Title: A methodology to constrain carbon dioxide emissions from coal-fired power plants using satellite observations of co-emitted nitrogen dioxide

Abstract

We present a method to infer CO₂ emissions from individual power plants based on satellite observations of co-emitted nitrogen dioxide (NO₂), which could serve as complementary verification of bottom-up inventories or be used to supplement these inventories. We demonstrate its utility on eight large and isolated US power plants, where accurate stack emission estimates of both gases are available for comparison. In the first step of our methodology, we infer nitrogen oxides (NO_x) emissions from US power plants using Ozone Monitoring Instrument (OMI) NO₂ tropospheric vertical column densities (VCDs) averaged over the ozone season (May–September) and a “top-down” approach that we previously developed. Second, we determine the relationship between NO_x and CO₂ emissions based on the direct stack emissions measurements reported by continuous emissions monitoring system (CEMS) programs, accounting for coal quality, boiler firing technology, NO_x emission control device type, and any change in operating conditions. Third, we estimate CO₂ emissions for power plants using the OMI-estimated NO_x emissions and the CEMS NO_x CO₂ emission ratio. We find that the CO₂ emissions estimated by our satellite-based method during 2005–2017 are in reasonable agreement with the US CEMS measurements, with a relative difference of 8 % ± 41 % (mean ±more » standard deviation). The broader implication of our methodology is that it has the potential to provide an additional constraint on CO₂ emissions from power plants in regions of the world without reliable emissions accounting. We explore the feasibility by comparing the derived NO_x CO₂ emission ratios for the US with those from a bottom-up emission inventory for other countries and applying our methodology to a power plant in South Africa, where the satellite-based emission estimates show reasonable consistency with other independent estimates. Though our analysis is limited to a few power plants, we expect to be able to apply our method to more US (and world) power plants when multi-year data records become available from new OMI-like sensors with improved capabilities, such as the TROPOspheric Monitoring Instrument (TROPOMI), and upcoming geostationary satellites, such as the Tropospheric Emissions: Monitoring Pollution (TEMPO) instrument.« less

Authors:

Liu, Fei  ^[1];

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Duncan, Bryan N. ^[2]; Krotkov, Nickolay A. ^[2]; Lamsal, Lok N. ^[3]; Beirle, Steffen ^[4]; Griffin, Debora  ^[5];

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McLinden, Chris A.  ^[5];

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Goldberg, Daniel L. ^[6]; Lu, Zifeng ^[6]

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+ Show Author Affiliations

1. Goddard Earth Sciences Technology and Research (GESTAR), Columbia, MD (United States); Univ. Space Research Association (USRA), MD (United States); NASA Goddard Space Flight Center (GSFC), Greenbelt, MD (United States)
2. NASA Goddard Space Flight Center (GSFC), Greenbelt, MD (United States)
3. Goddard Earth Sciences Technology and Research (GESTAR), Columbia, MD (United States); Univ. Space Research Association (USRA), MD (United States); NASA Goddard Space Flight Center (GSFC), Greenbelt, MD (United States)
4. Max-Planck-Institut für Chemie, Mainz (Germany)
5. Environment and Climate Change Canada, Toronto, ON (Canada)
6. Argonne National Lab. (ANL), Lemont, IL (United States)

Publication Date:

2020-01-03

Research Org.:

Argonne National Lab. (ANL), Argonne, IL (United States)

Sponsoring Org.:

National Aeronautics and Space Administration (NASA)

OSTI Identifier:

1615510

Grant/Contract Number:  

AC02-06CH11357

Resource Type:

Accepted Manuscript

Journal Name:

Atmospheric Chemistry and Physics (Online)

Additional Journal Information:

Journal Name: Atmospheric Chemistry and Physics (Online); Journal Volume: 20; Journal Issue: 1; Journal ID: ISSN 1680-7324

Publisher:

European Geosciences Union

Country of Publication:

United States

Language:

English

Subject:

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY