Title: Fossil fuel CO₂ emissions over metropolitan areas from space: A multi-model analysis of OCO-2 data over Lahore, Pakistan

Abstract

Urban areas, where gathering more than 55% of the global population, alone contributed to more than 70% of anthropogenic fossil fuel carbon dioxide (CO_2ff) emissions. Accurate quantification of CO_2ff emissions from urban areas is of great importance to the formulation of global warming mitigation policies to achieve carbon neutrality by 2050. Satellite-based inversion techniques are unique among “top-down” approaches, potentially allowing us to track CO_2ff emission changes over cities globally. However, its accuracy is still limited by incomplete background information, cloud blockages, aerosol contaminations, and uncertainties in models and emission inventories used as prior. To evaluate the current potential of space-based quantification techniques, we present the first attempt to monitor long-term changes in CO_2ff emissions based on the OCO-2 satellite measurements of column-averaged dry-air mole fractions of CO₂ (X_CO2) over a fast-growing Asian metropolitan area: Lahore, Pakistan. We first examined the OCO-2 data availability at global scale. About 17% of OCO-2 soundings are marked as high-quality soundings by quality flags over the global 70 most populated cities over the period 2014-2019. Cloud blockage and aerosol contamination are the two main causes of data loss. As an attempt to recover additional soundings, we evaluated the effectiveness of OCO-2 quality flags atmore » the city level by comparing three flux quantification methods (WRF-Chem, X-STILT, and flux cross-sectional integration method), all based on the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) product. The satellite/bottom-up emissions (OCO-2/ODIAC) ratios of the high-quality tracks better converged across the three methods compared to the all-data tracks with reduced uncertainties in emissions. Thus, OCO-2 quality flags are useful filters of low-quality OCO-2 retrievals at local scales, although originally designed for global-scale studies. All three methods consistently suggested that the ratio medians are greater than 1, which implies that the ODIAC slightly underestimated the CO2ff emissions over Lahore. Additionally, our estimation of the a posteriori CO2ff emission trend was about 734 kt C/year (i.e., an annual 6.7% increase). 10,000 Monte Carlo simulations of the Mann-Kendall upward trend test showed that less than 10% prior uncertainty for 8 tracks (or less than 20% prior uncertainty for 25 tracks) is required to achieve a greater-than-50% trend significant possibility at a 95% confidence level. It implies that the trend is driven by the prior and not due to the assimilation of OCO-2 retrievals. The key to improving the role of satellite data in CO₂ emission trend detection lies in collecting more frequent high-quality tracks near metropolitan areas to achieve significant constraints from X_CO2 retrievals.« less

Authors:

Lei, Ruixue ^[1]; Feng, Sha ^[2]; Danjou, Alexandre ^[3]; Broquet, Gregoire ^[3]; Wu, Dien ^[4]; Lin, John C. ^[5]; O'Dell, Chris ^[6]; Lauvaux, Thomas ^[7]

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+ Show Author Affiliations

1. Pennsylvania State Univ., University Park, PA (United States)
2. Pennsylvania State Univ., University Park, PA (United States) ; Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
3. Univ. Paris-Saclay, Gif-sur-Yvette (France). Laboratoire des Scienes du Climat et de l'Environnement
4. California Institute of Technology (CalTech), Pasadena, CA (United States)
5. Univ. of Utah, Salt Lake City, UT (United States)
6. Colorado State Univ., Fort Collins, CO (United States)
7. Univ. Paris-Saclay, Gif-sur-Yvette (France). Laboratoire des Scienes du Climat et de l'Environnement; Pennsylvania State Univ., University Park, PA (United States)

Publication Date:

2021-08-10

Research Org.:

Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

Sponsoring Org.:

USDOE

OSTI Identifier:

1821460

Report Number(s):

PNNL-SA-163611
Journal ID: ISSN 0034-4257; TRN: US2214346

Grant/Contract Number:  

AC05-76RL01830

Resource Type:

Accepted Manuscript

Journal Name:

Remote Sensing of Environment

Additional Journal Information:

Journal Volume: 264; Journal ID: ISSN 0034-4257

Publisher:

Elsevier

Country of Publication:

United States

Language:

English

Subject:

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY