects

Wildfire Project
Wildfire & Air Quality
PM2.5 Study
PM2.5 Characteristics
NPF Study
New Particle Formation
LIBS System
LIBS System Development

Wildfire & Air Quality

Impact of wildfire smoke on air quality in the United States

Objectives: Quantifying the impact of wildfire emissions on surface ozone
Dates of project initiation and completion: 2023.04.–present

📚 Related Publications

  • Lee, H. and Jaffe, D. A.: Impact of Wildfires on O3 and Air Quality Across the United States for 2019–2024 Using Generalized Additive Models, J. Geophys. Res.: Atmos., 130, e2025JD044088, 2025. https://doi.org/10.1029/2025JD044088
  • Lee, H. and Jaffe, D. A.: Wildfire impacts on O3 in the continental United States using PM2.5 and a generalized additive model (2018–2023), Environ. Sci. Technol., 58, 14764–14774, 2024. https://doi.org/10.1021/acs.est.4c05870
  • Lee, H. and Jaffe, D. A.: Impact of wildfire smoke on ozone concentrations using a Generalized Additive model in Salt Lake City, Utah, USA, 2006–2022, J. Air Waste Manag. Assoc., 74, 116–130, 2024. https://doi.org/10.1080/10962247.2023.2291197
  • Jaffe, D. A., Ninneman, M., Nguyen, L., Lee, H., Hu, L., Ketcherside, D., Jin L., Cope, E., Lyman, S., Jones, C., ONeli, T., Mansfield, M. L.: Key results from the Salt Lake regional smoke, ozone and Aerosol study (SAMOZA), J. Air Waste Manag. Assoc., 74, 163–180, 2024. https://doi.org/10.1080/10962247.2024.2301956

PM2.5 Characteristics

A study on the comprehensive characteristics of fine particulate matter (PM2.5)

Objectives: 1) Analyzing the chemical composition, sources, and transport of PM2.5 in winter haze over China and Korea and spring pollution over the Yellow Sea. 2) Developing advanced classification methods using machine learning to better understand fine particle characteristics and their health impacts. 3) Evaluating the performance and sustainability of face masks in filtering fine particles under various environmental conditions.
Dates of project initiation and completion: 2017.01.–2022.12.

📚 Related Publications

  • Khadgi, J., Lee, H., Seo, J., Hong, J.-H., and Park, K.: Morphological classification of fine particles in transmission electron microscopy images by using pre-trained convolution neural networks, Aerosol Sci. Technol., 1–10, 2024. https://doi.org/10.1080/02786826.2024.2322010
  • Park, M., Lee, S., Lee, H., Denna, M. C. F. J., Jang, J., Oh, D., Bae, M.-S., Jang, K.-S., and Park, K.: New health index derived from oxidative potential and cell toxicity of fine particulate matter to assess its potential health effect, Heliyon, 10 (3), e25310, 2024. https://doi.org/10.1016/j.heliyon.2024.e25310
  • Kwak, N., Lee, H., Maeng, H., Seo, A., Lee, K., Kim, S., Lee, M., Cha, J. W., Shin, B., and Park, K.: Morphological and chemical classification of fine particles over the Yellow Sea during spring, 2015–2018, Environ. Pollut., 305, 119286, 2022. https://doi.org/10.1016/j.envpol.2022.119286
  • Lee, H., Kim, S., Joo, H., Cho, H.-J., and Park, K.: A study on Performance and Reusability of Certified and Uncertified Face Masks, Aerosol Air Qual. Res., 22, 210370, 2022. https://doi.org/10.4209/aaqr.210370
  • Eom, S., Lee, H., Kim, J., Park, K., Kim, Y., Sheu, G.-R., Gay, D. A., Schmeltz, D., and Han, S.: Potential sources, scavenging processes, and source regions of mercury in the wet deposition of South Korea, Sci. Total Environ., 762, 143934, 2021. https://doi.org/10.1016/j.scitotenv.2020.143934
  • Park, M., Wang, Y., Chong, J., Lee, H., Jang, J., Song, H., Kwak, N., Borlaza, L. J. S., Maeng, H., Cosep, E. M. R., Denna, M. C. F. J., Chen, S., Seo, I., Bae, M.-S., Jang, K.-S., Choi, M., Kim, Y. H., Park, M., Ryu, J.-S., Park, S., Hu, M., and Park, K.: Simultaneous measurements of chemical characteristics and oxidative potential of fine particles during winter haze period in urban sites in China and Korea, Atmosphere, 11, 292, 2020. https://doi.org/10.3390/atmos11030292

New Particle Formation (NPF)

A study on new particle formation (NPF) in the ambient atmosphere

Objectives: Identifying differences of characteristics and governing factors for the NPF among sites
Dates of project initiation and completion: 2018.01.–2022.12.

📚 Related Publications

  • Lee, H., Cho, H., Kim, J., Yoon, Y. J., Lee, B. Y., and Park, K.: Comparison of new particle formation events in urban, agricultural, and Arctic environments, Atmos. Environ., 120634, 2024. https://doi.org/10.1016/j.atmosenv.2024.120634
  • Lee, H., Lee, K., Krejci, R., Fiebig, M., Lunder, C. R., Aas, W., Park, J., Park, K.-T., Lee, B. Y., Yoon, Y.-J., and Park, K.: Atmospheric new particle formation characteristics in the Arctic as measured at Mount Zeppelin, Svalbard, from 2016 to 2018, Atmos. Chem. Phys., 20, 13425–13441, 2020. https://doi.org/10.5194/acp-20-13425-2020

LIBS System Development

Development of laser-induced breakdown spectroscopy (LIBS) system

Objectives: Exploring the potential of LIBS technique in analyzing elements in flowback water from fracking operations and in detecting contamination particles in industrial processes
Dates of project initiation and completion: 2017.03.–2020.02.

📚 Related Publications

  • Lee, H., Kim, G., Kim, H.-A., Maeng, H., Park, H., and Park, K.: Application of laser-induced breakdown spectroscopy for detection of elements in flowback water samples from shale gas wells, Applied Optics, 59, 2254–2261, 2020. https://doi.org/10.1364/AO.381687
  • Kim, G., Kim, K., Maeng, H., Lee, H., and Park, K.: Development of Aerosol-LIBS (Laser-Induced Breakdown Spectroscopy) for Real-time Monitoring of Process-induced Particles, Aerosol and Air Quality Research, 19, 455–460, 2019. https://doi.org/10.4209/aaqr.2018.08.0312
  • Lee, H., Maeng, H., Kim, K., Kim, G., and Park, K.: Application of laser-induced breakdown spectroscopy for real-time detection of contamination particles during the manufacturing process, Applied Optics, 57 (12), 3288–3292, 2018. https://doi.org/10.1364/AO.57.003288
  • Maeng, H., Chae, H., Lee, H., Kim, G., Lee, H., Kim, K., Kwak, J., Cho, G., and Park, K.: Development of laser-induced breakdown spectroscopy (LIBS) with times ablation to improve detection efficiency, Aerosol Science and Technology, 51, 1009–1015, 2017. https://doi.org/10.1080/02786826.2017.1344352




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