Abstract
Urban areas, where more than 55% of the global population gathers, contribute more than 70% of anthropogenic fossil fuel carbon dioxide (CO2ff) emissions. Accurate quantification of CO2ff emissions from urban areas is of great importance for formulating 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 CO2ff emission changes over cities globally. However, their accuracy is still limited by incomplete background information, cloud blockages, aerosol contamination, and uncertainties in models and priori emission inventories. To evaluate the current potential of space-based quantification techniques, we present the first attempt to monitor long-term changes in CO2ff emissions based on the OCO-2 satellite measurements of column-averaged dry-air mole fractions of CO2 (XCO2) 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 over the global 70 most populated cities from 2014 to 2019 are marked as high-quality. 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 at the city level by comparing three flux quantification methods (WRF-Chem, X-STILT, and the flux cross-sectional integration method). The satellite/bottom-up emissions (OCO-2/ODIAC) ratios of the high-quality tracks with reduced uncertainties in emissions are better agreed across the three methods compared to the all-data tracks. This demonstrates that OCO-2 quality flags are useful filters of low-quality OCO-2 retrievals at local scales. All three methods consistently suggested that the ratio medians are greater than 1, implying that the ODIAC slightly underestimated 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 XCO2 retrievals.
Highlights
Carbon dioxide (CO2) alone has contributed to more than 60% of the global direct radiative forcing from Greenhouse Gases (GHGs) that has increased by 45% from 1990 to 2019 according to the AnnualGreenhouse Gas Index (AGGI)
Since this study focuses on the CO2ff emissions from city areas, we scanned all OCO-2 tracks over the 70 most populated cities globally from 2014 to 2019 to examine how many tracks are valid for CO2ff emission inversion calculations
The OCO-2 quality flags were originally designed for global-scale studies
Summary
Carbon dioxide (CO2) alone has contributed to more than 60% of the global direct radiative forcing from Greenhouse Gases (GHGs) that has increased by 45% from 1990 to 2019 according to the AnnualGreenhouse Gas Index (AGGI) Out of the global CO2ff emissions, more than 70% originate from cities alone (Birol, 2008; Mitchell et al, 2018), where more than 55% of the global population resides (World Bank, 2014). Urbanization is continuously increasing, with no projected decline (World Bank, 2014). This makes accurately quantifying CO2ff emissions from urban areas of great importance to formulating the global warming mitigation policies necessary to achieve carbon neutrality by 2050 (UNFCCC, 2015). Two distinct approaches are commonly used to estimate CO2ff emissions, i.e., ‘bottom-up’ and ‘top-down’. ‘Bottom-up’ approaches estimate CO2ff emissions based on standardized protocols, combining activity data such as fuel production and consumption as well as traffic monitoring data with pre-calculated emission factors for specific sources across different activity sectors (UNFCCC, 2015). Uncertainties in bottom-up approaches are caused by data gaps, a lack of information on energy and fuel use statistics, and outdated or inaccurate emission fac tors (Andres et al, 1996; Liu et al, 2015; Macknick, 2009) ranging from 5% in Organization for Economic Co-operation and Development (OECD) countries (Marland, 2008), to 15–20% for China (Gregg et al, 2008), to 50% or more for emerging economies (Andres et al, 2014)
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