Yunsoo Choi - University of Houston
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Yunsoo Choi

Yunsoo Choi

Yunsoo Choi

Curriculum Vitae pdf

Professor of Atmospheric Chemistry, AI Deep Learning (Machine Learning), Air quality modeling, Satellite Remote Sensing

Ph.D. 2007, Atmospheric Chemistry and Remote Sensing, Georgia Institute of Technology, Atlanta, Georgia 
M.S. 1999, Biophysical Chemistry, University of California, Irvine, California
M.S. 1996, Physical Chemistry, Hanyang University, Seoul, Korea
B.S. 1994, Chemistry, Hanyang University, Seoul, Korea

Office: 426F SR1
Phone: (713) 743-9748
ychoi6@uh.edu

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Editorship

  • Editor of Asia-Pacific Journal of Atmospheric Sciences
  • Editorial Board of Scientific Reports - Nature

Research Interests

Artificial Intelligence Machine Learning (Deep Learning talk at NASA Ames Research Center in July 2019) YouTube: AI-Deep Learning Modeling for Forecasting Ozone Air Quality, Weather, and Remotely Sensed AOD

We have performed AI deep learning modeling for air quality forecasting, pollen forecasting, Hurricane forecasting, climate change modeling, energy availability, electric vehicle impact, remote sensing data treatment, and twin-model development of all other physical and dynamical models.

Air Quality forecasting applications

It is often imperative to forecast air pollution in real-time so that communities can be forewarned to take decisions beforehand. For example, if there is significant pollution on a highway, drivers can decide to take a cleaner route. The primary objectives of this research interest in this space are:

  • To establish a real-time Air Quality Forecasting system over the continental United States to forecast hourly concentrations of gas-phase (g., ozone) and particle-phase species (e.g., PM2.5)
  • to analyze the model using archived ground and/or aircraft observations
  • Investigate the relationship between air pollution and human health, and
  • Use model results as inputs an optimized air pollution regulatory policy to mitigate the loss by medical costs and reduced efficiency in working.

Regional Chemical Transport Modeling applications

Multi-year trends in tropospheric ozone and aerosol concentrations are the results of both inter-annual variations of meteorological conditions by climate change and their precursor’s emission changes due to regulatory policy implementation. The goals of this research interest are:

  • Develop a reasonable approach to identify the impact of climate change and wildfires on ozone and particulate matter chemistry
  • Quantify the contributions of anthropogenic and natural sources (e.g., biogenic. soil, and lightning sources) on chemical and radiative tracers
  • Identify how regional transport of ozone and particulate matter have changed over the last decade

Remote Sensing applications

Data from satellite measurements is crucial for establishing a long-term monitoring system of the Earth’s health. We are targeting these atmospheric remote sensing data: 1) OMI, 2) GOME-2, 3) TES, 4) MLS, 5) MODIS, 6) MISR, 7) MOPITT, and 7) NOAA-16. These remote sensing data will be used to investigate the impacts of cloud convection, lightning activity, biogenic change, air pollution regulation, energy resource change, ocean/atmospheric phenomena (e.g., IOD, El Nino, and monsoon) and extreme weather (e.g., heat waves, typhoons, and hurricanes) on air pollution and atmospheric environment.

Energy Policy

Emissions from transportation sources, both gasoline and diesel vehicles, are a major source of both ozone and particulate matter pollution in urban regions. It is therefore imperative to understand the physical processes driving these emissions so as to develop appropriate control policy. We worked on a project in collaboration with the United States Environmental Protection Agency (USEPA) to explore the effects of temperature and driving conditions on gasoline exhaust VOC speciation – the results are currently being used to update the EPA’s SPECIATE emissions database. We also evaluated the effects of motor vehicle electrification under the influence of a greening grid on air quality and health impacts over the Houston Metropolitan Area, and are currently exploring changes in fuel cost savings and other related costs (e.g., externalities and social costs) due to electrification. We are also interested in wind and solar energy and are currently working on developing machine learning approaches to accurately predict generation over a given region.

Teaching Subjects

  • Deep learning for big data analytics
  • Introduction to climate change
  • Principles of atmospheric science
  • Numerical modeling in atmospheric modeling
  • Atmospheric modeling
  • Dynamic meteorology
  • Geophysical fluid dynamics
  • Mesoscale meteorology
  • Mesoscale meteorology forecasting

Group Members

  • Arman Pouyaei (Postdoctoral Scientist): CMAQ and WRF-Chem modeling
  • Ahmed Khan Salman (Ph.D. student): AI deep learning air quality/weather modeling
  • Ali Mousavinezhad (Ph.D. student): WRF-Chem and CMAQ modeling
  • Masoud Ghahremanloo (Ph.D. student): remote sensing and AI modeling
  • Jincheol Park (Ph.D. student): CMAQ modeling and remote sensing studies
  • Farah Jeba (Ph.D. student): WRF+Chem modeling for atmospheric chemistry
  • Mahmoudreza Momeni (Ph.D. student): CMAQ 4dvar and WRF-Chem 3dvar modeling
  • Arash Kashfi Yeganeh (Ph.D. student): CMAQ 4dvar and DDM modeling
  • Deveshwar Singh (Ph.D. student): AI DNN weather, air quality, and climate modeling
  • Delaney Nelson (Ph.D. student): Statistical modeling and source apportionment modeling
  • Hadi Zanganeh Kia (Ph.D. student): CFD modeling and AI modeling
  • Mahsa Payami (Ph.D. student): AI DNN weather and air quality modeling
  • Sagun Kayastha (Ph.D. student): AI DNN weather and air quality modeling
  • Rijul Dimri (Ph.D. student): WRF modeling and AI DNN modeling
  • Nima Khorshidian (Ph.D. student): WRF-Chem and CMAQ modeling
  • Reyhaneh Shams (Ph.D. student): AI DNN weather and air quality modeling
  • Shihab Shahriar (Ph.D. student): Deep learning modeling for air quality/weather forecasting

Visiting Scholars

  • Dr. Soon-Hwan Lee: Professor at Pusan National University
  • Dr. Sang Keen Song: Professor at Jeju National University

Student Opportunities 
Outstanding students who are interested in Ph.D. degree in AI Deep Learning (Machined Learning), Atmospheric Chemistry, Data Assimilation, Remote Sensing in atmospheric science are encouraged to contact us at  ychoi6@uh.edu with your CV to inquire about RA availability.

Publications (Peer-Reviewed Journals)

  • Momeni, Mahmoudreza, Choi, Yunsoo, Yeganeh, Arash Kashfi, Pouyaei, Arman, Jung, Jia, Park Jincheol, Shephard, Mark W., Dammers, Enrico, and Cady-Pereira, Karen, Constraining East Asia ammonia emissions through satellite observations and iterative finite difference mass balance (iFDMB) and investigating its impact on inorganic fine particulate matter, 2024, Environment International, https://doi.org/10.1016/j.envint.2024.108473
  • Singh, D., Choi, Y., Park, J., Salman, A.K., Sayeed, A., and Song, C.H., Deep-BCSI: A deep learning-based framework for bias correction and spatial imputation of PM2.5 concentrations in South Korea, 2024, Atmospheric Research,
    https://doi.org/10.1016/j.atmosres.2024.107283
  • Koo, Y-S., Choi, Y., and Ho, C-H., Air quality forecasting using big data and machine learning algorithms, 2023, APJAS, https://doi.org/10.1007/s13143-023-00347-z
  • Mousavinezhad, S., Choi, Y., Khorshidian, N., Ghahremanloo, M., and Momeni, M., Air Quality and health co-benefits of vehicle electrification and emission controls in the most populated United States urban hubs: insights from New York, Los Angels, Chicago, and Houston, 2023, Science of The Total Environment, https://doi.org/10.1016/j.scitotenv.2023.169577
  • Salman, A.K., Choi, Y., Park, J., Mousavinezhad, S., Payami, M., Momeni, M., and Ghahremanloo, M., Deep learning based emulator for simulating CMAQ surface NO2 levels over the CONUS, 2024, Atmospheric Environment, https://doi.org/10.1016/j.atmosenv.2023.120192
  • Salman, A.K., Choi, Y., Park, J., Mousavinezhad, S., Payami, M., Momeni, M., and Ghahremanloo, M., Deep learning based emulator for simulating CMAQ surface NO2 levels over the CONUS, 2024, Atmospheric Environment, https://doi.org/10.1016/j.atmosenv.2023.120192
  • Shams, S.R., Kalantary, S., Jahani, A., Shams, S. M., P., Kalantari, B., Singh, D., Moeinnadini, M., and Choi, Y., Assessing the effectiveness of artificial neural networks (ANN) and multiple linear regressions (MLR) in forecasting AQI and PM10 and evaluating health impacts through AirQ+ (case study: Tehran), 2023, Environmental Pollution, https://doi.org/10.1016/j.envpol.2023.122623
  • Nelson, D., Choi, Y., Sadeghi, B., Yeganeh, A.K., Ghahremanloo, M., Park, J., 2023, A comprehensive approach combining positive matrix factorization modeling, meteorology, and machine learning for source apportionment of surface ozone precursors: Underlying factors contributing to ozone formation in Houston, Texas, Environmental Pollution, https://doi.org/10.1016/j.envpol.2023.122223
  • Park, J., Jung, J., Choi, Y., Lim, H., Kim, M., Lee, K., Lee, Y.G., and Kim, J., 2023, Satellite-based, top-down approach for the adjustment of aerosol precursor emissions over East Asia: TROPOMI NO2 product, and the Geostationary Environment Monitoring Spectrometer (GEMS) AOD data fusion product and its proxy, Atmospheric Measurement Techniques, https://doi.org/10.5194/amt-16-3039-2023
  • Pouyaei, A., Mizzi, A., Choi, Y., Mousavinezhad, S., Khorshidian, N., 2023, Downwind ozone changes of the 2019 Williams Flats wildfire: Insights from WRF-chem/DART assimilation of OMI NO2, HCHO, and MODIS AOD retrievals, Journal of Geophysical Research Atmosphere, DOI: 10.1029/2022JD038019
  • Singh, D., Choi, Y., Dimri, R., Ghahremanloo, M., Pouyaei, A., 2023, An intercomparison of Deep-Learning Methods for Super-Resolution Bias-Correction (SRBC) of Indian Summer Monsoon Rainfall (ISMR) using CORDEX-SA Simulations, Asia-Pacific Journal of Atmospheric Sciences, DOI: 10.1007/s13143-023-00330-8
  • Kia, H.Z., Choi, Y., Nelson, D., Park, J., Pouyaei, A., 2023, Large eddy simulation of sneeze plumes and particles in a poorly ventilated outdoor air condition: A case study of the University of Houston main campus, Science of The Total Environment, DOI: 10.1016/j.scitotenv.2023.164694
  • Lops, Y., Ghahremanloo, M., Pouyaei, A., Choi, Y., Jung, J., Mousavinezhad, S., Salman, A., an Hammond, D., 2023, Spatiotemporal estimation of TROPOMI NO2 column with depthwise partial convolutional neural network, Neural Computing and Applications, DOI: 10.1007/s00521-023-08558-1
  • Ghahremanloo, M., Choi, Y., and Lops, Y., 2023, Deep learning mapping of surface MDA8 ozone: The impact of predictor variables on ozone levels over the contiguous United States, Environmental Pollution, https://doi.org/10.1016/j.envpol.2023.121508
  • Mousavinezhad, S., Ghahremanloo, M., Choi, Y., Pouyaei, A., Khorshidian, N., and Sadeghi, B., 2023, Surface ozone trends and related mortality across the climate regions of the contiguous United States during the most recent climate period, 1991-2020, Atmospheric Environment, https://doi.org/10.1016/j.atmosenv.2023.119693
  • Lops, Y., Choi, Y., Mousavinezhad, S., Salman, A.K., Nelson, D.L., and Singh, D., 2023, Development of deep convolutional neural network ensemble models for 36-month ENSO forecasts, Asia-Pacific Journal of Atmospheric Science, DOI : 10.1007/s13143-023-00319-3
  • Ghahremanloo, M., Lops, Y., Choi, Y., Mousavinezhad, S., and Jung, J., 2023, A coupled deep learning model for estimating surface NO2 levels from remote sensing data: 15-year study over the contiguous United States, Journal of Geophysical Research-Atmosphere, DOI: 10.1029/2022JD037010
  • Pan, S., Gan, L., Jung, J., Yu, W., Roy, A., Diao, L., Jeon, W., Souri, A.H., Gao, H.O., and Choi, Y., 2023, Quantifying the premature mortality and economic loss from wildfire-induced PM2.5 in the contiguous U.S., Science of the Total Environment, https://doi.org/10.1016/j.scitotenv.2023.162614
  • Pan, S., Yu, W., Fulton, L.M., Jung, J., Choi, Y., Gao, H.O., 2023, Impacts of the large-scale use of passenger electric vehicles on public health in 30 US. metropolitan areas, Renewable and Sustainable Energy Reviews, https://doi.org/10.1016/j.rser.2022.113100
  • Lee, K., Kim, M., Choi, M., Kim, J., Choi, Y., Jeong, J., Moon, K-J., Lee, S., 2022, Fast and operational gap filing in satellite-derived aerosol optical depths using statistical techniques, Journal of Applied remote sensing, https://doi.org/10.1117/1.JRS.16.044507
  • Mun, J., Choi, Y., Jeon, W., Lee. H.W., Kim, C-H., Park, S-Y., Bak, J., Jung, J., Oh, I., Park, J., and Kim, D., 2022, Assessing mass balance-based inverse modeling methods via a pseudo-observation test to constrain NOx emissions over South Korea, Atmospheric Environment, https://doi.org/10.1016/j.atmosenv.2022.119429
  • Sayeed, A., Choi, Y., Pouyaei, A., Lops, Y., Jung, J., Salman, A.K., 2022, CNN-based model for the spatial imputation (CMSI version 1.0) of in-situ ozone and PM2.5 measurements, Atmospheric Environment, https://doi.org/10.1016/j.atmosenv.2022.119348
  • Sadeghi, B., Ghahremanloo, M., Mousavinezahd, A., Lops, Y., Pouyaei, A., and Choi, Y., 2022, Contributions of meteorology to ozone variations: Application of deep learning and the Kolmogorov-Zurbenko filter, Environmental Pollution, DOI: 10.1016/j.envpol.2022.119863
  • Salman, A.H., Pouyaei, A., Choi, Y., Lops, Y., Sayeed, A., 2022, Deep learning solver for solving Advection-Diffusion Equation in comparison to Finite Difference Methods, Communications in Nonlinear Science and Numerical Simulation, https://doi.org/10.1016/j.cnsns.2022.106780
  • Park, J., Jung, J., Choi, Y., Mousavinezhad, S., Pouyaei, A., 2022, The sensitivities of ozone and PM2.5 concentrations to the satellite-derived lead area index over East Asia and its neighboring seas in the WRF-CMAQ modeling system, Environmental Pollution, 306, 119419, https://doi.org/10.1016/j.envpol.2022.119419
  • Pouyaei, A., Choi, Y., Jung, J., Mousavinezahd, S., Momeni, M., Song, C.H., 2022, Investigating the long-range transport of particulate matter in East Asia: Introducing a new Lagrangian diagnostic tool, Atmospheric Environment, DOI: 10.1016/j.atmosenv.2022.119096
  • Sadeghi, B., Pouyaei, A., Choi, Y., Rappenglueck, B., 2022, Influence of seasonal variability on source characteristics of VOCs at Houston industrial area, Atmospheric Environment, https://doi.org/10.1016/j.atmosenv.2022.119077
  • Jung, J., Choi, Y., Souri, A.H., Mousavinezhad, A., Sayeed, A., Lee, K., 2022, The impact of springtime-transported air pollutants on local air quality with satellite-constrained NOx emission adjustments over East Asia, Journal of Geophysical Research-Atmosphere, 10.1029/2021JD035251
  • Jung, J., Choi, Y., Mousavinezhad, S., Kang, D., Park, J., Pouyaei, A., Ghahremanloo, M., Momeni, M., and Kim, H., 2022, Changes in the ozone chemical regime over the contiguous United States inferred by the inversion of NOx and VOC emissions using satellite observation, Atmospheric Research, https://doi.org/10.1016/j.atmosres.2022.106076
  • Sayeed, A., Eslami, E., Lops, Y., and Choi, Y., 2022, CMAQ-CNN: A new-generation of post-processing techniques for chemical transport models using deep neural networks, Atmospheric Environment, https://doi.org/10.1016/j.atmosenv.2022.118961
  • Ghahremanloo, M., Lops, Y., Choi, Y., Jung, J., Mousavinezhad, S., and Hammond, D., 2022, A comprehensive study of the COVID-19 impact on PM2.5 levels over the contiguous United States: A deep learning approach, Atmospheric Environment, https://doi.org/10.1016/j.atmosenv.2022.118944
  • Lee, S., Song, C.H., Han, K.M., Henze, D.K., Lee, K., Yu, J., Woo, J-H., Jung, J., Choi, Y., Saide, P.E., and Carmichael, G.R., 2021, Impacts of uncertainties in emissions on aerosol data assimilation and short-term PM2.5 predictions over Northeast Asia, Atmospheric Environment, doi.org/10.1016/j.atmosenv.2021.118921
  • Ghahremanloo, M., Lops, Y., Choi, Y., and Yeganeh, B., 2021, Deep learning estimation of daily ground-level NO2 concentrations from remote sensing data, Journal of Geophysical Research-Atmosphere
  • Yeo, I. and Choi, Y., 2021, An efficient method for capturing the high peak concentrations of PM2.5 using Gaussian-Filtered deep learning, Sustainability, doi: 10.3390/su132111889
  • Sayeed, A., Choi, Y., Jung, J., Lops, Y., Eslami, E., Salman, A., 2021, A Deep Convectional Neural Network Model for improving WRF simulations, IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2021.3100902
  • Lops, Y., Pouyaei, A., Choi, Y., Jung, J., Salman, A., Sayeed, A., 2021, Application of a Partial Convolutional Neural Network for Estimating Geostationary Aerosol Optical Depth Data, Geophysical Research Letters, DOI: 10.1029/2021GL093096
  • Jeon, W., Park, Choi, Y., Mun, J., Kim, D., Kim, C., Lee, H., Bak, J., Jo, H., 2021, The mechanism of the formation of high sulfate concentrations over the Yellow Sea during the KORUS-AQ period: The effect of transport/atmospheric chemistry and ocean emissions, Atmospheric Research, https://doi.org/10.1016/j.atmosres.2021.105756
  • Pan, S., Fultion, L.W., Roy, A., Jung, J., Choi, Y., Gao, H.O., 2021, Shared use of electric autonomous vehicles: Air quality and health impacts of future mobility in the United States, Renewable and Sustainable Energy Reviews, 149, 111380, https://doi.org/10.1016/j.rser.2021.111380
  • Pouyaei, A., Sadeghi, B., Choi, Y., Jung, J., Souri, A.H., Zhao, C., and Song, C.H., 2021, Development and implementation of a physics-based convective mixing scheme in the CMAQ modeling framework, Journal of Advances in Modeling Earth System, DOI: https://doi.org/10.1029/2021MS002475
  • Yeo, I., Choi, Y., Lops, Y., and Sayeed, A., 2021, Efficient PM2.5 forecasting using geographical correlation based on integrated deep learning algorithms, Neural Computing and Applications, DOI: https://doi.org/10.1007/s00521-021-06082-8
  • Sayeed, A., Choi, Y., Eslami, E., Jung, J., Lops, Y., Salman, A.K., Lee, J., Park, H., Choi, M., 2021, A novel CMAQ-CNN hybrid model to forecast hourly surface-ozone concentrations 14 days in advance, Scientific Reports, DOI: https://doi.org/10.1038/s41598-021-90446-6
  • Sayeed, A., Lops, Y., Choi, Y., Jung, J., and Khan, A., 2021, Bias correcting and extending the PM forecast by CMAQ up to 7 days using deep convolutional neural networks, Atmospheric Environment, https://doi.org/10.1016/j.atmosenv.2021.118376
  • Mousavinezhad, S., Choi, Y., Pouyaei, A., Ghahremanloo, M., Nelson, D.L., 2021, A comprehensive investigation of surface ozone pollution in China, 2015-2019: Separating the contributions from meteorology and precursor emissions, Atmospheric Research, DOI: 10.1016/j.atmosres.2021.105599
  • Jung, J., Choi, Y., Wong, D.C., Nelson, D., and Lee, S., 2021, Role of sea fog over the Yellow Sea on air quality with the direct effects of aerosols, Journal of Geophysical Research-Atmosphere, https://doi.org/10.1029/2020JD033498
  • Ghahremanloo, M., Choi, Y., Sayeed, A., Salman, A.K., Pan, S., and Amani, M., 2021, Estimating daily high-resolution PM2.5 concentrations over Texas: machine learning approach, Atmospheric Environment. https://doi.org/10.1016/j.atmosenv.2021.118209
  • Song, S.-K., Choi, Y., Choi, Y., Flynn, J., and Sadeghi B., 2020, Characteristics of aerosol chemical components and their impacts on direct radiative forcing at urban and suburban locations in Southeast Texas, Atmospheric Environment, doi.org/10.1016/j.atmosenv.2020.118151
  • Eslami, E., Choi, Y., Lops, Y., Sayeed, A., and Salman, A.H., 2020, Using wavelet transform and dynamic time warping to identify the limitations of the CNN model as an air quality forecasting system, Geosci. Model Dev., 13, 6237-6251
  • Ghahremanloo, M., Lops, Y., Choi, Y. and Mousavinezhad, S., 2020, Impact of the COVID-19 outbreak on air pollution levels in East Asia, Science of The Total Environment, https://doi.org/10.1016/j.scitotenv.2020.14226
  • Pan, S., Jung, J., Li, Z., Hou, X., Roy, A., Choi, Y., and Gao, H.O., 2020, Air quality implications of COVID-19 in California, Sustainability, 12 (17), 10.3390/su12177067
  • Pouyaei, A., Choi, Y., Jung, J., Sadeghi, B., and Song, C.H., 2020, Concentration Trajectory Route of Air pollution with an Integrated Langrangian model (C-TRAIL Model v1.0) derived from the Community Multiscale Air Quality Model (CMAQ Model v5.2), Geosci. Model Dev., 13, 3498-3505
  • Souri, A.H., Choi, Y., Kodros, J.K., Jung, J., Shpund, J., Pierce, J.R., Lynn, B.H., Khain, A., and Chance, K., 2020, Response of Hurricane Harvey's rainfall to anthropogenic aerosols: A sensitivity study based on spectral bin microphysics with simulated aerosols, Atmospheric Research, doi.org/10.1016/j.atmosre.2020.104965
  • Sadeghi, B., Choi, Y., Yoon, S., Flynn, J., Kotsakis, A., Lee, S., 2020, The characterization of fine particulate matter downwind of Houston: Using integrated factor analysis to identify anthropogenic and natural sources, Environmental Pollution, doi.org/10.1016/j.envpol.2020.114345
  • Lops, Y., Choi, Y., Eslami, E., and Sayeed, A., 2019, Real-time 7-Day Forecast of Pollen Counts Using a Deep Convolutional Neural Network, Neural Computing and Applications, doi:10.1007/s00521-019-04665-0
  • Jeon, W., Lee, H.W., Lee, T-J., Yoo, J-W., Mun, J., Lee, S-H., Choi, Y., 2019, Impact of varying wind patterns on PM10 concentrations in the Seoul Metropolitan Area in South Korea from 2012 to 2016, JAMC, doi:10.1175/JAMC-D-19-0102.1
  • Sayeed, A., Choi, Y., Eslami E., Lops, Y., Roy, A., Jung, J., 2019, Using a deep convolutional neural network to predict 2017 ozone concentrations, 24 hours in advance, Neural Networks,  doi.org/10.1016/j.enunet.2019.09.033
  • Kim, J. et al., Choi, Y., 2019, New Era of Air Quality monitoring from space: Geostationary Environment Monitoring Spectrometer (GEMS), Bulletin of the American Meteorological Society, doi:10.1175/BAMS-D-18-0013.1
  • Jung, J., Souri, A.H., Wong, D. C., Lee, S., Jeon, W., Kim, J., and Choi, Y., 2019, The impact of the direct effect of aerosols on meteorology and air quality using aerosol optical depth assimilation during the KORUS-AQ campaign, Journal of Geophysical Research-Atmosphere, doi:10.1029/2019JD030641
  • Pan,S., Roy, A., Choi, Y., Sun, S., and Gao, H.O., 2019, The air quality and health impacts of projected long-haul truck and rail freight transportation in the United States in 2050, Environment International, doi:10.1016/j.envint.2019.104922
  • Eslami, E., Choi, Y., Lops, Y., and Sayeed, A., 2019, A real-time hourly ozone production system using deep convolutional neural network, in Neural Computing and Applications, doi:10.1007/s00521-019004282-x
  • Eslami, E., Salman, A.K., Choi, Y., Sayeed, A., Lops, Y., 2019, A data ensemble approach for real-time air quality forecasting using extremely randomized trees and deep neural networks, in Neural Computing and Applicaiton, doi:10.1007/s00521-019-04287-6
  • Pan, S., Roy, A., Choi, Y., Eslami, E., Thomas, S., Jiang, X., Gao, H.O., 2019, Potential impacts of electric vehicles on air quality and health endpoints in the Greater Houston Area in 2040, Atmospheric Environment, 207,38-51
  • Kotsakis, A., Choi, Y., Souri, A.H., Jeon, W., and Flynn, J., 2019, Characterization of Regional Wind Patterns Using Self-Organizing Maps: Impact on Dallas-Fort Worth Long-Term Ozone Trends, JAMC, doi:10.1175/JAMC-D-18-0045.1
  • Jeon, W., Choi, Y., Mun, J., Lee S-H., Choi, H-J., Yoo, J-W., Lee, H-J., Lee, H-W., 2018, Behavior of sulfate on the sea surface during its transport from Eastern China to South Korea, doi:10.1016/j.atmosenv.2018.05.017
  • Roy, A., Choi, Y., Souri, A.H., Jeon, W., Diao, L., Pan, S., Westenbarger, D., 2018, Effects of biomass burning emissions on air quality over the continental USA: a three-year comprehensive evaluation accounting for sensitivities due to boundary conditions and plume rise height, Environmental Contaminants, doi:10.1007/978-981-10-7332-8_12
  • Souri, A.H., Choi, Y., Pan, S., Curci, G., Nowlan, C., Janz, S. J., Kowalewski, M.G., Liu, J., Herman, J.R., Weinheimer, A.J., 2018, First Top-Down Estimates of Anthropogenic NOx Emissions Using High-Resolution Airborne Remote Sensing Observations, Journal of Geophysical Research-Atmosphere, doi10.1002/2017JD028009
  • Souri, A.H., Choi, Y., Jeon, W., Kochanski, A., Diao, L., Mandel, J., Bhave, P.V., and Pan, S., 2017, Quantifying the impact of biomass burning emissions on major inorganic aerosols and their precursors in the US, Journal of Geophysical Research-Atmosphere, doi:10.1002/2017JD026788
  • Pan, S., Choi, Y., Roy, A., and Jeon, W., 2017, Allocating emissions to 4km and 1km horizontal spatial resolutions and its impact on simulated Nox and O3 in Houston, TX, in Atmospheric Environment, 164, 398-415
  • Jeon, W., Choi, Y., Souri, A.H., Roy, A., Diao, L., Pan, S., Lee, H.W., Lee, S-H., 2017, Identification of chemical fingerprints in long-range transport of burning induced upper tropospheric ozone from Colorado to the North Atlantic Ocean, Science of The Total Environment, 613-614, 820-828,  doi.org/10.1016/j.scitotenv.2017.09.177
  • Jeon, W., Choi, Y., Roy, A., Pan, S., Price, D., Hwang, M-K., Kim, K. R., and Oh, I., 2017, Investigation of Primary Factors Affecting the Variation of Modeled Oak Pollen Concentrations: A case study for Southeast Texas in 2010, accepted in Asia-Pacific Journal of Atmospheric Sciences
  • Kotasakis, A., Morris, G.A., Lefer, B., Jeon, W., Roy, A., Minschwaner, K., Thompson, A., Choi, Y., 2017, Ozone production by corona discharges during a convective event in DISCOVER-AQ Houston, in Atmospheric Environment, doi:10.1016/j.atmosenv.2017.04.018
  • Leong, Y., Sanchez, N., Wallace, H., Karakurt Cevik, B., Hernandez, C., Han, Y., Flynn, J., Massoli, P., Floerchinger, C., Fortner, E., Herndon, S., Bean, J., Hildebrandt Ruiz, L., Jeon, W., Choi, Y., Lefer, B., Griffin, R., 2017, Overview of Surface measurements and Spatial Characterization of Submicron Particulate matter during the DISCOVER-AQ 2013 Campaign in Houston, in press in Journal of Air & Waste Management Association
  • Souri, A.H., Choi, Y., Jeon, W., Woo, J-H., Zhang, Q., Kurokawa, J-I., 2017, Remote-sensing evidence of decadal changes in major tropospheric ozone precursors over East Asia, in Journal of Geophysical Research-Atmosphere, doi:10.1002/2016JD025663
  • Roy Anirban, Yunsoo Choi, 2016, Effect of ambient temperature on species lumping for total organic gases in gasoline exhaust emissions, in Atmospheic Environment, doi:10.1016/j.atmosenv.2016.11.057
  • Shuai Pan, Yunsoo Choi, Wonbae Jeon, Anirban Roy, David A. Westenbarger, Hyun Cheol Kim, 2016, Impact of high-resolution sea surface temperature, emission spikes and wind on simulated surface ozone in Houston, Texas during a high ozone episode, in Atmospheric Environment, doi:10.1016/j.atmosenv.2016.12.030
  • Wonbae Jeon, Yunsoo Choi, Peter Percell, Amir Hossein Souri, Chang-Keun Song, Soon-Tae Kim, and Jhoon Kim, 2016, Computationally efficient air quality forecasting tool: implementaion of STOPS v1.5 model into CMAQ v5.0.2 for a prediction of Asian dust, Geoscientific Model Development 9(10): 3671-3684, doi:10.5194/gmd-9-3671-2016.
  • Lijun Diao, Yunsoo Choi, Beata Czader, Xiangshang Li, Shuai Pan, Anirban Roy, Amir Hossein Souri, Mark Estes, and Wonbae Jeon, 2016, Discrepancies between modeled and observed nocturnal isoprene in an urban enviornment and the possible causes: A case study in Houston, Atmospheric Research, 181, 257-264.
  • Wonbae Jeon, Yunsoo Choi, Peter Percell, Amir Hossein Souri, Chang-Keun Song, Soon-Tae Kim, and Jhoon Kim, 2016, The new implementation of a computationally efficient modeling tool (STOPS v1.5) into CMAQ v5.0.2 and its application for a more accurate prediction of Asian Dust, 2016, Geoscientific Model Development Discussion, doi:10.5194/gmd-20160180.
  • Effect of Ambient Temperature on Total Organic Gas Speciation Profiles from Light-Duty Gasoline Vehicle Exhaust, Anirban Roy, Darrel Soontag, Richard Cook, Catherine Yanca, Charles Schenk, and Yunsoo Choi, Environmental Science and Technology, 2016, 50(12), 6565-6573, pubs.acs.org/doi/abs/10.1021/acs.est.6b01081.
  • The impact of observation nudging on simulated meteorology and ozone concentrations during DISCOVER-AQ 2013 Texas campaign, Xiangshang Li, Yunsoo Choi, Beata Czader, Anirban Roy, Hyuncheol Kim, Barry Lefer, and Shuai Pan, 2016, Atmospheric Chemistry and Physics, 16, 3127-3144, www.atmos-chem-phys.net/16/3127/2016/.
  • Souri, Amir Hossein, Choi, Yunsoo, Jeon, Wonbae, Li, Xiangshang, Pan, Shuai, Diao, Lijun, Westenbarger, David A., 2016, Constraining NOx emissions using satellite NO2 measurements during 2013 DISCOVER-AQ Texas campaign, in Atmospheric Environment, v. 131, p. 371-381, doi:10.1016/j.atmosenv.2016.02.020
  • Souri, Amir Hossein, Choi, Yunsoo, Li, Xiangshang, Kotsakis, Alexander, Jiang, Xun, 2016, A 15-year climatology of wind pattern impacts on surface ozone in Houston, Texas, in Atmospheric Research, v. 174, p. 124-134, doi:10.1016/j.atmosres.2016.02.007
  • Diao, Lijun, Roy, Anirban, Czader, Beata, Pan, Shuai, Jeon, Wonbae, Souri, Amir Hossein, Choi, Yunsoo, 2016, Modeling the effect of relative humidity on nitrous acid formation in the Houston area, in Atmospheric Environment.
  • Jeon, Wonbae, Choi, Yunsoo, Lee, Hwa Woon, Lee, Soon-Hwan, Yoo, Jung-Woo, Park, Jaehyeong, Lee, Hyo-Jung, 2015, A quantitative analysis of grid nudging effect on each process of PM2.5 production in the Korean Peninsula, in Atmospheric Environment, doi:10.1016/j.atmosenv.2015.10.050.
  • Li, X., Choi, Y., Czader, B., Kim, H., Lefer, B., Pan, S., 2015, The impact of observation nudging on simulated meteorology and ozone concentrations during DISCOVER-AQ 2013 Texas campaign, in Atmos. Chem. Phys. Discuss., Copernicus Publications, v. 15, no. 19, p. 27357-27404, doi:10.5194/acpd-15-27357-2015
  • Pan, Shuai, Choi, Yunsoo, Roy, Anirban, Li, Xiangshang, Jeon, Wonbae, Souri, Amir Hossein, 2015, Modeling the uncertainty of several VOC and its impact on simulated VOC and ozone in Houston, Texas, in Atmospheric Environment, v. 120, p. 404-416. doi:10.1016/j.atmosenv.2015.09.029
  • Anirban Roy, Yunsoo Choi, 2015, Temperature dependence of source specific volatility basis sets for motor vehicle exhaust, in Atmospheric Environment, v. 119, p. 258-261.
  • Bella, D., Culpepper, J., Khaimova, J., Ahmed, N., Belkalai, Adam, Arroyo, I., Andrews, J., Gentle, S., Emmanuel, S., Lahmouh, M., Ealy, J., King, Zayna, Jenkins, O., Fu, D., Choi*, Y., Osterman, G., Gruszczynski, J., Skeete, D., Blaszczak-Boxe, C.S., 2015, Characterization of pollution transport into Texas using OMI and TES satellite, GIS and in situ data, and HYSPLIT back trajectory analyses: implications for TCEQ State Implementation Plans, in Air Qual Atmos Health, Springer Netherlands, p. 1.
  • Choi*, Yunsoo, Souri**, Amir Hossein, 2015, Chemical condition and surface ozone in large cities of Texas during the last decade: Observational evidence from OMI, CAMS, and model analysis, in Remote Sensing of Environment, v. 168, p. 90-101, doi:10.1016/j.rse.2015.06.026
  • Choi, Y. and Souri, A.H., The seasonal behavior and long-term trends of tropospheric ozone, its precursors and chemical conditions over Iran: a view from space, July 2015, Atmospheric Environment, 106, 232-240
  • Roy, A. and Choi, Y., Potential impact of changing the coal-natural gas split in power plants: an emissions inventory perspective, July 2015, Atmospheric Environment, 102, 413-415
  • Czadedr, B.H., Choi, Y., X. Li, S. Alveraz, and B. Lefer, Impact of updated traffic emissions on HONO mixing ratios simulated for urban site in Houston, Texas, 2015, Atmospheric Chemistry and Phys. 15(3), 1253-1263
  • Czader, B., Percell, P., Byun D., and Choi, Y., Development and evaluation of the Screening Trajectory Ozone Prediction System (STOPS, version1.0), 2014, Geoscientific Model Development Discussions, 7(6), 7619-7649
  • Choi, Y., The impact of satellite-adjusted NOx emissions on simulated NOx and O3 discrepancies in the urban and outflow areas of the Pacific and Lower Middle US, 2014, Atmospheric Chemistry and Physics, 14, 675-690
  • Choi, S., Joiner, J., Choi, Y., Duncan, B., Vasilkov, A., Krotkov, N., Bucsela, E., First Estimate of global free tropospheric NO2 abundances derived using a cloud slicing technique applied to Satellite observations from the Aura Ozone Monitoring Instrument (OMI), 2014, Atmospheric Chemistry and Physics, 14(19), 10565-10588
  • Lee, Y., Kim, J., Ho, C., An, S., Cho, H., Mao, R., Tian, B., Wu, D., Lee, J., Kalashnikova, O., Choi, Y., Yeh, S., The effects of ENSO under negative AO phase on spring dust activity over northern China: An observational investigation, 2014, International Journal of Climatology, DOI:10.1002/joc.4028
  • Yunsoo Choi, Hyuncheol Kim, Daniel Tong, and Pius Lee, Summertime weekly cycles of observed and modeled NOx and O3 concentrations as a function of satellite-derived ozone production sensitivity and land use types over the Continental United States, 2012, Atmospheric Chemistry and Physics, 12, 6291-6307
  • Chun Zhao, Yuhang Wang, Rong Fu, Derek Cunnold, and Yunsoo Choi, Impact of East Asia summer monsoon on the air quality over China: The view from space, 2010, Journal of Geophysical Research, doi:10.1029/2009JD012745.
  • Qing Yang, Derek Cunnold, Yunsoo Choi, Yuhang Wang, Ray Wang, Lucien Froidervaux, Anne Thompson, and  Pawan Bhartia, A study of tropospheric ozone column enhancements over North America using satellite data and a global chemical model, 2010, Journal of Geophysical Research, doi:10.1029/2009JD012616.
  • Yunsoo Choi, Gregory Osterman, Annmarie Eldering, Yuhang Wang, and Eric Edgerton, Understanding the contributions of anthropogenic and biogenic sources to CO enhancements and outflow observed over North America and the western Atlantic Ocean by TES and MOPITT, 2010, Atmospheric Environment, doi:10.1016/j.atmosenv.2010.01.029.
  • Yunsoo Choi, Jinwon Kim, Annmarie Eldering, Gregory Osterman, Yuk L. Yung, and K. N. Liou, Lightning and anthropogenic NOx sources over the U.S. and the western North Atlantic Ocean: Impact on OLR and radiative effects, 2009, Geophysical Research Letters, 36, L17806, doi:10.1029/2009GL039381.
  • Chun Zhao, Yuhang Wang, Yunsoo Choi, and Tao Zeng, Summertime impacts of convective transport and lightning NOX production over North America: modeling dependence on meteorological simulations, 2009, Atmospheric Chemistry and Physics, 9, 4315-4327.
  • Yunsoo Choi, Yuhang Wang, Qing Yang, Derek Cunnold, Tao Zeng, Changsub Shim, Ming Luo, Annmarie Eldering, Eric Bucsela, and James Gleason, Spring to summer northward migration of high O3 over the western North Atlantic, 2008, Geophysical Research Letters, 35, L04818, doi:10.1029/2007GL032276.
  • Yunsoo Choi, Yuhang Wang, Tao Zeng, Derek Cunnold, Eun-Su Yang, Randall Martin, and Kelly Chance, Valerie Thouret, and Eric Edgerton, Springtime transition of NO2, CO, and O3 over North America: Model evaluation and analysis, 2008, Journal of Geophysical Research, 113, D20311, doi:10.1029/2007JD009632.
  • Burcak Kaynak, Yongtao Hu, Randall V. Martin, Armistead Russell, Yunsoo Choi, and Yuhang Wang, The effect of lightning NOX production on surface ozone in the continental United States, 2008, Atmospheric Chemistry and Physics, 8, 5151-5159.
  • Serge Guillas, Jinghui Bao, Yunsoo Choi and Yuhang Wang, Statistical correction and downscaling of chemistry-transport model ozone forecasts over Atlanta, 2008, Atmospheric Environments, 42(6), 1338-1348.
  • Yuhang Wang, Yunsoo Choi, Tao Zeng, Douglas Davis, Martin Buhr, L. Gregory Huey, and William Neff, Assessing the photochemical impact of snow NOx emissions over Antarctica during ANTCI 2003, 2007, Atmospheric Environments, 41(19), 3944.
  • Jing Ping, Derek Cunnold, Yunsoo Choi and Yuhang Wang, Summertime tropospheric ozone columns from Aura OMI/MLS measurements versus regional model results over the United States, 2006, Geophysical Research Letters, 33(17), L17817.
  • Yuhang Wang, Yunsoo Choi, Tao Zeng, Brian Ridley, Nicola Blake, Donald Blake and Frank Flocke, Late-spring increase of trans-Pacific pollution transport in the upper troposphere, 2006, Geophysical Research Letters, 33, L01811.
  • Yunsoo Choi, Yuhang Wang, Tao Zeng, Randall Martin, Thomas Kurosu and Kelly Chance, Evidence of lightning NOX and convective transport of pollutants in satellite observations over North America, 2005, Geophysical Research Letters, 32, L02805.
  • Changsub Shim, Yuhang Wang, Yunsoo Choi, Paul I. Palmer, Dorian S. Abbot and Kelly Chance, Constraining global isoprene emissions with Global Ozone Monitoring Experiment (GOME) formaldehyde column measurements, 2005, Journal of Geophysical Research, 110, D24301.
  • Yuhang Wang, Changsub Shim, Nicola Blake, Donald Blake, Yunsoo Choi, Brian Ridley, Jack Dibb, Anthony Wimmers, Jennie Moody, Frank Flocke, Andrew Weinheimer, Robert Talbot and Elliot Atlas, Intercontinental transport of pollution manifested in the variability and seasonal trend of springtime O3 at northern middle and high latitudes, 2003, Journal of Geophysical Research, 108(D21), 4683.
  • Yunsoo Choi, Scott Elliott, Isobel J. Simpson, Donald R. Blake, Jonah J. Colman, Manvendra K. Dubey, Simone Meinardi, F. Sherwood Rowland, Tomoko Shirai and Felisa A. Smith, Survey of whole air data from the second airborne Biomass Burning and Lightning Experiment using principal component analysis, 2003, Journal of Geophysical Research, 108(D5), 4163.
  • J. Alfredo Freites, Yunsoo Choi and Douglas J. Tobias, Molecular Dynamics Simulations of a Pulmonary Surfactant Protein B Peptide in a Lipid Monolayer, 2003, Biophysical Journal, 84(4), 2169-2180.