Cooperative simultaneous inversion of satellite-based real-time PM2.5 and ozone levels using an improved deep learning model with attention mechanism
(2023) In Environmental Pollution 327.- Abstract
Ground-level fine particulate matter (PM2.5) and ozone (O3) are air pollutants that can pose severe health risks. Surface PM2.5 and O3 concentrations can be monitored from satellites, but most retrieval methods retrieve PM2.5 or O3 separately and disregard the shared information between the two air pollutants, for example due to common emission sources. Using surface observations across China spanning 2014–2021, we found a strong relationship between PM2.5 and O3 with distinct spatiotemporal characteristics. Thus, in this study, we propose a new deep learning model called the Simultaneous Ozone and PM2.5 inversion deep neural Network... (More)
Ground-level fine particulate matter (PM2.5) and ozone (O3) are air pollutants that can pose severe health risks. Surface PM2.5 and O3 concentrations can be monitored from satellites, but most retrieval methods retrieve PM2.5 or O3 separately and disregard the shared information between the two air pollutants, for example due to common emission sources. Using surface observations across China spanning 2014–2021, we found a strong relationship between PM2.5 and O3 with distinct spatiotemporal characteristics. Thus, in this study, we propose a new deep learning model called the Simultaneous Ozone and PM2.5 inversion deep neural Network (SOPiNet), which allows for daily real-time monitoring and full coverage of PM2.5 and O3 simultaneously at a spatial resolution of 5 km. SOPiNet employs the multi-head attention mechanism to better capture the temporal variations in PM2.5 and O3 based on previous days’ conditions. Applying SOPiNet to MODIS data over China in 2022, using 2019–2021 to construct the network, we found that simultaneous retrievals of PM2.5 and O3 improved the performance compared with retrieving them independently: the temporal R2 increased from 0.66 to 0.72 for PM2.5, and from 0.79 to 0.82 for O3. The results suggest that near-real time satellite-based air quality monitoring can be improved by simultaneous retrieval of different but related pollutants. The codes of SOPiNet and its user guide are freely available online at https://github.com/RegiusQuant/ESIDLM.
(Less)
- author
- Yan, Xing ; Zuo, Chen ; Li, Zhanqing ; Chen, Hans W. LU ; Jiang, Yize ; He, Bin ; Liu, Huiming ; Chen, Jiayi and Shi, Wenzhong
- organization
- publishing date
- 2023
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Deep learning model, Ozone, PM, Real-time, Satellite
- in
- Environmental Pollution
- volume
- 327
- article number
- 121509
- publisher
- Elsevier
- external identifiers
-
- scopus:85151558115
- pmid:36967005
- ISSN
- 0269-7491
- DOI
- 10.1016/j.envpol.2023.121509
- language
- English
- LU publication?
- yes
- id
- 5b8e0b6d-bf11-4a4a-8fb3-23215166c455
- date added to LUP
- 2023-05-15 15:01:51
- date last changed
- 2024-06-16 05:31:58
@article{5b8e0b6d-bf11-4a4a-8fb3-23215166c455,
abstract = {{<p>Ground-level fine particulate matter (PM<sub>2.5</sub>) and ozone (O<sub>3</sub>) are air pollutants that can pose severe health risks. Surface PM<sub>2.5</sub> and O<sub>3</sub> concentrations can be monitored from satellites, but most retrieval methods retrieve PM<sub>2.5</sub> or O<sub>3</sub> separately and disregard the shared information between the two air pollutants, for example due to common emission sources. Using surface observations across China spanning 2014–2021, we found a strong relationship between PM<sub>2.5</sub> and O<sub>3</sub> with distinct spatiotemporal characteristics. Thus, in this study, we propose a new deep learning model called the Simultaneous Ozone and PM<sub>2.5</sub> inversion deep neural Network (SOPiNet), which allows for daily real-time monitoring and full coverage of PM<sub>2.5</sub> and O<sub>3</sub> simultaneously at a spatial resolution of 5 km. SOPiNet employs the multi-head attention mechanism to better capture the temporal variations in PM<sub>2.5</sub> and O<sub>3</sub> based on previous days’ conditions. Applying SOPiNet to MODIS data over China in 2022, using 2019–2021 to construct the network, we found that simultaneous retrievals of PM<sub>2.5</sub> and O<sub>3</sub> improved the performance compared with retrieving them independently: the temporal R<sup>2</sup> increased from 0.66 to 0.72 for PM<sub>2.5</sub>, and from 0.79 to 0.82 for O<sub>3</sub>. The results suggest that near-real time satellite-based air quality monitoring can be improved by simultaneous retrieval of different but related pollutants. The codes of SOPiNet and its user guide are freely available online at https://github.com/RegiusQuant/ESIDLM.</p>}},
author = {{Yan, Xing and Zuo, Chen and Li, Zhanqing and Chen, Hans W. and Jiang, Yize and He, Bin and Liu, Huiming and Chen, Jiayi and Shi, Wenzhong}},
issn = {{0269-7491}},
keywords = {{Deep learning model; Ozone; PM; Real-time; Satellite}},
language = {{eng}},
publisher = {{Elsevier}},
series = {{Environmental Pollution}},
title = {{Cooperative simultaneous inversion of satellite-based real-time PM<sub>2.5</sub> and ozone levels using an improved deep learning model with attention mechanism}},
url = {{http://dx.doi.org/10.1016/j.envpol.2023.121509}},
doi = {{10.1016/j.envpol.2023.121509}},
volume = {{327}},
year = {{2023}},
}