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Khanh Do

Name: Khanh Do
Tel: TBD (office), (714) 837-2430 (mobile)
Email: kh.do@northeastern.edu

Education

Ph.D., Chemical and Environmental Engineering, University of California, Riverside, 2023

M.S., Electrical Engineering, University of California, Riverside, 2023

B.S., Chemical Engineering, University of California, Berkeley, 2015

Employment

Postdoctoral Scholar, Northeastern University, July 2023 - Present

Research Interests & Specialties:

My research focuses on predicting and investigating the air quality using statistical (machine learning methods) and deterministic models (WRF and CMAQ). I am interested in working with large datasets to recognize the patterns in data and explore the relationships between variables.

Keywords: Air quality and modeling; machine learning; computer vision; personal exposure; data assimilation; high-performance computing; GPU computing; air quality; climate.

Research Experience

Investigate the personal exposure of PM2.5 in the Inland Empire to quantify the air pollutant and to explain how personal habits, ethics, incomes, and communities of color drive their exposure at personal resolution (funded by Sloan Foundation).

Investigate the impacts of meteorology on ground level ozone and PM2.5 and determine a set of meteorological contributing factors to ozone formation by analyzing historical air quality and meteorology data and construct an empirical model from historical data to project air quality trends (funded by SCAQMD).

Implement GPU computing into CMAQ gas solver and utilize a large number of CUDA cores to improve CMAQ computational efficiency (funded by NSF CDS&E).

Assemble and operate BAM 1020 and perform collocation calibration for low-cost air quality sensors.

Research Interests & Specialties:

My research focuses on predicting and investigating the air quality using statistical (machine learning methods) and deterministic models (WRF and CMAQ). I am interested in working with large datasets to recognize the patterns in data and explore the relationships between variables.

Keywords: Air quality and modeling; machine learning; computer vision; personal exposure; data assimilation; high-performance computing; GPU computing; air quality; climate.

Relevant Experience

GPU Assisted Image Processing for High Resolution Traffic Footage, EE217 GPU Architecture and Programming

Modeling and Evaluating the Expected Waiting Time of M/M/1 and M/G/1 Data Center, EE252 Data Center Architecture

Vehicle Detection, Counting, and Classification, EE228 Introduction to Deep Learning

Vehicular Emission Detection using Image Processing Technique, EE241 Advanced Digital Image Processing

Model an Atmospheric Dispersion to Quantify the Ozone Concentrations in Riverside, CA, ME255 Transport Processes in the Atmospheric Boundary Layer

Awards and Honors

GAANN Fellowship (2019 - 2022)

Esther F. Hays Graduate Fellowship Award (2020 - 2021)

Salim Khan Graduate Award (2019 - 2020)

Outstanding Teaching Assistant Award 2023 in Chemical and Environmental Engineering for 2023

Publications

Ziqi Gao, Cesunica E. Ivey, Charles L. Blanchard, Do K, Sang-Mi Lee, Armistead G. Russell (2023). Emissions and meteorological impacts on PM2.5 species concentrations in Southern California using Generalized Additive Modeling. Science of the Total Environment. http://dx.doi.org/10.2139/ssrn.4377297

Ziqi Gao, Cesunica E. Ivey, Charles L. Blanchard, Do K, Sang-Mi Lee, Armistead G. Russel (2023)l. Emissions, meteorological and climate impacts on PM2.5 levels in Southern California using a generalized additive model: Historic trends and future estimates. Chemosphere. https://doi.org/10.1016/j.chemosphere.2023.138385

Do K, Mahish M, Kashfi Yeganeh A, Gao Z, Blanchard CL, and Ivey CE (2023). A Machine Learning Approach to Quantify the Impact of Meteorology on Tropospheric Ozone in the Inland Empire, CA. Environmental Science: Atmospheres. https://doi.org/10.1039/D2EA00077F

Md Hasibul Hasan, Haofei Yu, Cesunica Ivey, Ajay Pillarisetti, Ziyang Yuan, Do K, and Yi Li (2023). Unexpected Performance Improvements of Nitrogen Dioxide and Ozone Sensors by Including Carbon Monoxide Sensor Signal. https://doi.org/10.1021/acsomega.2c07734

Gao Z, Ivey CE, Blanchard CL, Do K, Lee S-M, and Russell AG (2022). Separating emissions and meteorological impacts on peak ozone concentrations in Southern California using generalized additive modeling. Environmental Pollution, 119503. https://doi.org/10.1016/j.envpol.2022.119503

Ziqi Gao, Yifeng Wang, Petros Vasilakos, Cesunica E. Ivey, Do K, and Armistead G. Russell (2022) Predicting peak daily maximum 8h ozone and linkages to emissions and meteorology in Southern California using machine learning methods. Geoscientific Model Development, 10.5194/gmd-15-9015-2022

Do K, Yu H, Velasquez J, Grell-Brisk M, Smith H, and Ivey CE (2021). A data-driven approach for characterizing community scale air pollution exposure disparities in inland Southern California. Journal of Aerosol Science, Article 105704. https://doi.org/10.1016/j.jaerosci.2020.105704

Ivey CE, Gao Z, Do K, Kashfi Yeganeh A, Russell AG, Blanchard CL, and Lee S-M (2020). Impacts of the 2020 COVID-19 Shutdown Measures on Ozone Production in the Los Angeles Basin. ChemRxiv. Preprint. https://doi.org/10.26434/chemrxiv.12805367.v1

Technical Reports

Ivey CE, Nguyen A, Xu RM, Do K, Hao P, and Barth M. Hyperlocal Monitoring of Traffic-Related Air Pollution to Assess Near-Term Impacts of Sustainable Transportation Interventions, final report. National Center for Sustainable Transportation, Contract No. DOT 69A3551747114, Feb 2023.

Ivey CE, Blanchard CL, Russell AG, Gao Z, and Do K. Ozone Meteorology Study, final report. South Coast Air Quality Management District, Contract No. 20058, January 2022.

Conference Activities

Presenter

Superior Performance of Convolutional Neural Network for Predicting PM2.5 Concentrations and Exceedances under Sparse Data Availability Poster Presentation at AEESP 2023, Boston, MA, June 2023.

A Machine Learning Approach to Quantify the Impact of Meteorology on Tropospheric Ozone in the Inland Empire, CA Oral presentation at 21st Annual CMAS Conference, Chapel Hill, NC, October 2022.

GPU-Assisted CMAQ Simulations Oral presentation at 21st Annual CMAS Conference, Chapel Hill, NC, October 2022.

Applying Machine Learning and Chemical Transport Model to investigate the influences of meteorology on PM2.5 and Ozone Poster Presentation at 38th Meeting of the American Association for Aerosol Research, October 2020.

High-Resolution Personal Exposure Monitoring of PM2.5 in Inland Southern California Poster Presentation at 37th Meeting of the American Association for Aerosol Research, Portland, Oregon, October 2019.

Co-author

Air Pollution Exposure Mitigation for the Protection of Impacted Communities Oral Presentation at AEESP 2023, Boston, MA, June 2023.

Vertical Ozone Profiles Measurement in Riverside, CA Poster Presentation at AGU Fall Meeting 2021, December 2021.

Application of Machine Learning for Future Air Quality Predictions in Southern California. Poster Presentation at 38th Meeting of the American Association for Aerosol Research, October 2020.

High-Temporal Resolution Personal Exposure Pilot Study in Inland Southern California. Poster Presentation at the Air Sensors International Conference, Pasadena, CA, May 2020.

CMAQ-Enhanced Estimates of Personal Exposure to Diesel Particulates. Poster Presentation at the Community Modeling and Analysis System Meeting, Chapel Hill, NC, October 2019.

Field Evaluation and calibration of a Six-parameter Low-cost Sensor System in Northwestern and Southeastern US Poster Presentation at the 37th Meeting of the American Association for Aerosol Research, October 2019.

Service Activities

Reviewer, Environmental Science & Technology, Nature sustainability

Teacher Assistant (CHE 117 Separation Processes, enrollment of 49), University of California, Riverside (2022)

Technical advisor to Center for Community Action and Environmental Justice (Jurupa Valley, CA) and The Air I Breathe (Colton, CA)

Community outreach in San Bernardino, CA