Derivation of Emissions From Satellite‐Observed Column Amounts and Its Application to TROPOMI NO₂ and CO Observations

Abstract

Satellite observations have tremendously advanced our understandings on the emissions, distributions, chemistry, and trends of critical atmospheric pollutants and greenhouse gases (

Sources of Formaldehyde in U.S. Oil and Gas Production Regions

“…For comparison, we include TROPOMI NO 2 measurements as a tracer for primary emissions from oil and gas production. Prior studies have shown that satellite NO 2 VCDs over the Permian Basin are correlated with oil and gas production activities since the mid-2000s − and can even be used to infer oil- and gas-related NO x emissions. , The summer averages 2018/2019 for formaldehyde, NO 2 , and oil and gas production are shown in Figure a–c. Here, oil and gas production volumes are averaged on a grid that matches the satellite data.…”

“…Most production regions in the United States are in relatively remote regions, where ground-based pollution measurements are sparse or nonexistent. Satellite remote sensing data have therefore played an important role in quantifying the emissions of methane and nitrogen oxides. ,− Satellite measurements of the nonmethane hydrocarbons released from oil and gas are not available to date. What is available are measurements of formaldehyde (HCHO), which can be released as a combustion byproduct and formed in the atmosphere from the photooxidation of precursor VOCs.…”

Avoiding methane emission rate underestimates when using the divergence method

“…Other work also demonstrates that some regions of negative emission estimated via the divergence method in the Permian can be related to changes in orogoraphy, surface albedo, or convergent wind fields [28]. Other works demonstrate that timeaveraged emission calculations from the divergence method are unlikely to be dominated by convergent wind fields [27], and other formulations of the divergence method calculate advective flux without any contribution from wind field divergence [48,49]. One could develop a model that prohibits the estimation of negative methane emissions (other works do so in a Bayesian framework [17,50]), though at this stage this would no longer purely be the 'divergence method' , which is driven entirely by the data and the principle of the conservation of mass.…”

On the theory of the divergence method for quantifying source emissions from satellite observations

“…The divergence method has mainly been applied to TROPOMI data (primarily NO 2 , but also CO and CH 4 data (e.g., Beirle et al, 2019Beirle et al, , 2021Beirle et al, , 2023Liu et al, 2021;de Foy & Schauer, 2022;Sun, 2022;Rey-Pommier et al, 2022, 2023Dix et al, 2022;Filonchyk & Peterson, 2023;Lonsdale & Sun, 2023). Recently, the method has also been applied to other satellites such as GEMS NO 2 data (Xu et al, 2023).…”

“…A study of its application to synthetic CO 2 data for the upcoming CO2M mission can be found in Hakkarainen et al (2022), which notes that a background and noise removal step must be applied to make the method robust for CO 2 data. Since its inception by Beirle et al (2019), notable improvements to the method include a different order of operations (differentiatethen-average instead of average-then-differentiate) as proposed in de Foy and Schauer (2022), computing the results on the satellite swaths themselves before remapping to a uniform grid as proposed in de Foy and Schauer (2022), skipping computations proportional to the wind divergence and adding a correction term proportional to the gradient of the topography as proposed in Sun (2022), and approximating the divergence operation using finite-difference stencils that can account for non-pixel-aligned transport as proposed in Sun (2022) and Veefkind et al (2023).…”