Toward Global Estimation of Ground-Level NO2 Pollution With Deep Learning and Remote Sensing

Item Type Journal paper
Abstract Air pollution is a central environmental problem in countries around the world. It contributes to climate change through the emission of greenhouse gases, and adversely impacts the health of billions of people. Despite its importance, detailed information about the spatial and temporal distribution of pollutants is complex to obtain. Ground-level monitoring stations are sparse, and approaches for modeling air pollution rely on extensive datasets which are unavailable for many locations. We introduce three techniques for the estimation of air pollution to overcome these limitations: 1) a baseline localized approach that mimics conventional land-use regression through gradient boosting; 2) an OpenStreetMap (OSM) approach with gradient boosting that is applicable beyond regions covered by detailed geographic datasets; and 3) a remote sensing-based deep learning method utilizing multiband imagery and trace-gas column density measurements from satellites. We focus on the estimation of nitrogen dioxide (NO2), a common anthropogenic air pollutant with adverse effects on the environment and human health. Our local baseline model achieves strong results with a mean absolute error (MAE) of 5.18 ± 0.16 μg/m3 NO2. Substituting localized inputs with OSM leads to a degraded performance (MAE 7.22 ± 0.14) but enables NO2 estimation at a global scale. The proposed deep learning model on remote sensing data combines high accuracy (MAE 5.5 ± 0.14) with global coverage and heteroscedastic uncertainty quantification. Our results enable the estimation of surface-level NO2 pollution with high spatial resolution for any location on Earth. We illustrate this capability with an out-of-distribution test set on the US westcoast. Code and data are publicly available.
Authors Scheibenreif, Linus Mathias; Mommert, Michael & Borth, Damian
Research Team AIML Lab
Journal or Publication Title IEEE Transactions on Geoscience and Remote Sensing
Language English
Subjects computer science
HSG Classification contribution to scientific community
Refereed Yes
Date 21 March 2022
Publisher IEEE
Place of Publication USA
Volume 60
Number 4705914
Page Range 1-14
Number of Pages 14
ISSN 0196-2892
ISSN-Digital 1558-0644
Publisher DOI
Official URL
Contact Email Address
Depositing User Linus Mathias Scheibenreif
Date Deposited 29 Jun 2022 14:11
Last Modified 20 Jul 2022 17:48


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Scheibenreif, Linus Mathias; Mommert, Michael & Borth, Damian (2022) Toward Global Estimation of Ground-Level NO2 Pollution With Deep Learning and Remote Sensing. IEEE Transactions on Geoscience and Remote Sensing, 60 (4705914). 1-14. ISSN 0196-2892

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