Air Quality

RESEARCH

✨ Research Overview

Keywords

Air pollutant, Particulate matter, Nitrogen dioxide, Ozone, Sulfur dioxide, satellite aerosol optical depth, Real-time Learning.

Forecasting

Short-term
PM concentration

Monitoring

Air pollutant
(PM10, PM2.5, NO2, O3, and SO2)

Aerosol optical depth (AOD)

📚 Representative Works

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Aerosol Optical Depth

Aerosol optical depth (AOD) is an essential indicator of the atmospheric condition and is one of the important factors in estimating the concentration of particulate matter (PM). In addition, it influences the calculation of the effect of scattering by the atmosphere, which directly affects the surface reflectance. Currently, we are investigated AOD research: 1) direct estimation of AOD and 2) AOD gap-filling.

A direct AOD modeling based on machine learning
AOD estimation under all-sky conditions

AOD Modeling

The current AOD is calculated by the radiative transfer model, which is very complex and has a lot of computation. AOD has been calculated from satellites by using a Look-Up Table (LUT) created by the radiative transfer model. Therefore, if a model that can calculate AOD in real-time directly from satellite data based on machine learning is developed, it will not only shorten the calculation amount and time but also overcome the part where real-time update that was impossible when using LUT.

Kang et al., 2022

📝 Publications

Last updated : 2023/03/12

Choi, H., Park, S., Kang, Y., Im, J., & Song, S. (2023). Retrieval of hourly PM2. 5 using top-of-atmosphere reflectance from geostationary ocean color imagers I and II. Environmental Pollution, 121169.

Kim, Y., Kang, E., Cho, D., Lee, S., & Im, J.. (2022). Improved Estimation of Hourly Surface Ozone Concentrations using Stacking Ensemble-based Spatial Interpolation. Journal of the Korean Association of Geographic Information Studies, 25(3), 74-99.

Park, S., Im, J., Kim J., & Kim, S.M. (2022). Geostationary satellite-derived ground-level particulate matter concentrations using real-time machine learning in Northeast Asia. Environmental Pollution, v.306, 119425.

Lee, S., Park, S., Lee, M. I., Kim, G., Im, J., & Song, C. K. (2022). Air Quality Forecasts Improved by Combining Data Assimilation and Machine Learning with Satellite AOD. Geophysical Research Letters, 49(1), e2021GL096066.

Kang, Y., Kim, M., Kang, E., Cho, D., & Im, J. (2022). Improved retrievals of aerosol optical depth and fine mode fraction from GOCI geostationary satellite data using machine learning over East Asia. ISPRS Journal of Photogrammetry and Remote Sensing, 183, 253-268.

Park, S., Kim, M., Im, J. (2021). Estimation of Ground-level PM10 and PM2.5 Concentrations Using Boosting-based Machine Learning from Satellite and Numerical Weather Prediction Data. Korean Journal of Remote Sensing, 37(2), 321-335.

Choi, H., Kang, Y., & Im, J. (2021). Estimation of TROPOMI-derived Ground-level SO2 Concentration Using Machine Learning Over East Asia. Korean Journal of Remote Sensing, 37(2), 275-290.

Kang, E., Yoo, C., Shin, Y., Cho, D., & Im, J. (2021). Comparative Assessment of Linear Regression and Machine Learning for Analyzing the Spatial Distribution of Ground-level NO2 Concentrations: A Case Study for Seoul, Korea. Korean Journal of Remote Sensing, v.37, no.6-1, pp.1739-1756.

Kang, Y., Choi, H., Im, J., Park, S., Shin, M., Song, C.K., & Kim, S. (2021). Estimation of surface-level NO2 and O3 concentrations using TROPOMI data and machine learning over East Asia. Environmental Pollution, 288, 117711.

Choi, H., Kang, Y., Im, J., Shin, M., Park, S., & Kim, S. M. (2020). Monitoring Ground-level SO2 Concentrations Based on a Stacking Ensemble Approach Using Satellite Data and Numerical Models. Korean Journal of Remote Sensing, 36(5_3), 1053-1066.

Park, S., Lee, J., Im, J., Song, C. K., Choi, M., Kim, J., ... & Lee, D. W. (2020). Estimation of spatially continuous daytime particulate matter concentrations under all sky conditions through the synergistic use of satellite-based AOD and numerical models. Science of The Total Environment, 713, 136516.

Shin, M., Kang, Y., Park, S., Im, J., Yoo, C., & Quackenbush, L. J. (2020). Estimating ground-level particulate matter concentrations using satellite-based data: a review. GIScience & Remote Sensing, 57(2), 174-189.

Park, S., Shin, M., Im, J., Song, C. K., Choi, M., Kim, J., ... & Kim, S. K. (2019). Estimation of ground-level particulate matter concentrations through the synergistic use of satellite observations and process-based models over South Korea. ACP, 19(2), 1097-1113.