Preprints
https://doi.org/10.5194/acp-2021-1029
https://doi.org/10.5194/acp-2021-1029

  04 Jan 2022

04 Jan 2022

Review status: this preprint is currently under review for the journal ACP.

Towards sector-based attribution using intra-city variations in satellite-based emission ratios between CO2 and CO

Dien Wu1, Junjie Liu2, Paul O. Wennberg1,3, Paul I. Palmer2,4, Robert R. Nelson2, Matthäus Kiel2, and Annmarie Eldering2 Dien Wu et al.
  • 1Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, USA
  • 2Jet Propulsion Laboratory, California Institute of Technology, Pasadena, USA
  • 3Division of Engineering and Applied Science, California Institute of Technology, Pasadena, USA
  • 4School of GeoSciences, University of Edinburgh, Edinburgh, UK

Abstract. Carbon dioxide (CO2) and air pollutants such as carbon monoxide (CO) are co-emitted by many combustion sources. Previous efforts have combined satellite-based observations of multiple tracers to calculate their emission ratio (ER) for inferring combustion efficiency at regional to city scale. Very few studies have focused on burning efficiency at the sub-city scale or related it to emission sectors using space-based observations. Several factors are important for deriving spatially-resolved ERs from asynchronous satellite measurements including 1) variations in meteorological conditions induced by different overpass times, 2) differences in vertical sensitivity of the retrievals (i.e., averaging kernel profiles), and 3) interferences from the biosphere and biomass burning. In this study, we extended an established emission estimate approach to arrive at spatially-resolved ERs based on retrieved column-averaged CO2 (XCO2) from the Snapshot Area Mapping (SAM) mode of the Orbiting Carbon Observatory-3 (OCO-3) and column-averaged CO from the TROPOspheric Monitoring Instrument (TROPOMI). To evaluate the influence of the confounding factors listed above and further explain the intra-urban variations in ERs, we leveraged a Lagrangian atmospheric transport model and an urban land cover classification dataset and reported ERCO from the sounding level to the overpass- and city- levels. We found that the difference in the overpass times and averaging kernels between OCO and TROPOMI strongly affect the estimated spatially-resolved ERCO. Specifically, a time difference of > 3 hours typically led to dramatic changes in the wind direction and shape of urban plumes and thereby making the calculation of accurate sounding-specific ERCO difficult. After removing those cases from consideration and applying a simple plume shift method when necessary, we discovered significant contrasts in combustion efficiencies between 1) two megacities versus two industry-oriented cities and 2) different regions within a city, based on six to seven nearly-coincident overpasses per city. Results suggest that the combustion efficiency for heavy industry in Los Angeles is slightly lower than its overall city-wide value (< 10 ppb-CO / ppm-CO2). In contrast, ERs related to the heavy industry in Shanghai are found to be much higher than Shanghai’s city-mean and more aligned with city-means of the two industry-oriented Chinese cities (approaching 20 ppb-CO / ppm-CO2). Although investigations based on a larger number of satellite overpasses are needed, our first analysis provides guidance for estimating intra-city gradients in combustion efficiency from future missions, such as those that will map column CO2 and CO concentration simultaneously with high spatiotemporal resolutions.

Dien Wu et al.

Status: open (until 19 Feb 2022)

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Dien Wu et al.

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Short summary
Most previous studies have combined satellite-based CO2 and CO data to infer combustion efficiency at the regional and city scale. In this study, we zoomed into the urban area and accounted for several factors that can affect the calculation of spatially-resolved combustion efficiency from TROPOMI and OCO-3. We further related the intra-city variability in combustion efficiency to heavy industry in the city without relying on prior emission inventories.
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