First Top-Down Estimates of Anthropogenic NOxEmissions Using High-Resolution AirborneRemote Sensing Observations
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Author/Creator ORCID
Date
2018-03-07
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Citation of Original Publication
Souri, A. H., Choi, Y., Pan, S., Curci, G.,Nowlan, C. R., Janz, S. J., et al. (2018). Firsttop-down estimates of anthropogenicNOxemissions using high-resolutionairborne remote sensing observations.Journal of Geophysical Research:Atmospheres,123, 3269–3284. https://doi.org/10.1002/2017JD028009.
Rights
This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
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Abstract
A number of satellite-based instruments have become an essential part of monitoring emissions.Despite sound theoretical inversion techniques, the insufficient samples and the footprint size of currentobservations have introduced an obstacle to narrow the inversion window for regional models. These keylimitations can be partially resolved by a set of modest high-quality measurements from airborne remotesensing. This study illustrates the feasibility of nitrogen dioxide (NO₂) columns from the Geostationary Coastal and Air Pollution Events Airborne Simulator (GCAS) to constrain anthropogenic NOx emissions in theHouston-Galveston-Brazoria area. We convert slant column densities to vertical columns using a radiativetransfer model with (i) NO₂ profiles from a high-resolution regional model (1 × 1 km²) constrained by P-3B aircraft measurements, (ii) the consideration of aerosol optical thickness impacts on radiance at NO₂ absorption line, and (iii) high-resolution surface albedo constrained by ground-based spectrometers. Wecharacterize errors in the GCAS NO₂ columns by comparing them to Pandora measurements andfind astriking correlation (r>0.74) with an uncertainty of 3.5 × 10¹⁵ molecules cm⁻². On 9 of 10 total days, the constrained anthropogenic emissions by a Kalmanfilter yield an overall 2–50% reduction in polluted areas,partly counterbalancing the well-documented positive bias of the model. The inversion, however, boostsemissions by 94% in the same areas on a day when an unprecedented local emissions event potentially occurred, significantly mitigating the bias of the model. The capability of GCAS at detecting such an event ensures the significance of forthcoming geostationary satellites for timely estimates of top-down emissions.