Carbon dioxide (CO
To evaluate the influences of the confounding factors listed above and further attribute intra-urban variations in ERs to certain sectors, we leveraged a Lagrangian atmospheric transport model with an urban land cover classification dataset and reported ER
Home to more than half of the total global population, urban areas have been expanding, especially in Asia and Africa, which had urbanization rates of 1.3 % and 1.1 % yr
Given the co-benefit of GHG reduction and improved air quality at various scales
CO and NO
ER
We performed a literature search for ER
When estimating fossil fuel emissions from a bottom-up perspective, most inventories rely on activity data and may involve prior knowledge of emission factors
Most existing studies have focused on quantifying an integrated ER for a whole city or region. We take a step forward, zooming into an urban area and leveraging spatially resolved satellite observations. Intra-city variations in the satellite-based concentration of a specific air pollutant such as NO
In this study, we explore the spatial distribution of ER
Our work seeks to answer the following two questions:
Is it possible to accurately quantify the spatially resolved ER Can the combustion efficiency for a given sector be determined without using prior emission inventories?
In Sect.
We target two types of cities: (1) industry- and energy-oriented cities (Baotou, China and Zibo, China) and (2) megacities with more diverse emission sectors (Shanghai, China and Los Angeles, USA). The four cities are selected considering the amount and quality of XCO
Our goal is to calculate ER
We evaluate all coincident OCO-3 SAM and TROPOMI overpass observations, but only select those with relatively small differences in overpass times. Considering the limited number of coincidences between sensors, two non-SAM overpasses from OCO-3 and one OCO-2 overpass are added to the analysis. As a result, six OCO–TROPOMI coincidences with high data quality from October 2019 to June 2021 are integrated into the final result for every city. Two of the total of 24 overpasses fall within the Northern Hemisphere summer months (both in June).
The column-averaged dry-air mole fraction of CO
The TROPOMI column density of CO molecules [mole cm
Spatial maps of FF XCO
Four mismatches between OCO-3 XCO
Previous studies
All the
For a given sounding, the estimated flux
The X-STILT model is adopted in this study (1) to provide the scaling factor
STILT releases an ensemble of air parcels from target observations (known as the “receptor”) and tracks the movement of those air parcels backward in time. The source region corresponding to each sounding is inferred from the “source–receptor relation” or the STILT “footprint”
To accommodate the use of satellite-based column data, X-STILT incorporates retrieval-specific AK and pressure weighting profiles into the footprint calculation
In short, the spatial summation of column footprints
Defining accurate background levels to extract urban FF enhancements has always been a challenge in top-down analyses, especially when dealing with column measurements with small signal-to-noise ratios.
The process of background determination used in this work involves the first step of identifying the urban plume and differentiating soundings as being within or outside of the plume. To outline the urban plume shape at the overpass time, we utilize the forward mode of STILT with the inclusion of wind uncertainty in atmospheric dispersion. Specifically, 1000 air parcels are released continuously from a rectangle representing the city domain (dashed black box in Fig.
Demonstrations of background determination from OCO-2 XCO
Next, the background value is calculated as the median value of observed X
The swath-dependent local background approach described above explicitly assumes equal contributions from non-FF sources and sinks for soundings in the background versus soundings in the urban plume, which may not always be the case. We then correct for the spatial gradient in contributions from biogenic and pyrogenic fluxes.
As proposed in
Next, the urban–background gradient in such anomalies is calculated as the difference between sounding-specific anomalies and the mean anomaly within the background region:
Flux exchanges from the ocean and chemical transformations (e.g., the CO sink from the hydroxyl radical (OH) and the source from the oxidation of volatile organic compounds, VOCs) are not considered. The average lifetime of CO against OH ranges from a few weeks to several months depending on the season – much longer than the few-hours timescale we care about. Yet, CO can be generated from the oxidation of CH
The uncertainty related to emissions should contain uncertainties from (1) the atmospheric transport (i.e., column footprints), (2) observations, and (3) non-FF sources and sinks, according to Eqs. (1) or (2). We neglect uncertainties from column footprints, assuming that no transport bias exists during either the OCO or the TROPOMI overpass time. The urban–background gradient in non-FF fluxes remains very small compared to FF enhancements (Sect.
We estimate the uncertainties of observed FF enhancements following Eq. (
The retrieval uncertainty (
A key objective of this study is to explain the intra-city variability of ER
Maps of 120 m Local Climate Zone (LCZ) from WUDAPT (
Here, we propose a novel approach to identifying ERs associated with heavy industry in a city. Instead of relying on prior emission inventories that can sometimes be erroneous regarding the magnitudes and the locations of sector-specific activities (see discussions in Sect.
To relate ER
Examples of modeled urban plumes during OCO-3 (red curve) and TROPOMI (blue curve) overpass times (in UTC). The likelihood of these meteorology-only urban plumes (no emission involved) is quantified by the normalized KDE binned in 10 intervals of the modeled air parcel distribution (yellow-green-purple contours). Three types of overpasses are shown, as follows:
ER
We examine impacts on ER
Recall that sounding-specific AKs and wind speeds were considered in the sounding-specific column footprint using X-STILT (Sect.
The second factor is the change in wind directions between two overpass times, which is evaluated using the same algorithm as the urban plume detection in Sect.
Besides changes in wind directions, the CO and CO
The last factor is the urban–background contrast in contributions from non-FF sources and sinks. The biogenic XCO
Although LA is surrounded by occasional intense wildfire activities, the column anomalies due to biomass burning that are suggested by the coupling of GFAS and X-STILT are minimal for the dates we examined. Yet, since wildfire-related ER
Observed enhancements are the net consequence of associated sources/sinks from source regions. That is, a high atmospheric content of CO
In the following subsections, we present ERs for each sounding and the aggregate for each overpass and city. Since the aggregation of sounding-level ERs to a single value per overpass or city is sensitive to the method/statistic adopted, we bootstrapped
A summary of the total power generation capacity (from the Global Power Plant Dataset of the World Resources Institute,
Scatter plots of CO and CO
Combustion efficiencies are generally poor for the two industry- and energy-oriented cities. The overpass-specific ERs span from 9.3
Zibo, along with the nearby county-level city of Zouping, accounted for over one-eighth of the total coal consumption of Shandong Province in 2017. The coal-fired power plants in the area contribute to a total power generation capacity of 9720 MW (Table
Although OCO-3 has sampled the Los Angeles Basin dozens of times to date, many overpasses did not pass the quality check (i.e., QF) and were removed from the final result due to their noticeable shifts in urban plumes between two overpass times (e.g., 3 March, 15 April, and 5 May 2020 for LA; discussed in Sect.
In contrast to LA, where urban plumes are usually well constrained by the basin, wind speeds and directions vary across different overpasses over Shanghai – i.e., there is a southeasterly wind on 4 February and 20 February 2020, a southwesterly wind on 24 February 2020 and 19 February 2021, and a northerly wind on 23 April and 30 December 2020. Such changes in the wind regime between overpasses over Shanghai suggest that soundings from an individual overpass may reflect emission patterns over different source regions, which emphasizes the importance of integrating atmospheric transport when interpreting temporal variations in observation-based ERs. In other words, one cannot simply use all the soundings over a city to calculate ERs; it is necessary to select those soundings that are affected by emissions from that city. The overpass-specific ER ranges from 4.2
Now we focus on the distribution of sounding-level ERs for these two megacities (Fig.
Industrial regions within the LA Basin are concentrated to the south, near the Port of LA; and to the west of downtown, near Los Angeles Airport and the Chevron Refinery in El Segundo (Fig.
In Shanghai, the heavy industry is concentrated to the north of the city center (Fig.
To validate the robustness of such ER shifts related to heavy industry, we tested the use of different percentile thresholds other than the 75th and 95th percentiles to determine industry-dominated soundings (Sect.
We acknowledge that although many iron/steel plants may aim at combusting as much CO as possible before releasing CO into the atmosphere, the indispensable role that CO plays in the iron/steel industry makes it unique when assessing its ER
This study is one of the first to analyze intra-city variations of emission ratios between CO and CO
Pyrogenic anomalies are minimal for the overpasses we examined but should be considered for certain cities (e.g., during dry seasons over Mexico City,
The biggest challenge affecting the robust estimation of spatially resolved ER
A summary of the wind directional shift between OCO-2/3 and TROPOMI overpass times. The
Contrary to previous work relying on inventory-based sector-based ERs, we attribute the intra-urban gradient to heavy industry using an urban land cover classification dataset. Such high-resolution localized maps help identify the observations strongly influenced by heavy industry. Based on a limited sample size, the heavy industry within the Greater Shanghai area is tied to an ER
A city-scale ER
The main limitation of this work is the relatively low sample size, which is largely constrained by the requirement for small differences in overpass times. When more satellite data or upcoming data from geostationary satellites become accessible, intra-city ERs can be used to more robustly assess the temporal variation in sector-oriented combustion efficiency, including across seasons or times (e.g., business-as-usual scenarios versus pandemic-disturbed time frames). Beyond the sheer number of soundings, uncertainty arises when aggregating CO
Another factor that we did not explicitly account for is the secondary CO production from both anthropogenic and biogenic VOCs (AVOCs, BVOCs). Under a cascade of reactions in favorable conditions, VOCs emitted from the upwind source location are oxidized to CO at various rates, which may result in higher CO at the downwind sounding location and divergence between enhancement ratios and emission ratios. As BVOCs are usually associated with shorter lifetimes compared to many AVOCs (e.g.,
This work provides insights into estimating emission ratios from future satellite sensors, as ERs help pinpoint hotspots with poor combustion efficiency, which inform sub-city emission/pollution control efforts.
Satellite-based ER estimates help in the evaluation of sector-specific emission factors and source locations adopted in bottom-up emission inventories (e.g.,
Spatial proxies, including nightlight data from the Black Marble (
We investigated fossil fuel combustion efficiency by quantifying the emission ratios of CO and CO
Future satellites (e.g., GeoCarb, TEMPO, CO2M) will provide better spatial and temporal coverage of XCO
A summary table of sector-specific and city-specific emission ratios of CO to CO
OCO-3 L2 B10r XCO
The supplement related to this article is available online at:
DW designed and carried out this analysis. JL, POW, and PIP supervised this study. RRN, MK, and AE provided the bias-corrected B10 data for the OCO-3 SAMs used in this work. All authors participated in the interpretation of the results and in the writing and editing of the paper.
The contact author has declared that none of the authors has any competing interests.
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The computations presented here were conducted in the Resnick High-Performance Computing Center, a facility supported by the Resnick Sustainability Institute at the California Institute of Technology. The first author appreciates the discussion with Joshua Laughner, Eric Kort, Tomohiro Oda, and John Lin. We thank Julia Marshall and the second anonymous referee for their careful reading of our submitted manuscript and for their constructive suggestions that have helped improve our study.
The production of the OCO-3 science data products used in this paper was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (prime contract number 80NM0018D0004). The research effort was funded by the Jet Propulsion Laboratory Research and Technology Development project R.21.023.106. The analysis was supported by the W. M. Keck Institute for Space Studies and by the National Aeronautics and Space Administration (grant no. 80NSSC21k1064).
This paper was edited by Jason West and reviewed by Stijn Dellaert and Julia Marshall.