Estimation of Air Pollution with Remote Sensing Data: Revealing Greenhouse Gas Emissions from Space
Type
conference paper
Date Issued
2021-07-23
Author(s)
Research Team
AIML Lab
Abstract
Air pollution is a major driver of climate change. Anthropogenic emissions from the burning of fossil fuels for transportation and power generation emit large amounts of problematic air pollutants, including Greenhouse Gases (GHGs). Despite the importance of limiting GHG emissions to mitigate climate change, detailed information about the spatial and temporal distribution of GHG and other air pollutants is difficult to obtain. Existing models for surface-level air pollution rely on extensive land-use datasets which are often locally restricted and temporally static. This work proposes a deep learning approach for the prediction of ambient air pollution that only relies on remote sensing data that is globally available and frequently updated. Combining optical satellite imagery with satellite-based atmospheric column density air pollution measurements enables the scaling of air pollution estimates (in this case NO2) to high spatial resolution (up to ∼10m) at arbitrary locations and adds a temporal component to these estimates. The proposed model performs with high accuracy when evaluated against air quality measurements from ground stations (mean absolute error <6 μg/m3). Our results en- able the identification and temporal monitoring of major sources of air pollution and GHGs.
Language
English
HSG Classification
contribution to scientific community
HSG Profile Area
None
Publisher
ICML
Publisher place
ICML 2021 Workshop on Tackling Climate Change with Machine Learning Workshop
Event Title
ICML 2021 Workshop on Tackling Climate Change with Machine Learning Workshop
Event Location
Virtual
Event Date
23.07.2021
Official URL
Subject(s)
Division(s)
Contact Email Address
linus.scheibenreif@unisg.ch
Eprints ID
264662
File(s)![Thumbnail Image]()
Name
scheibenreif-ccai-icml21.pdf
Size
5.34 MB
Format
Adobe PDF
Checksum (MD5)
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