Articles | Volume 15, issue 4
https://doi.org/10.5194/amt-15-1075-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-1075-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Mar 2022
Research article |
| 03 Mar 2022
| 03 Mar 2022
Continuous mapping of fine particulate matter (PM2.5) air quality in East Asia at daily 6 × 6 km2 resolution by application of a random forest algorithm to 2011–2019 GOCI geostationary satellite data
Drew C. Pendergrass
CORRESPONDING AUTHOR
School of Engineering and Applied Sciences, Harvard University,
Cambridge, MA, USA
Shixian Zhai
School of Engineering and Applied Sciences, Harvard University,
Cambridge, MA, USA
Jhoon Kim
Department of Atmospheric Sciences, Yonsei University, Seoul, South
Korea
Particulate Matter Research Institute, Samsung Advanced Institute of Technology (SAIT), Suwon, South Korea
Ja-Ho Koo
Department of Atmospheric Sciences, Yonsei University, Seoul, South
Korea
Seoyoung Lee
Department of Atmospheric Sciences, Yonsei University, Seoul, South
Korea
Minah Bae
Department of Environmental and Safety Engineering, Ajou University, Suwon, South Korea
Soontae Kim
Department of Environmental and Safety Engineering, Ajou University, Suwon, South Korea
Hong Liao
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and
Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China
Daniel J. Jacob
School of Engineering and Applied Sciences, Harvard University,
Cambridge, MA, USA
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Cited
11 citations as recorded by crossref.
- A comprehensive review delineates advancements in retrieving particulate matter utilising satellite aerosol optical depth: Parameter consideration, data processing, models development and future perspectives S. Padimala & C. Matli 10.1016/j.atmosres.2024.107514
- Near-real-time hourly PM2.5 prediction over East Asia using geostationary satellite products and machine learning J. Lee et al. 10.1016/j.atmosenv.2024.120700
- State-of-art in modelling particulate matter (PM) concentration: a scoping review of aims and methods L. Gianquintieri et al. 10.1007/s10668-024-04781-5
- Retrieval of aerosol optical properties from GOCI-II observations: Continuation of long-term geostationary aerosol monitoring over East Asia S. Lee et al. 10.1016/j.scitotenv.2023.166504
- Air Quality Sensor Experts Convene: Current Quality Assurance Considerations for Credible Data K. Barkjohn et al. 10.1021/acsestair.4c00125
- Why is ozone in South Korea and the Seoul metropolitan area so high and increasing? N. Colombi et al. 10.5194/acp-23-4031-2023
- Prescribed Burns as a Tool to Mitigate Future Wildfire Smoke Exposure: Lessons for States and Rural Environmental Justice Communities M. Kelp et al. 10.1029/2022EF003468
- North Korean CO emissions reconstruction using DMZ ground observations, TROPOMI space-borne data, and the CMAQ air quality model E. Kim et al. 10.1016/j.scitotenv.2024.171059
- Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning S. Shen et al. 10.1021/acsestair.3c00054
- Secondary aerosol formation drives atmospheric particulate matter pollution over megacities (Beijing and Seoul) in East Asia Y. Qiu et al. 10.1016/j.atmosenv.2023.119702
- Relating geostationary satellite measurements of aerosol optical depth (AOD) over East Asia to fine particulate matter (PM<sub>2.5</sub>): insights from the KORUS-AQ aircraft campaign and GEOS-Chem model simulations S. Zhai et al. 10.5194/acp-21-16775-2021
10 citations as recorded by crossref.
- A comprehensive review delineates advancements in retrieving particulate matter utilising satellite aerosol optical depth: Parameter consideration, data processing, models development and future perspectives S. Padimala & C. Matli 10.1016/j.atmosres.2024.107514
- Near-real-time hourly PM2.5 prediction over East Asia using geostationary satellite products and machine learning J. Lee et al. 10.1016/j.atmosenv.2024.120700
- State-of-art in modelling particulate matter (PM) concentration: a scoping review of aims and methods L. Gianquintieri et al. 10.1007/s10668-024-04781-5
- Retrieval of aerosol optical properties from GOCI-II observations: Continuation of long-term geostationary aerosol monitoring over East Asia S. Lee et al. 10.1016/j.scitotenv.2023.166504
- Air Quality Sensor Experts Convene: Current Quality Assurance Considerations for Credible Data K. Barkjohn et al. 10.1021/acsestair.4c00125
- Why is ozone in South Korea and the Seoul metropolitan area so high and increasing? N. Colombi et al. 10.5194/acp-23-4031-2023
- Prescribed Burns as a Tool to Mitigate Future Wildfire Smoke Exposure: Lessons for States and Rural Environmental Justice Communities M. Kelp et al. 10.1029/2022EF003468
- North Korean CO emissions reconstruction using DMZ ground observations, TROPOMI space-borne data, and the CMAQ air quality model E. Kim et al. 10.1016/j.scitotenv.2024.171059
- Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning S. Shen et al. 10.1021/acsestair.3c00054
- Secondary aerosol formation drives atmospheric particulate matter pollution over megacities (Beijing and Seoul) in East Asia Y. Qiu et al. 10.1016/j.atmosenv.2023.119702
1 citations as recorded by crossref.
Latest update: 17 Nov 2024
Short summary
This paper uses a machine learning algorithm to infer high-resolution maps of particulate air quality in eastern China, Japan, and the Korean peninsula, using data from a geostationary satellite along with meteorology. We then perform an extensive evaluation of this inferred air quality and use it to diagnose trends in the region. We hope this paper and the associated data will be valuable to other scientists interested in epidemiology, air quality, remote sensing, and machine learning.
This paper uses a machine learning algorithm to infer high-resolution maps of particulate air...
