Estimation of surface PM2.5 concentrations from atmospheric gas species retrieved from TROPOMI using deep learning: Impacts of fire on air pollution over Thailand

Son, R.; Stratoulias, D.; Kim, H.C.; Yoon, J-H.

doi:10.1016/j.apr.2023.101875

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Estimation of surface PM2.5 concentrations from atmospheric gas species retrieved from TROPOMI using deep learning: Impacts of fire on air pollution over Thailand

2023
By Son, R. ; Stratoulias, D. ; Kim, H.C. ; ...
Source: Atmospheric Pollution Research 14 (2023) 101875

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Details:

Journal Title:

Atmospheric Pollution Research
Personal Author:

Son, R. ; Stratoulias, D. ; Kim, H.C. ; Yoon, J-H.

Son, R. ; Stratoulias, D. ; Kim, H.C. ; Yoon, J-H. Less -
NOAA Program & Office:

NESDIS (National Environmental Satellite, Data, and Information Service) ; CISESS (Cooperative Institute for Satellite Earth System Studies)

NESDIS (National Environmental Satellite, Data, and Information Service) ; CISESS (Cooperative Institute for Satellite Earth System Studies) Less -
Description:

Surface PM2.5 concentration is routinely observed at limited number of surface monitoring stations. To overcome its limited spatial coverage, space-borne monitoring system has been established. However, it also faces various challenges such as cloud contamination and limited vertical resolution. In this study, we propose a deep learning-based surface PM2.5 estimation method using the attentive interpretable tabular learning neural network (TabNet) with atmospheric gas species retrieved from the tropospheric monitoring instrument (TROPOMI). Unlike previous applications that primarily used decision tree-based algorithms, TabNet provides interpretable decision-making steps to identify dominant factors. By incorporating five TROPOMI products (i.e., NO2, SO2, O3, CO, HCHO), we have tested the system's capability to produce surface PM2.5 concentration without aerosol optical property, which was used more traditionally. The proposed model successfully captures spatiotemporal variations over Thailand in the period of 2018–2020, and it outperforms other leading machine learning models, particularly at high concentrations. The interpretable decision-making steps highlight that carbon monoxide is the most influential chemical component, which relates to the seasonal burning in southeast Asia. It is found that air quality impacts from fire are stronger in the northern part of Thailand and fires in neighboring countries should not be neglected. The proposed method successfully estimates surface PM2.5 concentration without aerosol optical property, implying its potential to advance monitoring air quality over remote regions.
Keywords:

[+]

Air—Pollution Deep Learning (Machine Learning) Fine Particulate Matter PM2.5 Tabular Learning Neural Network (TabNet) Tropospheric Monitoring Instrument (TROPOMI)
Source:

Atmospheric Pollution Research 14 (2023) 101875
DOI:

https://doi.org/10.1016/j.apr.2023.101875
Format:

PDF
Document Type:

Journal Article
Funding:

Grant no. NA19NES4320002
Place as Subject:

Thailand
License:

https://creativecommons.org/licenses/by-nc-nd/4.0/
Rights Information:

CC BY-NC-ND
Compliance:

Submitted
Main Document Checksum:

[+]

urn:sha256:2479a7172df930b678499f97674a4358197412abdd474fe6e4a0b3a5e4dfbe01
Download URL:

https://repository.library.noaa.gov/view/noaa/55913/noaa_55913_DS1.pdf
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