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Estimation of Air Pollution with Remote Sensing Data
Type
conference poster
Date Issued
2022-05-01
Research Team
AIML Lab
Abstract
Air pollution is a major driver of climate change. Anthropogenic emissions from the burning of fos- sil 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 mit- igate climate change, detailed information about the spatial and temporal distribution of GHG and other air pollutants is difficult to obtain. Exist- ing models for surface-level air pollution rely on extensive land-use datasets which are often lo- cally restricted and temporally static. This work proposes a deep learning approach for the pre- diction of ambient air pollution that only relies on remote sensing data that is globally available and frequently updated. Combining optical satel- lite imagery with satellite-based atmospheric col- umn 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 compo- nent to these estimates. The proposed model per- forms 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 Profile Area
None
Event Title
Swiss Remote Sensing Days
Event Location
Ascona
Event Date
1-4 May 2022
Subject(s)
Division(s)
Eprints ID
268264
File(s)