Articles | Volume 15, issue 4
Research article
03 Mar 2022
Research article |  | 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, Shixian Zhai, Jhoon Kim, Ja-Ho Koo, Seoyoung Lee, Minah Bae, Soontae Kim, Hong Liao, and Daniel J. Jacob


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2021-273', Anonymous Referee #1, 03 Dec 2021
    • AC1: 'Reply on RC1', Drew Pendergrass, 20 Jan 2022
  • RC2: 'Comment on amt-2021-273', Anonymous Referee #2, 04 Dec 2021
    • AC2: 'Reply on RC2', Drew Pendergrass, 20 Jan 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Drew Pendergrass on behalf of the Authors (20 Jan 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish subject to technical corrections (04 Feb 2022) by Marloes Penning de Vries
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.