FFCO2 Emissions Research Articles

Spatiotemporal variations of fossil fuel CO2 emissions in China: A sectoral allocation approach based on multi-source data

Fossil fuel (FF) CO2 emissions account for the largest portion of human-related CO2 emissions. It is essential to accurately understand the spatial distribution of high-resolution FFCO2 emissions to formulate different carbon emission reduction policies in different regions. Therefore, a sectoral allocation approach was proposed to estimate FFCO2 emissions in China from 2000 to 2021 based on multi-source data. Furthermore, the spatiotemporal characteristics of FFCO2 emissions in different sectors were analyzed at different scales, and the spatial correlation of FFCO2 emissions in the service sector and industrial sector was also evaluated through Moran's index. The results showed that the mean R2 value of the sectoral allocation approach (0.89) exceeds that of the approach using only nighttime light (0.72). Moreover, the calculated results were utilized to analyze the Spatiotemporal variation of FFCO2 emissions. The analysis revealed that China's FFCO2 emissions increased from 3173 Mt in 2000–10662 Mt in 2021. The high emissions of FFCO2 mainly come from the industrial sectors in North China and Central China, as well as the service sectors in the eastern coastal cities and other provincial capital cities. The spatial dependence of FFCO2 emissions in the industrial sector was stronger than that in the service sector, but the spatial dependence of FFCO2 emissions in the service sector showed an increasing trend from 2000 to 2021. These results have important references and implications for region-specific carbon emission reduction policies.

Potential of 14C-based vs. ΔCO-based ΔffCO2 observations to estimate urban fossil fuel CO2 (ffCO2) emissions

Abstract. Atmospheric transport inversions are a powerful tool for independently estimating surface CO2 fluxes from atmospheric CO2 concentration measurements. However, additional tracers are needed to separate the fossil fuel CO2 (ffCO2) emissions from non-fossil CO2 fluxes. In this study, we focus on radiocarbon (14C), the most direct tracer of ffCO2, and the continuously measured surrogate tracer carbon monoxide (CO), which is co-emitted with ffCO2 during incomplete combustion. In the companion paper by Maier et al. (2024), we determined discrete 14C-based and continuous ΔCO-based estimates of the ffCO2 excess concentration (ΔffCO2) compared with a clean-air reference for the urban Heidelberg observation site in southwestern Germany. The ΔCO-based ΔffCO2 concentration was calculated by dividing the continuously measured ΔCO excess concentration by an average 14C-basedΔCO/ΔffCO2 ratio. Here, we use the CarboScope inversion framework adapted for the urban domain around Heidelberg to assess the potential of both types of ΔffCO2 observations to investigate ffCO2 emissions and their seasonal cycle. We find that, although they are more precise, 14C-based ΔffCO2 observations from almost 100 afternoon flask samples collected in the 2 years of 2019 and 2020 are not well suited for estimating robust ffCO2 emissions in the main footprint of this urban area, which has a very heterogeneous distribution of sources including several point sources. The benefit of the continuous ΔCO-based ΔffCO2 estimates is that they can be averaged to reduce the impact of individual hours with an inadequate model performance. We show that the weekly averaged ΔCO-based ΔffCO2 observations allow for a robust reconstruction of the seasonal cycle of the area source ffCO2 emissions from temporally flat a priori emissions. In particular, the distinct COVID-19 signal – with a steep drop in emissions in spring 2020 – is clearly present in these data-driven a posteriori results. Moreover, our top-down results show a shift in the seasonality of the area source ffCO2 emissions around Heidelberg in 2019 compared with the bottom-up estimates from the Netherlands Organization for Applied Scientific Research (TNO). This highlights the huge potential of ΔCO-based ΔffCO2 to validate bottom-up ffCO2 emissions at urban stations if the ΔCO/ΔffCO2 ratios can be determined without biases.

Advancing Regional–Scale Spatio–Temporal Dynamics of FFCO2 Emissions in Great Bay Area

Estimating city–scale emissions using gridded inventories lacks direct, precise measurements, resulting in significant uncertainty. A Kalman filter integrates diverse, uncertain information sources to deliver a reliable, accurate estimate of the true system state. By leveraging multiple gridded inventories and a Kalman filter fusion method, we developed an optimal city–scale (3 km) FFCO2 emission product that incorporates quantified uncertainties and connects global–regional–city scales. Our findings reveal the following: (1) Kalman fusion post–reconstruction reduces estimate uncertainties for 2000–2014 and 2015–2021 to ±9.77% and ±11.39%, respectively, outperforming other inventories and improving accuracy to 73% compared to ODIAC and EDGAR (57%, 65%). (2) Long–term trends in the Greater Bay Area (GBA) show an upward trajectory, with a 2.8% rise during the global financial crisis and a −0.19% decline during the COVID-19 pandemic. Spatial analysis uncovers a “core–subcore–periphery” emission pattern. (3) The core city GZ consistently contributes the largest emissions, followed by DG as the second–largest emitter, and HK as the seventh–highest emitter. Factors influencing the center–shift of the pattern include the urban form of cities, population migration, GDP contribution, but not electricity consumption. The reconstructed method and product offer a reliable solution for the lack of directly observed emissions, enhancing decision–making accuracy for policymakers.

Exploring differences in FFCO2 emissions in the United States: comparison of the Vulcan data product and the EPA national GHG inventory

Quantitative assessment of greenhouse gas emissions is an essential step to plan, track, and verify emission reductions. Multiple approaches have been taken to quantify U.S. CO2 emissions from fossil fuel combustion (FFCO2), the primary driver of global climate change. A 2020 study analyzing atmospheric 14CO2 observations (a key check on bottom-up estimates) and multiple inventories found significant differences in the U.S. total FFCO2 emissions. The specific reasons for the differences were left for future work. Here, we take up this task and explore the differences between two widely used U.S. FFCO2 inventories, the Vulcan FFCO2 emissions data product and the Environmental Protection Agency (EPA) GHG inventory, developed using mostly independent data sources. Where possible, we isolate definitions and data sources to quantify/understand discrepancies. We find that the initial 2011 emissions difference (104 MtC yr−1; RD = 10.7%) can be reduced by aligning the two estimates to account for differing definitions of emission categories or system boundaries. Out of the remaining 90.6 MtC yr−1 gap (RD = 6.2%), we find that differences can be largely explained by data completeness, emission factors, and fuel heating values. The remaining difference, 45.4 MtC yr−1 (3.2%), is difficult to isolate due to limited EPA documentation and disaggregation of emissions by sector/fuel categories. Furthermore, the final net difference obscures countervailing gross differences (∼40 MtC yr−1) within individual sectors. Nevertheless, this comparison suggests the potential for a national estimation approach that can simultaneously satisfy reporting at the national/global scale and the local scale, maintaining internal consistency throughout and offering detailed decision support to a much wider array of stakeholders.

Assumptions about prior fossil fuel inventories impact our ability to estimate posterior net CO2 fluxes that are needed for verifying national inventories

Monitoring national and global greenhouse gas (GHG) emissions is a critical component of the Paris Agreement, necessary to verify collective activities to reduce GHG emissions. Top-down approaches to infer GHG emission estimates from atmospheric data are widely recognized as a useful tool to independently verify emission inventories reported by individual countries under the United Nation Framework Convention on Climate Change. Conventional top-down atmospheric inversion methods often prescribe fossil fuel CO2 emissions (FFCO2) and fit the resulting model values to atmospheric CO2 observations by adjusting natural terrestrial and ocean flux estimates. This approach implicitly assumes that we have perfect knowledge of FFCO2 and that any gap in our understanding of atmospheric CO2 data can be explained by natural fluxes; consequently, it also limits our ability to quantify non-FFCO2 emissions. Using two independent FFCO2 emission inventories, we show that differences in sub-annual emission distributions are aliased to the corresponding posterior natural flux estimates. Over China, for example, where the two inventories show significantly different seasonal variations in FFCO2, the resulting differences in national-scale flux estimates are small but are significant on the subnational scale. We compare natural CO2 flux estimates inferred from in-situ and satellite observations. We find that sparsely distributed in-situ observations are best suited for quantifying natural fluxes and large-scale carbon budgets and less suitable for quantifying FFCO2 errors. Satellite data provide us with the best opportunity to quantify FFCO2 emission errors; a similar result is achievable using dense, regional in-situ measurement networks. Enhancing the top-down flux estimation capability for inventory verification requires a coordinated activity to (a) improve GHG inventories; (b) extend methods that take full advantage of measurements of trace gases that are co-emitted during combustion; and (c) improve atmospheric transport models.

Evaluating the performance of a Picarro G2207-i analyser for high-precision atmospheric O2 measurements

Abstract. Fluxes of oxygen (O2) and carbon dioxide (CO2) in and out of the atmosphere are strongly coupled for terrestrial biospheric exchange processes and fossil fuel combustion but are uncoupled for oceanic air–sea gas exchange. High-precision measurements of both species can therefore provide constraints on the carbon cycle and can be used to quantify fossil fuel CO2 (ffCO2) emission estimates. In the case of O2, however, due to its large atmospheric mole fraction (∼20.9 %) it is very challenging to measure small variations to the degree of precision and accuracy required for these applications. We have tested an atmospheric O2 analyser based on the principle of cavity ring-down spectroscopy (Picarro Inc., model G2207-i), both in the laboratory and at the Weybourne Atmospheric Observatory (WAO) field station in the UK, in comparison to well-established, pre-existing atmospheric O2 and CO2 measurement systems. In laboratory tests analysing dry air in high-pressure cylinders, we found that the best precision was achieved with 30 min averaging and was ±0.5 ppm (∼±2.4 per meg). Also from continuous measurements from a cylinder of dry air, we found the 24 h peak-to-peak range of hourly averaged values to be 1.2 ppm (∼5.8 per meg). These results are close to atmospheric O2 compatibility goals as set by the UN World Meteorological Organization. However, from measurements of ambient air conducted at WAO we found that the built-in water correction of the G2207-i does not sufficiently correct for the influence of water vapour on the O2 mole fraction. When sample air was dried and a 5-hourly baseline correction with a reference gas cylinder was employed, the G2207-i's results showed an average difference from the established O2 analyser of 13.6±7.5 per meg (over 2 weeks of continuous measurements). Over the same period, based on measurements of a so-called “target tank”, analysed for 12 min every 7 h, we calculated a repeatability of ±5.7±5.6 per meg and a compatibility of ±10.0±6.7 per meg for the G2207-i. To further examine the G2207-i's performance in real-world applications we used ambient air measurements of O2 together with concurrent CO2 measurements to calculate ffCO2. Due to the imprecision of the G2207-i, the ffCO2 calculated showed large differences from that calculated from the established measurement system and had a large uncertainty of ±13.0 ppm, which was roughly double that from the established system (±5.8 ppm).

Reductions in California's Urban Fossil Fuel CO2 Emissions During the COVID‐19 Pandemic

AbstractFossil fuel carbon dioxide (CO2) emissions (ffCO2) constitute the majority of greenhouse gas emissions and are the main determinant of global climate change. The COVID‐19 pandemic caused wide‐scale disruption to human activity and provided an opportunity to evaluate our capability to detect ffCO2 emission reductions. Quantifying changes in ffCO2 levels is especially challenging in cities, where climate mitigation policies are being implemented but local emissions lead to spatially and temporally complex atmospheric mixing ratios. Here, we assess ffCO2 emission patterns associated with pandemic‐induced changes to human activity using direct observations of on‐road CO2 mole fractions in the Los Angeles (LA) urban area and analyses of the radiocarbon (14C) content of annual grasses collected by community scientists throughout California, USA. With COVID‐19 mobility restrictions in place in 2020, we observed a significant reduction in ffCO2 levels across California, especially in urban centers. In LA, on‐road CO2 enhancements were 60 ± 16% lower than the corresponding period of 2019 and rebounded to pre‐pandemic levels by 2021. Plant 14C analysis indicated ffCO2 reductions of 5 ± 10 ppm in 2020 relative to pre‐pandemic observations in LA. However, ffCO2 emission trajectories varied substantially by region and sector as COVID‐related restrictions were relaxed. Further development of these techniques could aid efforts to monitor decarbonization in cities, especially in developing countries without established CO2 monitoring infrastructure.

Development of regression models to forecast the CO2 emissions from fossil fuels in the BRICS and MINT countries

This study focuses to propose regression models to forecast the fossil fuel-related carbon dioxide (FFCO2) emissions in the BRICS (Brazil, the Russian Federation, India, China, South Africa) and MINT (Mexico, Indonesia, Nigeria, Turkey) countries. In order to get an overview, a short review on the current status of the fossil fuel use and FFCO2 emissions is firstly presented. Then, statistical regression models are developed and verified by various statistical tests including coefficient of determination, F-and t-tests, and the actual versus predicted data plots. Additionally, the most significant variables affecting the FFCO2 emissions of the BRICS and MINT countries are statistically determined. Moreover, the forecasting accuracies and effectiveness of the proposed models are measured using the mean absolute percentage error. Using the proposed models, furthermore, the future FFCO2 emissions of the BRICS and MINT countries are estimated and discussed. The results show that BRICS and MINT countries will come to a position that will guide the world economy in the near future due to their significant superiorities. The modeling results reveal that the proposed models have a strong practical applicability potential. Moreover, forecasting results clearly demonstrate that significant increases are expected in the future FFCO2 emissions of the BRICS and MINT countries.

Novel quantification of regional fossil fuel CO2 reductions during COVID-19 lockdowns using atmospheric oxygen measurements.

It is not currently possible to quantify regional-scale fossil fuel carbon dioxide (ffCO2) emissions with high accuracy in near real time. Existing atmospheric methods for separating ffCO2 from large natural carbon dioxide variations are constrained by sampling limitations, so that estimates of regional changes in ffCO2 emissions, such as those occurring in response to coronavirus disease 2019 (COVID-19) lockdowns, rely on indirect activity data. We present a method for quantifying regional signals of ffCO2 based on continuous atmospheric measurements of oxygen and carbon dioxide combined into the tracer “atmospheric potential oxygen” (APO). We detect and quantify ffCO2 reductions during 2020–2021 caused by the two U.K. COVID-19 lockdowns individually using APO data from Weybourne Atmospheric Observatory in the United Kingdom and a machine learning algorithm. Our APO-based assessment has near–real-time potential and provides high-frequency information that is in good agreement with the spread of ffCO2 emissions reductions from three independent lower-frequency U.K. estimates.

A model for urban biogenic CO<sub>2</sub> fluxes: Solar-Induced Fluorescence for Modeling Urban biogenic Fluxes (SMUrF v1)

Abstract. When estimating fossil fuel carbon dioxide (FFCO2) emissions from observed CO2 concentrations, the accuracy can be hampered by biogenic carbon exchanges during the growing season, even for urban areas where strong fossil fuel emissions are found. While biogenic carbon fluxes have been studied extensively across natural vegetation types, biogenic carbon fluxes within an urban area have been challenging to quantify due to limited observations and differences between urban and rural regions. Here we developed a simple model representation, i.e., Solar-Induced Fluorescence (SIF) for Modeling Urban biogenic Fluxes (“SMUrF”), that estimates the gross primary production (GPP) and ecosystem respiration (Reco) over cities around the globe. Specifically, we leveraged space-based SIF, machine learning, eddy-covariance (EC) flux data, and ancillary remote-sensing-based products, and we developed algorithms to gap-fill fluxes for urban areas. Grid-level hourly mean net ecosystem exchange (NEE) fluxes are extracted from SMUrF and evaluated against (1) non-gap-filled measurements at 67 EC sites from FLUXNET during 2010–2014 (r>0.7 for most data-rich biomes), (2) independent observations at two urban vegetation and two crop EC sites over Indianapolis from August 2017 to December 2018 (r=0.75), and (3) an urban biospheric model based on fine-grained land cover classification in Los Angeles (r=0.83). Moreover, we compared SMUrF-based NEE with inventory-based FFCO2 emissions over 40 cities and addressed the urban–rural contrast in both the magnitude and timing of CO2 fluxes. To illustrate the application of SMUrF, we used it to interpret a few summertime satellite tracks over four cities and compared the urban–rural gradient in column CO2 (XCO2) anomalies due to NEE against XCO2 enhancements due to FFCO2 emissions. With rapid advances in space-based measurements and increased sampling of SIF and CO2 measurements over urban areas, SMUrF can be useful to inform the biogenic CO2 fluxes over highly vegetated regions during the growing season.

Country-scale trends in air pollution and fossil fuel CO2 emissions during 2001–2018: confronting the roles of national policies and economic growth

Fossil fuel (FF) burning, the main energy source of the modern world’s economy, remains the major source of anthropogenic carbon dioxide (CO2) and pollutants in the atmosphere. Based on 18 years (2001–2018) of aerosol optical depth (AOD) data from Moderate Resolution Imaging Spectroradiometer satellite, FFCO2 emissions from the Open-Data Inventory for Anthropogenic Carbon dioxide, and gross domestic product (GDP) data from the World Bank, we found that air quality, FF consumption, and economy are strongly bonded at the continental scale but decoupled at the national level under favorable policies. The comparison of AOD vs PM2.5 and NO2 over urbanized areas shows that the pollutants leading to the AOD load can vary significantly by country. A strong connection between GDP and FFCO2 emissions indicates that economic growth deeply replies on FF consumption in most countries. Meanwhile, air pollution is more associated with the growing trend than the level of development of a country. With more mature technologies and renewable energy, economies can keep growing without compromising their environment and population health.

The Vulcan Version 3.0 High-Resolution Fossil Fuel CO2 Emissions for the United States.

Estimates of high‐resolution greenhouse gas (GHG) emissions have become a critical component of climate change research and an aid to decision makers considering GHG mitigation opportunities. The “Vulcan Project” is an effort to estimate bottom‐up carbon dioxide emissions from fossil fuel combustion and cement production (FFCO2) for the U.S. landscape at space and time scales that satisfy both scientific and policy needs. Here, we report on the Vulcan version 3.0 which quantifies emissions at a resolution of 1 km2/hr for the 2010–2015 time period. We estimate 2011 FFCO2 emissions of 1,589.9 TgC with a 95% confidence interval of 1,367/1,853 TgC (−14.0%/+16.6%), implying a one‐sigma uncertainty of ~ ±8%. Per capita emissions are larger in states dominated by electricity production and industrial activity and smaller where onroad and building emissions dominate. The U.S. FFCO2 emissions center of mass (CoM) is located in the state of Missouri with mean seasonality that moves on a near‐elliptical NE/SW path. Comparison to ODIAC, a global gridded FFCO2 emissions estimate, shows large total emissions differences (100.4 TgC for year 2011), a spatial correlation of 0.68 (R2), and a mean absolute relative difference at the 1 km2 scale of 104.3%. The Vulcan data product offers a high‐resolution estimate of FFCO2 emissions in every U.S. city, obviating costly development of self‐reported urban inventories. The Vulcan v3.0 annual gridded emissions data product can be downloaded from the Oak Ridge National Laboratory Distributed Active Archive Center (Gurney, Liang, et al., 2019, https://doi.org/10.3334/ORNLDAAC/1741).

A dedicated flask sampling strategy developed for Integrated Carbon Observation System (ICOS) stations based on CO<sub>2</sub> and CO measurements and Stochastic Time-Inverted Lagrangian Transport (STILT) footprint modelling

Abstract. In situ CO2 and CO measurements from five Integrated Carbon Observation System (ICOS) atmosphere stations have been analysed together with footprint model runs from the regional Stochastic Time-Inverted Lagrangian Transport (STILT) model to develop a dedicated strategy for flask sampling with an automated sampler. Flask sampling in ICOS has three different purposes, namely (1) to provide an independent quality control for in situ observations, (2) to provide representative information on atmospheric components currently not monitored in situ at the stations, and (3) to collect samples for 14CO2 analysis that are significantly influenced by fossil fuel CO2 (ffCO2) emission areas. Based on the existing data and experimental results obtained at the Heidelberg pilot station with a prototype flask sampler, we suggest that single flask samples are collected regularly every third day around noon or in the afternoon from the highest level of a tower station. Air samples shall be collected over 1 h, with equal temporal weighting, to obtain a true hourly mean. At all stations studied, more than 50 % of flasks collected around midday will likely be sampled during low ambient variability (<0.5 parts per million (ppm) standard deviation of 1 min values). Based on a first application at the Hohenpeißenberg ICOS site, such flask data are principally suitable for detecting CO2 concentration biases larger than 0.1 ppm with a 1σ confidence level between flask and in situ observations from only five flask comparisons. In order to have a maximum chance to also sample ffCO2 emission areas, additional flasks are collected on all other days in the afternoon. To check if the ffCO2 component will indeed be large in these samples, we use the continuous in situ CO observations. The CO deviation from an estimated background value is determined the day after each flask sampling, and depending on this offset, an automated decision is made as to whether a flask shall be retained for 14CO2 analysis. It turned out that, based on existing data, ffCO2 events of more than 4–5 ppm that would allow ffCO2 estimates with an uncertainty below 30 % were very rare at all stations studied, particularly in summer (only zero to five events per month from May to August). During the other seasons, events could be collected more frequently. The strategy developed in this project is currently being implemented at the ICOS stations.

Policy-Relevant Assessment of Urban CO2 Emissions.

Global fossil fuel carbon dioxide (FFCO2) emissions will be dictated to a great degree by the trajectory of emissions from urban areas. Conventional methods to quantify urban FFCO2 emissions typically rely on self-reported economic/energy activity data transformed into emissions via standard emission factors. However, uncertainties in these traditional methods pose a roadblock to implementation of effective mitigation strategies, independently monitor long-term trends, and assess policy outcomes. Here, we demonstrate the applicability of the integration of a dense network of greenhouse gas sensors with a science-driven building and street-scale FFCO2 emissions estimation through the atmospheric CO2 inversion process. Whole-city FFCO2 emissions agree within 3% annually. Current self-reported inventory emissions for the city of Indianapolis are 35% lower than our optimal estimate, with significant differences across activity sectors. Differences remain, however, regarding the spatial distribution of sectoral FFCO2 emissions, underconstrained despite the inclusion of coemitted species information.

The Relationship between On-Road FFCO2 Emissions and Socio-Economic/Urban Form Factors for Global Cities: Significance, Robustness and Implications

Transportation accounts for 18% of global fossil fuel carbon dioxide (FFCO2) emissions, especially in urban areas. An improved understanding of on-road FFCO2 emissions is essential to both carbon science and mitigation policy. Previous studies have identified the driving factors and quantified their relationship to on-road FFCO2 emissions. However, they have been primarily based on case studies conducted in individual cities, and the research results remain inconclusive due to the considerable heterogeneity of cities and associated outcomes. In order to achieve more general results and to further understand their uncertainties, this study explored the relationships between socio-economic/urban form data and self-reported on-road FFCO2 emissions for a sample of global cities based on the adjusted Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model. The robustness and sensitivity of these relationships was evaluated by introducing artificial errors, conducting cross-validation, and examining various model specifications. Results indicated that fuel economy (p-value < 3.1 × 10−8), vehicle ownership (p-value < 3.0 × 10−4), road density (p-value < 4.4 × 10−3) and population density (p-value < 3.1 × 10−3) were statistically significant factors that correlate with on-road FFCO2 emissions. Of these four variables, fuel economy and vehicle ownership had the most robust relationships. These results offer potential policy insights into on-road FFCO2 emissions mitigation in cities, in addition to offering a means to generate emissions estimates without detailed bottom-up information.

Constraining Fossil Fuel CO2 Emissions From Urban Area Using OCO‐2 Observations of Total Column CO2

AbstractSatellite observations of the total column dry‐air CO2 (XCO2) are expected to support the quantification and monitoring of fossil fuel CO2 (ffCO2) emissions from urban areas. We evaluate the utility of the Orbiting Carbon Observatory 2 (OCO‐2) XCO2 retrievals to optimize whole‐city emissions, using a Bayesian inversion system and high‐resolution transport modeling. The uncertainties of constrained emissions related to transport model, satellite measurements, and local biospheric fluxes are quantified. For the first two uncertainty sources, we examine cities of different landscapes: “plume city” located in relatively flat terrain, represented by Riyadh and Cairo; and “basin city” located in basin terrain, represented by Los Angeles (LA). The retrieved scaling factors of emissions and their uncertainties show prominent variabilities from track to track, due to the varying meteorological conditions and relative locations of the tracks transecting plumes. To explore the performance of multiple tracks in retrieving emissions, pseudo data experiments are carried out. The estimated least numbers of tracks required to constrain the total emissions for Riyadh (<10% uncertainty), Cairo (<10%), and LA (<5%) are 8, 5, and 7, respectively. Additionally, to evaluate the impact of biospheric fluxes on derivation of the ffXCO2 enhancements, we conduct simulations for Pearl River Delta metropolitan area. Significant fractions of local XCO2 enhancements associated with local biospheric XCO2 variations are shown, which potentially lead to biased estimates of ffCO2 emissions. We demonstrate that satellite measurements can be used to improve urban ffCO2 emissions with a sufficient amount of measurements and appropriate representations of the uncertainty components.

High-resolution spatiotemporal patterns of China’s FFCO2 emissions under the impact of LUCC from 2000 to 2015

Fossil fuel carbon dioxide (FFCO2) emissions have become a principal driver behind the increase of atmospheric CO2 concentration and spatiotemporal variations of atmospheric CO2 in the urban surface layer. This study quantifies the 2000–2015 urban high-resolution spatiotemporal patterns of China’s FFCO2 emissions under the impact of the land-use and land-cover change. Multi-source data were used together with various up-to-date geostatistics and spatial analysis methods. FFCO2 emissions were determined to rise over the 15 years in the highest emitting cities in the South and East of China. The high-value clusters inside of all cities expanded outward from their city centers and in some cases transferred to economic development zones or new city centers, while the expansion speeds and variation time were found to differ significantly. We found further that then FFCO2 emissions spatial distribution is interconnected with diverse factors: urbanization, and either croplands (rainfed, irrigated, and post-flooding) or native vegetation, being the two most important. As expected, the increase in urban areas was associated with increased FFCO2 emissions, while the wettability in croplands or the increase in native vegetation have an association with the decrease of FFCO2 emissions. Unlike previous studies, we have found no change associated with changes in water cover. Finally, while the primary source of FFCO2 emissions is still coal, there has been a gradual move to cleaner energy (natural gas in Beijing) or more efficient industrial processes (Wuxi and Dalian), although diverse industrial structures and energy efficiencies exist. Over time, the current spatial patterns of FFCO2 emissions in China will conflict with these trends at the macroscale.

The Hestia fossil fuel CO<sub>2</sub> emissions data product for the Los Angeles megacity (Hestia-LA)

Abstract. High-resolution bottom-up estimation provides a detailed guide for city greenhouse gas mitigation options, offering details that can increase the economic efficiency of emissions reduction options and synergize with other urban policy priorities at the human scale. As a critical constraint to urban atmospheric CO2 inversion studies, bottom-up spatiotemporally explicit emissions data products are also necessary to construct comprehensive urban CO2 emission information systems useful for trend detection and emissions verification. The “Hestia Project” is an effort to provide bottom-up granular fossil fuel (FFCO2) emissions for the urban domain with building/street and hourly space–time resolution. Here, we report on the latest urban area for which a Hestia estimate has been completed – the Los Angeles megacity, encompassing five counties: Los Angeles County, Orange County, Riverside County, San Bernardino County and Ventura County. We provide a complete description of the methods used to build the Hestia FFCO2 emissions data product for the years 2010–2015. We find that the LA Basin emits 48.06 (±5.3) MtC yr−1, dominated by the on-road sector. Because of the uneven spatial distribution of emissions, 10 % of the largest-emitting grid cells account for 93.6 %, 73.4 %, 66.2 %, and 45.3 % of the industrial, commercial, on-road, and residential sector emissions, respectively. Hestia FFCO2 emissions are 10.7 % larger than the inventory estimate generated by the local metropolitan planning agency, a difference that is driven by the industrial and electricity production sectors. The detail of the Hestia-LA FFCO2 emissions data product offers the potential for highly targeted, efficient urban greenhouse gas emissions mitigation policy. The Hestia-LA v2.5 emissions data product can be downloaded from the National Institute of Standards and Technology repository (https://doi.org/10.18434/T4/1502503, Gurney et al., 2019).

Working towards confident spaceborne monitoring of carbon emissions from cities using Orbiting Carbon Observatory-2

Cities are responsible for 70% of fossil fuel CO2 emissions (FFCO2), and these emissions are poorly monitored. As a result, the use of spaceborne observation as a tool for addressing urban FFCO2 emissions has intensified. This work aims to understand the suitability of modern spaceborne remote sensing for capturing CO2 urban enhancement and for monitoring FFCO2 emissions from cities. We have used the first four years of observations from NASA Orbiting Carbon Observatory 2 (OCO-2) to collect CO2 urban anomalies (XCO2ano) from large cities (>500 km2 area) by combining urban-to-rural gradient and statistical filtering approaches. Approximately half of all XCO2ano (44%) were significant and noise-free to meet the accuracy requirements for city-scale applications. We captured positive CO2 enhancement over urban areas compared to background at a global scale. Median XCO2ano estimates were positive at 1.07 ± 0.80 ppm (worldwide), 1.05 ± 0.80 ppm (Northern Hemisphere), and 0.96 ± 0.80 ppm (Southern Hemisphere). Most monthly XCO2ano (~83%) emerged in four regions of the Northern Hemisphere (East Asia, Europe, North America, and South Asia), where most cities with strong emissions are located. We report that the XCO2ano from numerous cities have a moderate linear relationship with city size (r = 0.54–0.65) and FFCO2 strength (r = 0.64). As expected, five out of the six strongest XCO2ano were found over the megacities (>10 million population) of Los Angeles (2.04 ± 0.91 ppm), Tehran (1.94 ± 1.54 ppm), Rhine-Main Metropolitan Area (1.51 ± 0.59 ppm), Pearl River Delta (1.48 ± 1.11 ppm), and Seoul (1.47 ± 1.72 ppm), with Houston (1.50 ± 0.72 ppm) as the only non-megacity. Overall, we have shown that spaceborne remote sensing of XCO2 is suitable for capturing CO2 enhancement over a wide range of cities (especially in the Northern Hemisphere). Though we registered the global-scale urban footprint in CO2 signal and demonstrated the sensitivity of XCO2ano to city size and FFCO2, spaceborne remote sensing is still limited in its provision of columnar CO2 enhancement which is fundamentally linked with underlying urban emissions. This linkage may be more effectively addressed when spaceborne remote sensing becomes increasingly optimized for city-scale applications.

Detection of fossil fuel emission trends in the presence of natural carbon cycle variability

Atmospheric CO2 observations have the potential to monitor regional fossil fuel emission (FFCO2) changes to support carbon mitigation efforts such as the Paris Accord, but they must contend with the confounding impacts of the natural carbon cycle. Here, we quantify trend detection time and magnitude in gridded total CO2 fluxes—the sum of FFCO2 and natural carbon fluxes—under an idealized assumption that monthly total CO2 fluxes can be perfectly resolved at a 2°×2° resolution. Using Coupled Model Intercomparison Project 5 (CMIP5) ‘business-as-usual’ emission scenarios to represent FFCO2 and simulated net biome exchange (NBE) to represent natural carbon fluxes, we find that trend detection time for the total CO2 fluxes at such a resolution has a median of 10 years across the globe, with significant spatial variability depending on FFCO2 magnitude and NBE variability. Differences between trends in the total CO2 fluxes and the underlying FFCO2 component highlight the role of natural carbon cycle variability in modulating regional detection of FFCO2 emission trends using CO2 observations alone, particularly in the tropics and subtropics where mega-cities with large populations are developing rapidly. Using CO2 estimates alone at such a spatiotemporal resolution can only quantify fossil fuel trends in a few places—mostly limited to arid regions. For instance, in the Middle East, FFCO2 can explain more than 75% of the total CO2 trends in ∼70% of the grids, but only ∼20% of grids in China can meet such criteria. Only a third of the 25 megacities we analyze here show total CO2 trends that are primarily explained (>75%) by FFCO2. Our analysis provides a theoretical baseline at a global scale for the design of regional FFCO2 monitoring networks and underscores the importance of estimating biospheric interannual variability to improve the accuracy of FFCO2 trend monitoring. We envision that this can be achieved with a fully integrated carbon cycle assimilation system with explicit constraints on FFCO2 and NBE, respectively.