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High-Resolution Mapping of Nitrogen Dioxide With TROPOMI

First Results and Validation Over the Canadian Oil Sands

Journal Article (2019)
Author(s)

Debora Griffin (Environment Canada)

Xiaoyi Zhao (Environment Canada)

Chris A. McLinden (Environment Canada)

Folkert Boersma (Wageningen University & Research, Royal Netherlands Meteorological Institute (KNMI))

Adam Bourassa (University of Saskatchewan)

Enrico Dammers (Environment Canada)

Doug Degenstein (University of Saskatchewan)

Henk Eskes (Royal Netherlands Meteorological Institute (KNMI))

Pepijn Veefkind (TU Delft - Atmospheric Remote Sensing, Royal Netherlands Meteorological Institute (KNMI))

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Research Group
Atmospheric Remote Sensing
DOI related publication
https://doi.org/10.1029/2018GL081095
More Info
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Publication Year
2019
Language
English
Research Group
Atmospheric Remote Sensing
Issue number
2
Volume number
46
Pages (from-to)
1049-1060
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Abstract

TROPOspheric Monitoring Instrument (TROPOMI), on-board the Sentinel-5 Precurser satellite, is a nadir-viewing spectrometer measuring reflected sunlight in the ultraviolet, visible, near-infrared, and shortwave infrared. From these spectra several important air quality and climate-related atmospheric constituents are retrieved, including nitrogen dioxide (NO2) at unprecedented spatial resolution from a satellite platform. We present the first retrievals of TROPOMI NO2 over the Canadian Oil Sands, contrasting them with observations from the Ozone Monitoring Instrument satellite instrument, and demonstrate TROPOMI's ability to resolve individual plumes and highlight its potential for deriving emissions from individual mining facilities. Further, the first TROPOMI NO2 validation is presented, consisting of aircraft and surface in situ NO2 observations, and ground-based remote-sensing measurements between March and May 2018. Our comparisons show that the TROPOMI NO2 vertical column densities are highly correlated with the aircraft and surface in situ NO2 observations, and the ground-based remote-sensing measurements with a low bias (15–30 %); this bias can be reduced by improved air mass factors.