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,Shixian Zhai,Jhoon Kim,Ja-Ho Koo,Seoyoung Lee,Minah Bae,Soontae Kim,Hong Liao,and Daniel J. Jacob
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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Total article views: 4,262 (including HTML, PDF, and XML)
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Total article views: 2,800 (including HTML, PDF, and XML)
Thereof 2,800 with geography defined
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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...
