A Novel Dataset and Benchmark for Surface NO2 Prediction from Remote Sensing Data Including COVID Lockdown Measures
Type
conference paper
Date Issued
2021-07-16
Author(s)
Research Team
AIML Lab
Abstract
NO2 is an atmospheric trace gas that contributes to global warming as a precursor of greenhouse gases and has adverse effects on human health. Surface NO2 concentrations are commonly measured through strictly localized networks of air quality stations on the ground. This work presents a novel dataset of surface NO2 measurements aligned with atmospheric column densities from Sentinel-5P, as well as geographic and meteorological variables and lockdown information. The dataset provides access to data from a variety of sources through a common format and will foster data-driven research into the causes and effects of NO2 pollution. We showcase the value of the new dataset on the task of surface NO2 estimation with gradient boosting. The resulting models enable daily estimates and confident identification of EU NO2 exposure limit breaches. Additionally, we investigate the influence of COVID-19 lockdowns on air quality in Europe and find a significant decrease in NO2 levels.
Language
English
HSG Classification
contribution to scientific community
Publisher
IEEE
Publisher place
IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2021
Event Title
IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2021
Event Location
Brussels, Belgium (virtual)
Event Date
11-16 July 2021
Subject(s)
Division(s)
Contact Email Address
linus.scheibenreif@unisg.ch
Eprints ID
264664