Quantification and attribution of urban fossil fuel emissions through atmospheric measurements

Super, Ingrid

doi:10.18174/457839

Cited by 1 publication

(5 citation statements)

References 151 publications

(370 reference statements)

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“…However, this possibility needs to be verified with correlative measurements having sufficient spatiotemporal coverage of the region. As has been suggested in past studies (e.g., Turnbull et al 2006;Vardag et al 2015;Super, 2018;Halliday et al 2019), these comparisons across flight groups, sampling locations, altitude, and time highlight the importance of understanding and properly accounting for the spatiotemporal variability of ΔCO ΔCO ! ⁄ when estimating ffCO2 emissions since differences in ΔCO ΔCO !…”

Section: Enhancement Ratiossupporting

confidence: 64%

“…⁄ are valid and temporal changes in CO2 are reasonably characterized (e.g., Nassar et al 2013;. This has been indicated for example in Super (2018) and Nathan et al (2018). As has been highlighted in this study, the use of regression approach in deriving these relationships are confounded by mixing and transport-related processes making it difficult to attribute the changes in the slopes to emission ratios alone, especially when analyzing downwind measurements.…”

Section: Discussion and General Implicationsmentioning

confidence: 76%

“…There have been several studies using information on local enhancement (∆ ) that can be derived from ΔCO ΔCO ! ⁄ to constrain ffCO2 emissions (Super, 2018). This approach employs assumptions on the spatiotemporal distribution of emission ratios between CO and ffCO2 using mass balance.…”

Section: Discussion and General Implicationsmentioning

confidence: 99%

“…Such datasets have been utilized in the past to provide additional constraints on combustion-related emission patterns in urban regions and biomass burning activities at local to global scales. They have been extended to provide insights on ffCO2 or fire CO2 (e.g., Suntharalingam et al, 2004;Palmer et al 2006;Wang et al, 2010;Turnbull et al, 2011;Brioude et al, 2013;Lopez et al, 2013;Silva et al, 2013;Konovalov et al, 2014;Lindenmaier et al 2014;Ammoura et al, 2016;Bowman et al, 2017;Super, 2018;Nathan et al, 2018;Boschetti et al, 2018 among others), as well as to identify and characterize air masses (e.g., Halliday et al 2019). These studies used CO as an indirect tracer of combustion through a variety of ways: data analysis, model-data comparison, modeling, or inversions at different scales and region depending on their application.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, derived CO:CO2 enhancement ratios near a source region are used to verify bulk CO:CO2 emission ratios from these inventories. Hourly ffCO2 emission profile from traffic are also deduced from measurements of CO (e.g., Vogel et al, 2010;Super, 2018). Because of its medium-length lifetime (1 to 2 months), CO is also a useful tracer of pollution (incl.…”

Section: Introductionmentioning

confidence: 99%

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On the relationship between tropospheric CO and CO2 during KORUS-AQ and its role in constraining anthropogenic CO2

Tang

Gaubert

Emmons

et al. 2020

Preprint

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Abstract. While the complementarity of CO data in monitoring CO2 from fossil-fuel combustion (ffCO2) is widely known, a rigorous demonstration of its use in reducing uncertainties on top-down regional ffCO2 emissions is still warranted. Here, we report a case study investigating the regional covariation of observed and modeled abundances of CO, CO2, and ffCO2 and demonstrating its implication to joint CO:CO2 inversions. We use data from a recent aircraft field campaign (KORUS-AQ) conducted over Korea and neighboring regions on May 2016 for this case study. We use the Community Atmosphere Model with Chemistry (CAM-Chem) to simulate CO, CO2, ffCO2 and associated source tags, using a posteriori fluxes from global CO2 flux inversions and CO emissions independently calibrated against CO data. Among other model-data comparisons, CAM-Chem simulations show an underestimation in CO2 (1 ppm), CO (24 ppb) and ffCO2 (1 ppm) against aircraft measurements. These are all within the range of model and data uncertainties. Although the overall observed enhancement ratio, ΔCO⁄ΔCO2 (~ 13.3 &om; 0.21 ppb/ppm), is well captured by CAM-Chem (~ 13.8 &pm; 0.23 ppb/ppm), we find an overestimation (29 ppb/ppm) for air samples between 2 to 3 km, where East Asian influence is substantial (35 %). The contribution of ffCO2 from Korea and Japan is smaller (30 %) and localized below 3 km, suggesting that regional ffCO2 and background and non-ffCO2 cannot be neglected in interpreting observed enhancements in this region. These spatial variations translate in the joint CO:CO2 inversion to increases in a posteriori ffCO2 estimates from East Asia (27 % &pm; 24 %) and Korea and Japan (9 % &pm; 17 %). This is consistent (albeit larger in 1-sigma uncertainty) with our estimate using 14CO2 data (27 % &pm; 9 % and 10 % &pm; 3 %, respectively). In contrast, the inversion using only CO2 data shows a decrease by ~ 5 % &pm; 27 % in East Asia and ~ 6 % &pm; 19 % in Korea and Japan. Our results show that inversions using both CO2 and CO can be an effective approach in constraining ffCO2 when the regional variations of CO and CO2 relationships are appropriately accounted for. Although this further points to the potential of augmenting current observing system of CO2 with CO for global inverse analyses of ffCO2 from different regions of the globe, we highlight the need to verify the spatiotemporal distribution of the covariation of CO with CO2 in both regional and global models. We caution its use for constraining local ffCO2, unless the spatiotemporal a priori flux distribution and surface processes are reasonably represented, as they may confound the analysis. These have important implications on inversion studies using columnar data from satellite observations, especially for regions lacking necessary verification measurements.

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Section: Enhancement Ratiossupporting

confidence: 64%

Section: Discussion and General Implicationsmentioning

confidence: 76%

Section: Discussion and General Implicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the relationship between tropospheric CO and CO2 during KORUS-AQ and its role in constraining anthropogenic CO2

Tang

Gaubert

Emmons

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

Quantification and attribution of urban fossil fuel emissions through atmospheric measurements

Cited by 1 publication

References 151 publications

On the relationship between tropospheric CO and CO<sub>2</sub> during KORUS-AQ and its role in constraining anthropogenic CO<sub>2</sub>

On the relationship between tropospheric CO and CO<sub>2</sub> during KORUS-AQ and its role in constraining anthropogenic CO<sub>2</sub>

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Quantification and attribution of urban fossil fuel emissions through atmospheric measurements

Cited by 1 publication

References 151 publications

On the relationship between tropospheric CO and CO&lt;sub&gt;2&lt;/sub&gt; during KORUS-AQ and its role in constraining anthropogenic CO&lt;sub&gt;2&lt;/sub&gt;

On the relationship between tropospheric CO and CO&lt;sub&gt;2&lt;/sub&gt; during KORUS-AQ and its role in constraining anthropogenic CO&lt;sub&gt;2&lt;/sub&gt;

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On the relationship between tropospheric CO and CO<sub>2</sub> during KORUS-AQ and its role in constraining anthropogenic CO<sub>2</sub>

On the relationship between tropospheric CO and CO<sub>2</sub> during KORUS-AQ and its role in constraining anthropogenic CO<sub>2</sub>