Preprints
https://doi.org/10.5194/egusphere-2023-2635
https://doi.org/10.5194/egusphere-2023-2635
12 Jan 2024
 | 12 Jan 2024

Post-process correction improves the accuracy of satellite PM2.5 retrievals

Andrea Porcheddu, Ville Kolehmainen, Timo Lähivaara, and Antti Lipponen

Abstract. Estimates of PM2.5 levels are crucial for monitoring air quality and studying the epidemiological impact of air quality on the population. Currently, the most precise measurements of PM2.5 are obtained from ground stations, resulting in limited spatial coverage. In this study, we consider satellite-based PM2.5 retrieval, which involves conversion of high-resolution satellite retrieval of Aerosol Optical Depth (AOD) into high-resolution PM2.5 retrieval. To improve the accuracy of the AOD to PM2.5 conversion, we employ the machine learning based post-process correction to correct the AOD-to-PM conversion ratio derived from Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis model data. The post-process correction approach utilizes a fusion and downscaling of satellite observation and retrieval data, MERRA-2 reanalysis data, various high resolution geographical indicators, meteorological data and ground station observations for learning a predictor for the approximation error in the AOD to PM2.5 conversion ratio. The corrected conversion ratio is then applied to estimate PM2.5 levels given the high-resolution satellite AOD retrieval data derived from Sentinel-3 observations. Our model produces PM2.5 estimates with a spatial resolution of 100 meters at satellite overpass times. Additionally, we have incorporated an ensemble of neural networks to provide error envelopes for machine learning related uncertainty in the PM2.5 estimates.

Andrea Porcheddu, Ville Kolehmainen, Timo Lähivaara, and Antti Lipponen

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-2635', Anonymous Referee #1, 31 Jan 2024
  • CC1: 'Group comment on egusphere-2023-2635', Adam Povey, 13 Feb 2024
  • RC2: 'Comment on egusphere-2023-2635', Anonymous Referee #2, 19 Feb 2024
Andrea Porcheddu, Ville Kolehmainen, Timo Lähivaara, and Antti Lipponen
Andrea Porcheddu, Ville Kolehmainen, Timo Lähivaara, and Antti Lipponen

Viewed

Total article views: 249 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
189 50 10 249 3 3
  • HTML: 189
  • PDF: 50
  • XML: 10
  • Total: 249
  • BibTeX: 3
  • EndNote: 3
Views and downloads (calculated since 12 Jan 2024)
Cumulative views and downloads (calculated since 12 Jan 2024)

Viewed (geographical distribution)

Total article views: 247 (including HTML, PDF, and XML) Thereof 247 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 25 Feb 2024
Download
Short summary
This study focuses on improving the accuracy of satellite-based PM2.5 retrieval, crucial for monitoring air quality and its impact on health. It employs machine learning to correct the AOD-to-PM2.5 conversion ratio using various data sources. The approach produces high-resolution PM2.5 estimates with improved accuracy. The method is flexible and can incorporate additional training data from different sources, making it a valuable tool for air quality monitoring and epidemiological studies.