Imperial College London
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Esra Suel

Faculty of Medicine, School of Public Health

Honorary Research Fellow
 
 
 
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Contact

 

esra.suel

 
 
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Location

 

609Skempton BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Sorek-Hamer:2022:10.3390/atmos13050696,
author = {Sorek-Hamer, M and Von, Pohle M and Sahasrabhojanee, A and Akbari, Asanjan A and Deardorff, E and Suel, E and Lingenfelter, V and Das, K and Oza, NC and Ezzati, M and Brauer, M},
doi = {10.3390/atmos13050696},
journal = {Atmosphere},
pages = {1--16},
title = {A deep learning approach for meter-scale air quality estimation in urban environments using very high-spatial-resolution satellite imagery},
url = {http://dx.doi.org/10.3390/atmos13050696},
volume = {13},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - High-spatial-resolution air quality (AQ) mapping is important for identifying pollution sources to facilitate local action. Some of the most populated cities in the world are not equipped with the infrastructure required to monitor AQ levels on the ground and must rely on other sources, such as satellite derived estimates, to monitor AQ. Current satellite-data-based models provide AQ mapping on a kilometer scale at best. In this study, we focus on producing hundred-meter-scale AQ maps for urban environments in developed cities. We examined the feasibility of an image-based object-detection analysis approach using very high-spatial-resolution (2.5 m) commercial satellite imagery. We fed the satellite imagery to a deep neural network (DNN) to learn the association between visual urban features and air pollutants. The developed model, which solely uses satellite imagery, was tested and evaluated using both ground monitoring observations and land-use regression modeled PM2.5 and NO2 concentrations over London, Vancouver (BC), Los Angeles, and New York City. The results demonstrate a low error with a total RMSE < 2 µg/m3 and highlight the contribution of specific urban features, such as green areas and roads, to continuous hundred-meter-scale AQ estimations. This approach offers promise for scaling to global applications in developed and developing urban environments. Further analysis on domain transferability will enable application of a parsimonious model based merely on satellite images to create hundred-meter-scale AQ maps in developing cities, where current and historical ground data are limited.
AU  - Sorek-Hamer,M
AU  - Von,Pohle M
AU  - Sahasrabhojanee,A
AU  - Akbari,Asanjan A
AU  - Deardorff,E
AU  - Suel,E
AU  - Lingenfelter,V
AU  - Das,K
AU  - Oza,NC
AU  - Ezzati,M
AU  - Brauer,M
DO  - 10.3390/atmos13050696
EP  - 16
PY  - 2022///
SN  - 2073-4433
SP  - 1
TI  - A deep learning approach for meter-scale air quality estimation in urban environments using very high-spatial-resolution satellite imagery
T2  - Atmosphere
UR  - http://dx.doi.org/10.3390/atmos13050696
UR  - https://www.mdpi.com/2073-4433/13/5/696
UR  - http://hdl.handle.net/10044/1/96961
VL  - 13
ER  -
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