Optimal chest compression for cardiac arrest until the establishment of ECPR: Secondary analysis of the SAVE-J II study.

INTRODUCTION: The widespread incorporation of extracorporeal cardiopulmonary resuscitation (ECPR) for out-of-hospital cardiac arrest requires the delivery of effective and high-quality chest compressions prior to the initiation of ECPR. The aim of this study was to evaluate and compare the effectiveness of mechanical and manual chest compressions until the initiation of ECPR. METHODS: This study was a secondary analysis of the Japanese retrospective multicenter registry "Study of Advanced Life Support for Ventricular Fibrillation by Extracorporeal Circulation II (SAVE-J II)". Patients were divided into two groups, one receiving mechanical chest compressions and the other receiving manual chest compressions. The primary outcome measure was mortality at hospital discharge, while the secondary outcome was the cerebral performance category (CPC) score at discharge. RESULTS: Of the 2157 patients enrolled in the SAVE-J II trial, 453 patients (329 in the manual compression group and 124 in the mechanical compression group) were included in the final analysis. Univariate analysis showed a significantly higher mortality rate at hospital discharge in the mechanical compression group compared to the manual compression group (odds ratio [95% CI] = 2.32 [1.34-4.02], p = 0.0026). Multivariate analysis showed that mechanical chest compressions were an independent factor associated with increased mortality at hospital discharge (adjusted odds ratio [95% CI] = 2.00 [1.11-3.58], p = 0.02). There was no statistically significant difference in CPC between the two groups. CONCLUSION: For patients with out-of-hospital cardiopulmonary arrest who require ECPR, extreme caution should be used when performing mechanical chest compressions.

Anthropogenic Osmium in Macroalgae from Tokyo Bay Reveals Widespread Contamination from Municipal Solid Waste.

Human activity is influencing the global osmium cycle, driving the Os isotopic composition (187Os/188Os) of the hydrosphere and associated sedimentary material to lower values. Here, we present the Re and Os abundance and isotope systematics of macroalgae, a proxy for seawater, from Tokyo Bay to elucidate the potential sources of anthropogenic Os to the Pacific Ocean. Macroalgae from the Uraga Channel, which connects Tokyo Bay to the Pacific Ocean, record relatively low Os abundances (∼10.1 pg/g) and an 187Os/188Os of ∼0.9, indicative of surface ocean seawater. Contrastingly, macroalgae within the bay closest to central Tokyo record the highest Os abundances (∼22.8 pg/g) and lowest 187Os/188Os values (∼0.47), suggesting contamination from human activity. To determine the source of anthropogenic Os, we have developed the first Os emission inventory, based on the East Asian Air Pollutant Emission Grid database (EAGrid2010). The close relationship (R2 = 0.67 and p-value = <0.05) between Os inventories and macroalgal data suggests that municipal solid waste incinerators (MSWIs) are the dominant source of Os to Tokyo Bay. Projections for Japan estimate that 26-18+38 ng Os/m2/yr is released from MSWI smokestacks, leading to a concentration in precipitation of 26-18+38 fg/g, identifying MSWIs as a major contributor of anthropogenic Os to the hydrological cycle.

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.

Intrajejunal infusion of 2-monoacylglycerol reduced food intake without inducing diarrhea in rats.

Some nutrients, such as carbohydrate, fat and protein, are known to stimulate satiety. However, the effect of sn-2-monoacylglycerol (2-MG), one of the digestive products of triglycerides, on food intake is still unclear. In the present study, the effects of 2-MG on food intake and diarrhea were evaluated and compared with long-chain fatty acid (LCFA) in rats by intrajejunal infusion. Intrajejunal infusion of 2-MG reduced food intake. In addition, 2-MG did not induce diarrhea at the condition that it comparably reduced food intake as compared with LCFA. These results suggest that 2-MG stimulates satiety without inducing diarrhea, different from LCFA.

Tracking unaccounted greenhouse gas emissions due to the war in Ukraine since 2022.

Accounting and reporting of greenhouse gas (GHG) emissions are mandatory for Parties under the Paris Agreement. Emissions reporting is important for understanding the global carbon cycle and for addressing global climate change. However, in a period of open conflict or war, military emissions increase significantly and the accounting system is not currently designed to account adequately for this source. In this paper we analyze how, during the first 18 months of the 2022/2023 full-scale war in Ukraine, GHG national inventory reporting to the UNFCCC was affected. We estimated the decrease of emissions due to a reduction in traditional human activities. We identified major, war-related, emission processes from the territory of Ukraine not covered by current GHG inventory guidelines and that are not likely to be included in national inventory reports. If these emissions are included, they will likely be incorporated in a way that is not transparent with potentially high uncertainty. We analyze publicly available data and use expert judgment to estimate such emissions from (1) the use of bombs, missiles, barrel artillery, and mines; (2) the consumption of oil products for military operations; (3) fires at petroleum storage depots and refineries; (4) fires in buildings and infrastructure facilities; (5) fires on forest and agricultural lands; and (6) the decomposition of war-related garbage/waste. Our estimate of these war-related emissions of carbon dioxide, methane, and nitrous oxide for the first 18 months of the war in Ukraine is 77 MtCO2-eq. with a relative uncertainty of +/-22 % (95 % confidence interval).

Satellites capture socioeconomic disruptions during the 2022 full-scale war in Ukraine.

Since February 2022, the full-scale war in Ukraine has been strongly affecting society and economy in Ukraine and beyond. Satellite observations are crucial tools to objectively monitor and assess the impacts of the war. We combine satellite-based tropospheric nitrogen dioxide (NO2) and carbon dioxide (CO2) observations to detect and characterize changes in human activities, as both are linked to fossil fuel combustion processes. We show significantly reduced NO2 levels over the major Ukrainian cities, power plants and industrial areas: the NO2 concentrations in the second quarter of 2022 were 15-46% lower than the same quarter during the reference period 2018-2021, which is well below the typical year-to-year variability (5-15%). In the Ukrainian capital Kyiv, the NO2 tropospheric column monthly average in April 2022 was almost 60% smaller than 2019 and 2021, and about 40% smaller than 2020 (the period mostly affected by the COVID-19 restrictions). Such a decrease is consistent with the essential reduction in population and corresponding emissions from the transport and commercial/residential sectors over the major Ukrainian cities. The NO2 reductions observed in the industrial regions of eastern Ukraine reflect the decline in the Ukrainian industrial production during the war (40-50% lower than in 2021), especially from the metallurgic and chemical industry, which also led to a decrease in power demand and corresponding electricity production by thermal power plants (which was 35% lower in 2022 compared to 2021). Satellite observations of land properties and thermal anomalies indicate an anomalous distribution of fire detections along the front line, which are attributable to shelling or other intentional fires, rather than the typical homogeneously distributed fires related to crop harvesting. The results provide timely insights into the impacts of the ongoing war on the Ukrainian society and illustrate how the synergic use of satellite observations from multiple platforms can be useful in monitoring significant societal changes. Satellite-based observations can mitigate the lack of monitoring capability during war and conflicts and enable the fast assessment of sudden changes in air pollutants and other relevant parameters.

Decadal decrease in Los Angeles methane emissions is much smaller than bottom-up estimates.

Methane, a powerful greenhouse gas, has a short atmospheric lifetime ( ~ 12 years), so that emissions reductions will have a rapid impact on climate forcing. In megacities such as Los Angeles (LA), natural gas (NG) leakage is the primary atmospheric methane source. The magnitudes and trends of fugitive NG emissions are largely unknown and need to be quantified to verify compliance with emission reduction targets. Here we use atmospheric remote sensing data to show that, in contrast to the observed global increase in methane emissions, LA area emissions decreased during 2011-2020 at a mean rate of (-1.57 ± 0.41) %/yr. However, the NG utility calculations indicate a much larger negative emissions trend of -5.8 %/yr. The large difference between top-down and bottom-up trends reflects the uncertainties in estimating the achieved emissions reductions. Actions taken in LA can be a blueprint for COP28 and future efforts to reduce methane emissions.

Solvent-responsive structural colored balloons.

The structural colored balloons (SCBs) consisting of polymer microcapsules showed several structural colors developed by optical thin-layer interference. The SCBs were prepared using a mixture of low- and high-molecular-weight polystyrene to give solvent responsiveness. When the surrounding solvent was transferred from water to the acetone/water mixture using a flow cell, the SCBs swelled at first and shrunk subsequently. The gradual color change of the SCBs was observed along with the size change. The color change accompanying the size change was successfully reproduced by assuming that the total amount of polymer in the thin film does not change. The swelling rate was rationalized by the diffusion of solvent through the shell polystyrene film to the inside of the balloons.

GHG Monitoring Project for the Global Stocktake 2023: implications of the COP26 Japan Pavilion seminar.

During the 2021 Glasgow Climate Change Conference (COP26), a hybrid seminar event "Greenhouse gas (GHG) Monitoring Project for the Global Stocktake 2023" was held at the COP26 Japan Pavilion on 2nd of November 2011. The participants presented and discussed science-based GHG monitoring approaches in support of the Global Stocktake. This report summarizes the five research talks given at the event.

Bile cytology diagnosis in challenging cases: Validation of diagnostic bile cytology criteria and extensive study for immunocytochemical markers.

BACKGROUND: Bile cytology is useful in diagnosing biliary tract lesions, albeit often challenging due to equivocal findings. To achieve better diagnoses for clinical decisions, we conducted cytomorphological and immunocytochemical studies of bile cytology cases. MATERIALS AND METHODS: We re-evaluated 40 bile cytology cases with initial equivocal diagnoses, taken from the cytology records of Jichi Medical University Hospital, including 1778 bile cytology specimens. First, we assessed the cases by the diagnostic bile cytology criteria of the Japanese Society of Clinical Cytology. Second, we searched for useful immunocytochemical markers by extensive immunohistochemical analyses using tissue microarray for 10 antibodies: S100P, IMP3, GLUT1, p53, S100A4, Mapsin, MUC17, CD10, MDM2, and SMAD4. Microarrays were from 257 extrahepatic bile duct carcinoma cases. To elucidate the utility of immunocytochemistry, we applied selected markers to immunocytochemical evaluation of the equivocal cases after cell transfer. RESULTS: The criteria indicated a sensitivity 60%, specificity 87%, and accuracy 70%. Irregularly overlapping (88%), arranged (96%), and shaped (76%) nuclei were more common in malignant cases, while enlarged nuclei were more frequent in benign cases (67% vs. 28%). We applied S100P and IMP3, which showed higher accuracy (88% and 77%) in tissue microarray, to immunocytochemistry. The sensitivity of S100P and IMP3 were 69% and 70%, respectively. The specificity of S100P and IMP3 were 50% and 100%, respectively. CONCLUSION: The criteria showed a certain effectiveness even in challenging cases, and some pitfalls associated with reactive changes of benign cells. Although comprehensive diagnosis including cytomorphology seems preferable, S100P and IMP3 are promising immunocytochemical markers.

An Interpolation Method to Reduce the Computational Time in the Stochastic Lagrangian Particle Dispersion Modeling of Spatially Dense XCO2 Retrievals.

A growing constellation of satellites is providing near-global coverage of column-averaged CO2 observations. Launched in 2019, NASA's OCO-3 instrument is set to provide XCO2 observations at a high spatial and temporal resolution for regional domains (100 × 100 km). The atmospheric column version of the Stochastic Time-Inverted Lagrangian Transport (X-STILT) model is an established method of determining the influence of upwind sources on column measurements of the atmosphere, providing a means of analysis for current OCO-3 observations and future space-based column-observing missions. However, OCO-3 is expected to provide hundreds of soundings per targeted observation, straining this already computationally intensive technique. This work proposes a novel scheme to be used with the X-STILT model to generate upwind influence footprints with less computational expense. The method uses X-STILT generated influence footprints from a key subset of OCO-3 soundings. A nonlinear weighted averaging is applied to these footprints to construct additional footprints for the remaining soundings. The effects of subset selection, meteorological data, and topography are investigated for two test sites: Los Angeles, California, and Salt Lake City, Utah. The computational time required to model the source sensitivities for OCO-3 interpretation was reduced by 62% and 78% with errors smaller than other previously acknowledged uncertainties in the modeling system (OCO-3 retrieval error, atmospheric transport error, prior emissions error, etc.). Limitations and future applications for future CO2 missions are also discussed.

Assessing the recent impact of COVID-19 on carbon emissions from China using domestic economic data.

The outbreak of coronavirus disease 2019 (COVID-19) has caused tremendous loss to human life and economic decline in China and worldwide. It has significantly reduced gross domestic product (GDP), power generation, industrial activity and transport volume; thus, it has reduced fossil-related and cement-induced carbon dioxide (CO2) emissions in China. Due to time delays in obtaining activity data, traditional emissions inventories generally involve a 2-3-year lag. However, a timely assessment of COVID-19's impact on provincial CO2 emission reductions is crucial for accurately understanding the reduction and its implications for mitigation measures; furthermore, this information can provide constraints for modeling studies. Here, we used national and provincial GDP data and the China Emission Accounts and Datasets (CEADs) inventory to estimate the emission reductions in the first quarter (Q1) of 2020. We find a reduction of 257.7 Mt. CO2 (11.0%) over Q1 2019. The secondary industry contributed 186.8 Mt. CO2 (72.5%) to the total reduction, largely due to lower coal consumption and cement production. At the provincial level, Hubei contributed the most to the reductions (40.6 Mt) due to a notable decrease of 48.2% in the secondary industry. Moreover, transportation significantly contributed (65.1 Mt), with a change of -22.3% in freight transport and -59.1% in passenger transport compared with Q1 2019. We used a point, line and area sources (PLAS) method to test the GDP method, producing a close estimate (reduction of 10.6%). One policy implication is a change in people's working style and communication methods, realized by working from home and holding teleconferences, to reduce traffic emissions. Moreover, GDP is found to have potential merit in estimating emission changes when detailed energy activity data are unavailable. We provide provincial data that can serve as spatial disaggregation constraints for modeling studies and further support for both the carbon cycle community and policy makers.

Regional impacts of COVID-19 on carbon dioxide detected worldwide from space.

Activity reductions in early 2020 due to the coronavirus disease 2019 pandemic led to unprecedented decreases in carbon dioxide (CO2) emissions. Despite their record size, the resulting atmospheric signals are smaller than and obscured by climate variability in atmospheric transport and biospheric fluxes, notably that related to the 2019­2020 Indian Ocean Dipole. Monitoring CO2 anomalies and distinguishing human and climatic causes thus remain a new frontier in Earth system science. We show that the impact of short-term regional changes in fossil fuel emissions on CO2 concentrations was observable from space. Starting in February and continuing through May, column CO2 over many of the world's largest emitting regions was 0.14 to 0.62 parts per million less than expected in a pandemic-free scenario, consistent with reductions of 3 to 13% in annual global emissions. Current spaceborne technologies are therefore approaching levels of accuracy and precision needed to support climate mitigation strategies with future missions expected to meet those needs.

Comparing a global high-resolution downscaled fossil fuel CO2 emission dataset to local inventory-based estimates over 14 global cities.

BACKGROUND: Compilation of emission inventories (EIs) for cities is a whole new challenge to assess the subnational climate mitigation effort under the Paris Climate Agreement. Some cities have started compiling EIs, often following a global community protocol. However, EIs are often difficult to systematically examine because of the ways they were compiled (data collection and emission calculation) and reported (sector definition and direct vs consumption). In addition, such EI estimates are not readily applicable to objective evaluation using modeling and observations due to the lack of spatial emission extents. City emission estimates used in the science community are often based on downscaled gridded EIs, while the accuracy of the downscaled emissions at city level is not fully assessed. RESULTS: This study attempts to assess the utility of the downscaled emissions at city level. We collected EIs from 14 major global cities and compare them to the estimates from a global high-resolution fossil fuel CO2 emission data product (ODIAC) commonly used in the science research community. We made necessary adjustments to the estimates to make our comparison as reasonable as possible. We found that the two methods produce very close area-wide emission estimates for Shanghai and Delhi (< 10% difference), and reach good consistency in half of the cities examined (< 30% difference). The ODIAC dataset exhibits a much higher emission compared to inventory estimates in Cape Town (+ 148%), Sao Paulo (+ 43%) and Beijing (+ 40%), possibly related to poor correlation between nightlight intensity with human activity, such as the high-emission and low-lighting industrial parks in developing countries. On the other hand, ODIAC shows lower estimates in Manhattan (- 62%), New York City (- 45%), Washington D.C. (- 42%) and Toronto (- 33%), all located in North America, which may be attributable to an underestimation of residential emissions from heating in ODIAC's nightlight-based approach, and an overestimation of emission from ground transportation in registered vehicles statistics of inventory estimates. CONCLUSIONS: The relatively good agreement suggests that the ODIAC data product could potentially be used as a first source for prior estimate of city-level CO2 emission, which is valuable for atmosphere CO2 inversion modeling and comparing with satellite CO2 observations. Our compilation of in-boundary emission estimates for 14 cities contributes towards establishing an accurate inventory in-boundary global city carbon emission dataset, necessary for accountable local climate mitigation policies in the future.

Using Space-Based Observations and Lagrangian Modeling to Evaluate Urban Carbon Dioxide Emissions in the Middle East.

Improved observational understanding of urban CO2 emissions, a large and dynamic global source of fossil CO2, can provide essential insights for both carbon cycle science and mitigation decision making. Here we compare three distinct global CO2 emissions inventory representations of urban CO2 emissions for five Middle Eastern cities (Riyadh, Mecca, Tabuk, Jeddah, and Baghdad) and use independent satellite observations from the Orbiting Carbon Observatory-2 (OCO-2) satellite to evaluate the inventory representations of afternoon emissions. We use the column version of the Stochastic Time-Inverted Lagrangian Transport (X-STILT) model to account for atmospheric transport and link emissions to observations. We compare XCO2 simulations with observations to determine optimum inventory scaling factors. Applying these factors, we find that the average summed emissions for all five cities are 100 MtC year-1 (50-151, 90% CI), which is 2.0 (1.0, 3.0) times the average prior inventory magnitudes. The total adjustment of the emissions of these cities comes out to ~7% (0%, 14%) of total Middle Eastern emissions (~700 MtC year-1). We find our results to be insensitive to the prior spatial distributions in inventories of the cities' emissions, facilitating robust quantitative assessments of urban emission magnitudes without accurate high-resolution gridded inventories.

Statistical characterization of urban CO2 emission signals observed by commercial airliner measurements.

Cities are responsible for the largest anthropogenic CO2 emissions and are key to effective emission reduction strategies. Urban CO2 emissions estimated from vertical atmospheric measurements can contribute to an independent quantification of the reporting of national emissions and will thus have political implications. We analyzed vertical atmospheric CO2 mole fraction data obtained onboard commercial aircraft in proximity to 36 airports worldwide, as part of the Comprehensive Observation Network for Trace gases by Airliners (CONTRAIL) program. At many airports, we observed significant flight-to-flight variations of CO2 enhancements downwind of neighboring cities, providing advective fingerprints of city CO2 emissions. Observed CO2 variability increased with decreasing altitude, the magnitude of which varied from city to city. We found that the magnitude of CO2 variability near the ground (~1 km altitude) at an airport was correlated with the intensity of CO2 emissions from a nearby city. Our study has demonstrated the usefulness of commercial aircraft data for city-scale anthropogenic CO2 emission studies.

A city-level comparison of fossil-fuel and industry processes-induced CO2 emissions over the Beijing-Tianjin-Hebei region from eight emission inventories.

BACKGROUND: Quantifying CO2 emissions from cities is of great importance because cities contribute more than 70% of the global total CO2 emissions. As the largest urbanized megalopolis region in northern China, the Beijing-Tianjin-Hebei (Jing-Jin-Ji, JJJ) region (population: 112.7 million) is under considerable pressure to reduce carbon emissions. Despite the several emission inventories covering the JJJ region, a comprehensive evaluation of the CO2 emissions at the prefectural city scale in JJJ is still limited, and this information is crucial to implementing mitigation strategies. RESULTS: Here, we collected and analyzed 8 published emission inventories to assess the emissions and uncertainty at the JJJ city level. The results showed that a large discrepancy existed in the JJJ emissions among downscaled country-level emission inventories, with total emissions ranging from 657 to 1132 Mt CO2 (or 849 ± 214 for mean ± standard deviation (SD)) in 2012, while emission estimates based on provincial-level data estimated emissions to be 1038 and 1056 Mt. Compared to the mean emissions of city-data-based inventories (989 Mt), provincial-data-based inventories were 6% higher, and national-data-based inventories were 14% lower. Emissions from national-data-based inventories were 53-75% lower in the high-emitting industrial cities of Tangshan and Handan, while they were 47-160% higher in Beijing and Tianjin than those from city-data-based inventories. Spatially, the emissions pattern was consistent with the distribution of urban areas, and urban emissions in Beijing contributed 50-70% of the total emissions. Higher emissions from Beijing and Tianjin resulted in lower estimates of prefectural cities in Hebei for some national inventories. CONCLUSIONS: National-level data-based emission inventories produce large differences in JJJ prefectural city-level emission estimates. The city-level statistics data-based inventories produced more consistent estimates. The consistent spatial distribution patterns recognized by these inventories (such as high emissions in southern Beijing, central Tianjin and Tangshan) potentially indicate areas with robust emission estimates. This result could be useful in the efficient deployment of monitoring instruments, and if proven by such measurements, it will increase our confidence in inventories and provide support for policy makers trying to reduce emissions in key regions.

Daily CO2 Emission Reduction Indicates the Control of Activities to Contain COVID-19 in China.

Lockdown measures are essential to containing the spread of coronavirus disease 2019 (COVID-19), but they will slow down economic growth by reducing industrial and commercial activities. However, the benefits of activity control from containing the pandemic have not been examined and assessed. Here we use daily carbon dioxide (CO2) emission reduction in China estimated from statistical data for energy consumption and satellite data for nitrogen dioxide (NO2) measured by the Ozone Monitoring Instrument (OMI) as an indicator for reduced activities consecutive to a lockdown. We perform a correlation analysis to show that a 1% day-1 decrease in the rate of COVID-19 cases is associated with a reduction in daily CO2 emissions of 0.22% ± 0.02% using statistical data for energy consumption relative to emissions without COVID-19, or 0.20% ± 0.02% using satellite data for atmospheric column NO2. We estimate that swift action in China is effective in limiting the number of COVID-19 cases <100,000 with a reduction in CO2 emissions of up to 23% by the end of February 2020, whereas a 1-week delay would have required greater containment and a doubling of the emission reduction to meet the same goal. By analyzing the costs of health care and fatalities, we find that the benefits on public health due to reduced activities in China are 10-fold larger than the loss of gross domestic product. Our findings suggest an unprecedentedly high cost of maintaining activities and CO2 emissions during the COVID-19 pandemic and stress substantial benefits of containment in public health by taking early actions to reduce activities during the outbreak of COVID-19.

Exploiting OMI NO2 satellite observations to infer fossil-fuel CO2 emissions from U.S. megacities.

Fossil-fuel CO2 emissions and their trends in eight U.S. megacities during 2006-2017 are inferred by combining satellite-derived NOX emissions with bottom-up city-specific NOX-to-CO2 emission ratios. A statistical model is fit to a collection NO2 plumes observed from the Ozone Monitoring Instrument (OMI), and is used to calculate top-down NOX emissions. Decreases in OMI-derived NOX emissions are observed across the eight cities from 2006 to 2017 (-17% in Miami to -58% in Los Angeles), and are generally consistent with long-term trends of bottom-up inventories (-25% in Miami to -49% in Los Angeles), but there are some interannual discrepancies. City-specific NOX-to-CO2 emission ratios, used to calculate inferred CO2, are estimated through annual bottom-up inventories of NOX and CO2 emissions disaggregated to 1 × 1 km2 resolution. Over the study period, NOX-to-CO2 emission ratios have decreased by ~40% nationwide (-24% to -51% for our studied cities), which is attributed to a faster reduction in NOX when compared to CO2 due to policy regulations and fuel type shifts. Combining top-down NOX emissions and bottom-up NOX-to-CO2 emission ratios, annual fossil-fuel CO2 emissions are derived. Inferred OMI-based top-down CO2 emissions trends vary between +7% in Dallas to -31% in Phoenix. For 2017, we report annual fossil-fuel CO2 emissions to be: Los Angeles 113 ±â€¯49 Tg/yr; New York City 144 ±â€¯62 Tg/yr; and Chicago 55 ±â€¯24 Tg/yr. A study in the Los Angeles area, using independent methods, reported a 2013-2016 average CO2 emissions rate of 104 Tg/yr and 120 Tg/yr, which suggests that the CO2 emissions from our method are in good agreement with other studies' top-down estimates. We anticipate future remote sensing instruments - with better spatial and temporal resolution - will better constrain the NOX-to-CO2 ratio and reduce the uncertainty in our method.

Evaluating nighttime lights and population distribution as proxies for mappinganthropogenic CO2 emission in Vietnam, Cambodia and Laos.

Tracking spatiotemporal changes in GHG emissions is key to successful implementation of the United Nations Framework Convention on Climate Change (UNFCCC). And while emission inventories often provide a robust tool to track emission trends at the country level, subnational emission estimates are often not reported or reports vary in robustness as the estimates are often dependent on the spatial modeling approach and ancillary data used to disaggregate the emission inventories. Assessing the errors and uncertainties of the subnational emission estimates is fundamentally challenging due to the lack of physical measurements at the subnational level. To begin addressing the current performance of modeled gridded CO2 emissions, this study compares two common proxies used to disaggregate CO2 emission estimates. We use a known gridded CO2 model based on satellite-observed nighttime light (NTL) data (Open Source Data Inventory for Anthropogenic CO2, ODIAC) and a gridded population dataset driven by a set of ancillary geospatial data. We examine the association at multiple spatial scales of these two datasets for three countries in Southeast Asia: Vietnam, Cambodia and Laos and characterize the spatiotemporal similarities and differences for 2000, 2005, and 2010. We specifically highlight areas of potential uncertainty in the ODIAC model, which relies on the single use of NTL data for disaggregation of the non-point emissions estimates. Results show, over time, how a NTL-based emissions disaggregation tends to concentrate CO2 estimates in different ways than population-based estimates at the subnational level. We discuss important considerations in the disconnect between the two modeled datasets and argue that the spatial differences between data products can be useful to identify areas affected by the errors and uncertainties associated with the NTL-based downscaling in a region with uneven urbanization rates.