Emission Database For Global Atmospheric Research Research Articles

Estimating global 0.1° scale gridded anthropogenic CO2 emissions using TROPOMI NO2 and a data-driven method

Satellite remote sensing is a promising approach for monitoring global CO2 emissions. However, existing satellite-based CO2 observations are too coarse to meet the requirements of fine-scale global mapping. We propose a novel data-driven method to estimate global anthropogenic CO2 emissions at a 0.1° scale, which integrates emissions inventories and satellite data while bypassing the inadequate accuracy of CO2 observations. Due to the co-emitted anthropogenic emissions of nitrogen oxides (NOx = NO + NO2) and CO2, high-resolution NO2 measurements from the TROPOspheric Monitoring Instrument (TROPOMI) are employed to map the global anthropogenic emissions at a global 0.1° scale. We construct the driving features from NO2 data and also incorporate gridded CO2/NOx emission ratios and NOx/NO2 conversion ratios as driving data to describe co-emissions. Both ratios are predicted using a long short-term memory (LSTM) neural network (with an R2 of 0.984 for the CO2/NOx emission ratio and an R2 of 0.980 for the NOx/NO2 conversion ratio). The data-driven model for estimating anthropogenic CO2 emissions is implemented by random forest regression (RFR) and trained using the Emissions Database for Global Atmospheric Research (EDGAR). The satellite-based anthropogenic CO2 emission dataset at a global 0.1° scale agrees well with the national CO2 emission inventories (an R2 of 0.998 with Global Carbon Budget (GCB) and an R2 of 0.996 with EDGAR) and consistent with city-level emission estimates from Carbon Monitor Cities (CMC) with the R2 of 0.824. This data-driven method based on satellite-observed NO2 provides a new perspective for fine-resolution anthropogenic CO2 emissions estimation.

Integrated satellite observations unravel the relationship between urbanization and anthropogenic non-methane volatile organic compound emissions globally

As urban areas expand globally, human activities are leading to a sustained increase in non-methane volatile organic compound (NMVOC) emissions, escalating both environmental and health-related concerns. Given their diverse origins, estimating anthropogenic NMVOC emissions levels from global urban areas remains challenging. Here, we integrate TROPOspheric Monitoring Instrument (TROPOMI) formaldehyde (HCHO) column data, Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light (NTL) radiance data, and the Emission Database for Global Atmospheric Research (EDGAR) to develop a method for estimating global anthropogenic NMVOC emissions. Furthermore, we construct a linear model to analyze the relationship between urbanization and anthropogenic NMVOC emissions. Our research reveals that meticulously filtered TROPOMI HCHO columns have a high Pearson correlation coefficient (r = 0.91) with anthropogenic NMVOC emissions, indicating its reliability as an indicator reflecting the levels of anthropogenic NMVOC emissions. We establish linear models at various scales, including global, continental, and national, linking HCHO columns (as indicators of anthropogenic NMVOC emissions) and NTL radiance (as an indicator of urbanization). The global-scale linear model exhibits an r of 0.81, with a slope of 0.42 × 1015 molec. cm−2 nanoWatts−1 cm2 sr and an intercept of 9.26 × 1015 molec. cm−2. This linear model reflects a positive correlation between urbanization and anthropogenic NMVOC emissions, also serving as a tool for estimating the levels of anthropogenic NMVOC emissions in urban areas. This study offers valuable insights for real-time monitoring of extensive anthropogenic NMVOC emissions.

A new method for estimating megacity NOx emissions and lifetimes from satellite observations

Abstract. We present a new method for estimating NOx emissions and effective lifetimes from large cities based on NO2 measurements from the TROPOspheric Monitoring Instrument (TROPOMI) (PAL dataset, May 2018–November 2021). As in previous studies, the estimate is based on the downwind plume evolution for different wind directions separately. The novelty of the presented approach lies in the simultaneous fit of downwind patterns for opposing wind directions, which makes the method far more robust (i.e., less prone to local minima with nonphysically high or low lifetimes) than a single exponential decay fit. In addition, the new method does not require the assumption of a city being a “point source” but also derives the spatial distribution of emissions. The method was successfully applied to 100 cities worldwide on a seasonal scale. Fitted emissions generally agree reasonably with the Emissions Database for Global Atmospheric Research (EDGAR) v6 (R=0.72) and are on average 14 % lower, while estimated uncertainties are still rather large (≈ 30 %–50 %). Lifetimes were found to be rather short (2.44 ± 0.68 h) and show no distinct dependency on season or latitude, which might be a consequence of discarding observations at high solar zenith angles (>65°). The main limitations of this and similar methods are the underlying assumptions of steady state (meaning constant emissions, wind fields and chemical conditions) within about 100 km downwind from a city, which is probably a simplification that is too strong in order to reach higher accuracies.

Estimate Gaps of Montreal Protocol-Regulated Potent Greenhouse Gas HFC-152a Emissions in China Have Been Explained.

1,1-Difluoroethane (HFC-152a) is a hydrofluorocarbon regulated by the Montreal Protocol, and its emissions in China are of concern as China will regulate HFC-152a in 2024. However, no observation-inferred top-down estimates were undertaken after 2017, and substantial gaps existed among previous estimates of China's HFC-152a emissions. Using the atmospheric observations and inverse modeling, this study reveals China's HFC-152a emissions of 9.4 ± 1.7 Gg/yr (gigagrams per year), 10.6 ± 1.8 Gg/yr, and 9.7 ± 1.5 Gg/yr in 2018, 2019, and 2020, respectively. In addition, we display an overall increasing trend during 2011-2020, which is in contrast to the decreasing and steady trend reported by the Emission Database for Global Atmospheric Research (EDGAR) and the Chinese government, respectively. Subsequently, we establish a comprehensive bottom-up emission inventory matching with top-down estimates and thus succeed in explaining the gaps among previous estimates. Furthermore, the contribution of China's emissions to global HFC-152a emission growth increased from 15% during 2001-2010 to >100% during 2011-2020. An emission projection based on our improved inventory shows that the Kigali Amendment (KA) would assist in avoiding 1535.6-4710.6 Gg (251.8-772.5 Tg CO2-eq) HFC-152a emissions during 2024-2100. Our findings indicate relatively accurate China's HFC-152a emissions and provide scientific support for addressing climate change and implementing the KA.

Retrieval anthropogenic CO2 emissions from OCO-2 and comparison with gridded emission inventories

Estimating anthropogenic CO2 emissions from satellite observations contributes to transparency in CO2 emissions reporting. In this study, we proposed a method for calculating CO2 emissions using Orbiting Carbon Observatory-2 (OCO-2) XCO2 (the column-averaged CO2 dry-air mole fraction) observations. We identified local XCO2 plume enhancements on the OCO-2 track and retrieved CO2 emissions through minimizing the difference between the XCO2 enhancement identified from OCO-2 and the XCO2 enhancement simulated by the Gaussian plume model based on emission rates provided by Open-Data Inventory for Anthropogenic Carbon Dioxide (ODIAC). Among 473 cases successfully retrieved from the OCO-2 from September 2014 to June 2023, the average hourly estimated CO2 emissions (OCO-2 emissions) are 6166.52 tCO2/h with the uncertainty of 1604.51 tCO2/h. Comparison with ODIAC, EDGAR (Emissions Database for Global Atmospheric Research) and MEIC (Multi-resolution Emission Inventory for China) emission inventories shows that the OCO-2 emissions are slightly overestimated. Globally, OCO-2 emissions are approximately 1.25 times that of ODIAC and 1.22 times that of EDGAR, while in China, they are 1.39 times that of MEIC. By conducting comprehensive assessments encompassing case studies in California, Riyadh, and Xinjiang, and by comparing the results with relevant investigations, this discrepancy can be attributed to missing statistics on emission inventories, actual emissions varying with the time of day, or complex terrain and other factors. This study effectively quantifies CO2 emissions using OCO-2 satellite data and provides valuable insights for monitoring significant regional CO2 emissions.

Pantropical CO2 emissions and removals for the AFOLU sector in the period 1990–2018

Transparent, accurate, comparable, and complete estimates of greenhouse gas emissions and removals are needed to support mitigation goals and performance assessments under the Paris Agreement. Here, we present a comparative analysis of the agriculture forestry and other land use (AFOLU) emission estimates from different datasets, including National Greenhouse Gas Inventories (NGHGIs), FAOSTAT, the BLUE, OSCAR, and Houghton (here after updated H&N2017) bookkeeping models; Emissions Database for Global Atmospheric Research (EDGAR); and the US Environmental Protection Agency (EPA). We disaggregate the fluxes for the forestry and other land use (FOLU) sector into forest land, deforestation, and other land uses (including non-forest land uses), while agricultural emissions are disaggregated according to the sources (i.e., livestock, croplands, rice cultivation, and agricultural fires). Considering different time periods (1990–1999, 2000–2010, and 2011–2018), we analyse the trend of the fluxes with a key focus on the tropical regions (i.e., Latin America, sub-Saharan Africa, and South and Southeast Asia). Three of the five data sources indicated a decline in the net emissions over the tropics over the period 1990–2018. The net FOLU emissions for the tropics varied with values of 5.47, 5.22, 4.28, 3.21, and 1.17 GtCO2 year−1 (for BLUE, OSCAR, updated H&N2017, FAOSTAT, and NGHGIs, respectively) over the recent period (2011–2018). Gross deforestation emissions over the same period were 5.87, 7.16, 5.48, 3.96, and 3.74 GtCO2 year−1 (for BLUE, OSCAR, updated H&N2017, FAOSTAT, and NGHGIs). The net forestland sink was −1.97, −3.08, −2.09, −0.53, and −3.00 GtCO2 year−1 (for BLUE, OSCAR, updated H&N2017, FAOSTAT, and NGHGIs). Continental analysis indicated that the differences between the data sources are much large in sub-Saharan Africa and South and Southeast Asia than in Latin America. Disagreements in the FOLU emission estimates are mainly explained by differences in the managed land areas and the processes considered (i.e., direct vs indirect effects of land use change, and gross vs net accounting for deforestation). Net agricultural emissions from cropland, livestock, and rice cultivation were more homogenous across the FAOSTAT, EDGAR, and EPA datasets, with all the data sources indicating an increase in the emissions over the tropics. However, there were notable differences in the emission from agricultural fires. This study highlights the importance of investing and improving data sources for key fluxes to achieve a more robust and transparent global stocktake.

A global catalogue of CO2 emissions and co-emitted species from power plants, including high-resolution vertical and temporal profiles

Abstract. We present a high-resolution global emission catalogue of CO2 and co-emitted species (NOx, SO2, CO, CH4) from thermal power plants for the year 2018. The construction of the database follows a bottom-up approach, which combines plant-specific information with national energy consumption statistics and fuel-dependent emission factors for CO2 and emission ratios for co-emitted species (e.g. the amount of NOx emitted relative to CO2: NOx/CO2). The resulting catalogue contains annual emission information for more than 16 000 individual facilities at their exact geographical locations. Each facility is linked to a country- and fuel-dependent temporal profile (i.e. monthly, day of the week and hourly) and a plant-level vertical profile, which were derived from national electricity generation statistics and plume rise calculations that combine stack parameters with meteorological information. The combination of the aforementioned information allows us to derive high-resolution spatial and temporal emissions for modelling purposes. Estimated annual emissions were compared against independent plant- and country-level inventories, including Carbon Monitoring for Action (CARMA), the Global Infrastructure emission Database (GID) and the Emissions Database for Global Atmospheric Research (EDGAR), as well as officially reported emission data. Overall good agreement is observed between datasets when comparing the CO2 emissions. The main discrepancies are related to the non-inclusion of auto-producer or heat-only facilities in certain countries due to a lack of data. Larger inconsistencies are obtained when comparing emissions from co-emitted species due to uncertainties in the fuel-, country- and region-dependent emission ratios and gap-filling procedures. The temporal distribution of emissions obtained in this work was compared against traditional sector-dependent profiles that are widely used in modelling efforts. This highlighted important differences and the need to consider country dependencies when temporally distributing emissions. The resulting catalogue (https://doi.org/10.24380/0a9o-v7xe, Guevara et al., 2023) is developed in the framework of the Prototype System for a Copernicus CO2 service (CoCO2) European Union (EU)-funded project to support the development of the Copernicus CO2 Monitoring and Verification Support capacity (CO2MVS).

Tracing sources of atmospheric methane using clumped isotopes.

We apply a recently developed measurement technique for methane (CH4) isotopologues* (isotopic variants of CH4-13CH4, 12CH3D, 13CH3D, and 12CH2D2) to identify contributions to the atmospheric burden from fossil fuel and microbial sources. The aim of this study is to constrain factors that ultimately control the concentration of this potent greenhouse gas on global, regional, and local levels. While predictions of atmospheric methane isotopologues have been modeled, we present direct measurements that point to a different atmospheric methane composition and to a microbial flux with less clumping (greater deficits relative to stochastic) in both 13CH3D and 12CH2D2 than had been previously assigned. These differences make atmospheric isotopologue data sufficiently sensitive to variations in microbial to fossil fuel fluxes to distinguish between emissions scenarios such as those generated by different versions of EDGAR (the Emissions Database for Global Atmospheric Research), even when existing constraints on the atmospheric CH4 concentration profile as well as traditional isotopes are kept constant.

A simplified non-linear chemistry transport model for analyzing NO2 column observations: STILT–NOx

Abstract. Satellites monitoring air pollutants (e.g., nitrogen oxides; NOx = NO + NO2) or greenhouse gases (GHGs) are widely utilized to understand the spatiotemporal variability in and evolution of emission characteristics, chemical transformations, and atmospheric transport over anthropogenic hotspots. Recently, the joint use of space-based long-lived GHGs (e.g., carbon dioxide; CO2) and short-lived pollutants has made it possible to improve our understanding of emission characteristics. Some previous studies, however, lack consideration of the non-linear NOx chemistry or complex atmospheric transport. Considering the increase in satellite data volume and the demand for emission monitoring at higher spatiotemporal scales, it is crucial to construct a local-scale emission optimization system that can handle both long-lived GHGs and short-lived pollutants in a coupled and effective manner. This need motivates us to develop a Lagrangian chemical transport model that accounts for NOx chemistry and fine-scale atmospheric transport (STILT–NOx) and to investigate how physical and chemical processes, anthropogenic emissions, and background may affect the interpretation of tropospheric NO2 columns (tNO2). Interpreting emission signals from tNO2 commonly involves either an efficient statistical model or a sophisticated chemical transport model. To balance computational expenses and chemical complexity, we describe a simplified representation of the NOx chemistry that bypasses an explicit solution of individual chemical reactions while preserving the essential non-linearity that links NOx emissions to its concentrations. This NOx chemical parameterization is then incorporated into an existing Lagrangian modeling framework that is widely applied in the GHG community. We further quantify uncertainties associated with the wind field and chemical parameterization and evaluate modeled columns against retrieved columns from the TROPOspheric Monitoring Instrument (TROPOMI v2.1). Specifically, simulations with alternative model configurations of emissions, meteorology, chemistry, and inter-parcel mixing are carried out over three United States (US) power plants and two urban areas across seasons. Using the U.S. Environmental Protection Agency (EPA)-reported emissions for power plants with non-linear NOx chemistry improves the model–data alignment in tNO2 (a high bias of ≤ 10 % on an annual basis), compared to simulations using either the Emissions Database for Global Atmospheric Research (EDGAR) model or without chemistry (bias approaching 100 %). The largest model–data mismatches are associated with substantial biases in wind directions or conditions of slower atmospheric mixing and photochemistry. More importantly, our model development illustrates (1) how NOx chemistry affects the relationship between NOx and CO2 in terms of the spatial and seasonal variability and (2) how assimilating tNO2 can quantify systematic biases in modeled wind directions and emission distribution in prior inventories of NOx and CO2, which laid a foundation for a local-scale multi-tracer emission optimization system.

Quantification of carbon monoxide emissions from African cities using TROPOMI

Abstract. Carbon monoxide (CO) is an air pollutant that plays an important role in atmospheric chemistry and is mostly emitted by forest fires and incomplete combustion in, for example, road transport, residential heating, and industry. As CO is co-emitted with fossil fuel CO2 combustion emissions, it can be used as a proxy for CO2. Following the Paris Agreement, there is a need for independent verification of reported activity-based bottom-up CO2 emissions through atmospheric measurements. CO can be observed daily at a global scale with the TROPOspheric Monitoring Instrument (TROPOMI) satellite instrument with daily global coverage at a resolution down to 5.5 × 7 km2. To take advantage of this unique TROPOMI dataset, we develop a cross-sectional flux-based emission quantification method that can be applied to quantify emissions from a large number of cities, without relying on computationally expensive inversions. We focus on Africa as a region with quickly growing cities and large uncertainties in current emission estimates. We use a full year of high-resolution Weather Research and Forecasting (WRF) simulations over three cities to evaluate and optimize the performance of our cross-sectional flux emission quantification method and show its reliability down to emission rates of 0.1 Tg CO yr−1. Comparison of the TROPOMI-based emission estimates to the Dynamics–Aerosol–Chemistry–Cloud Interactions in West Africa (DACCIWA) and Emissions Database for Global Atmospheric Research (EDGAR) bottom-up inventories shows that CO emission rates in northern Africa are underestimated in EDGAR, suggesting overestimated combustion efficiencies. We see the opposite when comparing TROPOMI to the DACCIWA inventory in South Africa and Côte d'Ivoire, where CO emission factors appear to be overestimated. Over Lagos and Kano (Nigeria) we find that potential errors in the spatial disaggregation of national emissions cause errors in DACCIWA and EDGAR respectively. Finally, we show that our computationally efficient quantification method combined with the daily TROPOMI observations can identify a weekend effect in the road-transport-dominated CO emissions from Cairo and Algiers.

Effect of Carbon Dioxide, Methane and Nitrous Oxide with TCO and Solar Flux on the Earth’s Stratosphere Temperature in Mid-Latitude Region for Period 2000-2018

Abstract The stratosphere is the second layer of the atmosphere and the important of this layer is to contain the ozone layer which absorbs the UV radiation from the Sun. The main objective of this research is to study the effect of GHGs and TCO on the temperature of stratosphere layer in the presence of influence of solar radiation in mid-latitude region, specifically capital Beijing (latitude 40°N; longitude 116°E) in China are taken as a study case, because it is the most city in the world that emits greenhouse gases for the period (2000-2018) selected in this research. The data for GHGs (carbon dioxide, methane and nitrous oxide) are taken from Emissions Database for Global Atmospheric Research (EDGAR), the monthly average data for the total column ozone and temperature are taken from the European Centre for Medium Range Weather Forecasts (ECMWF). While the data for the solar radiation is taken from the NASA Goddard Institute for Space Studies. The data for Sunspot number is taken from Sunspot Index and Long-term Solar Observations (SILSO), all these data are taken for the period (2000-2018) above the capital Beijing. From data analysis and results it found that there is a cooling effect on the stratosphere layer due to increase in greenhouse gases and decrease in the total column ozone, as well as the decreasing in solar radiation leads to this cooling.

The HTAP_v3 emission mosaic: merging regional and global monthly emissions (2000–2018) to support air quality modelling and policies

Abstract. This study, performed under the umbrella of the Task Force on Hemispheric Transport of Air Pollution (TF-HTAP), responds to the global and regional atmospheric modelling community's need of a mosaic emission inventory of air pollutants that conforms to specific requirements: global coverage, long time series, spatially distributed emissions with high time resolution, and a high sectoral resolution. The mosaic approach of integrating official regional emission inventories based on locally reported data, with a global inventory based on a globally consistent methodology, allows modellers to perform simulations of high scientific quality while also ensuring that the results remain relevant to policymakers. HTAP_v3, an ad hoc global mosaic of anthropogenic inventories, has been developed by integrating official inventories over specific areas (North America, Europe, Asia including Japan and South Korea) with the independent Emissions Database for Global Atmospheric Research (EDGAR) inventory for the remaining world regions. The results are spatially and temporally distributed emissions of SO2, NOx, CO, non-methane volatile organic compounds (NMVOCs), NH3, PM10, PM2.5, black carbon (BC), and organic carbon (OC), with a spatial resolution of 0.1∘ × 0.1∘ and time intervals of months and years, covering the period 2000–2018 (https://doi.org/10.5281/zenodo.7516361, Crippa, 2023, https://edgar.jrc.ec.europa.eu/dataset_htap_v3, last access: June 2023). The emissions are further disaggregated into 16 anthropogenic emitting sectors. This paper describes the methodology applied to develop such an emission mosaic, reports on source allocation, differences among existing inventories, and best practices for the mosaic compilation. One of the key strengths of the HTAP_v3 emission mosaic is its temporal coverage, enabling the analysis of emission trends over the past 2 decades. The development of a global emission mosaic over such long time series represents a unique product for global air quality modelling and for better-informed policymaking, reflecting the community effort expended by the TF-HTAP to disentangle the complexity of transboundary transport of air pollution.

Investigating high methane emissions from urban areas detected by TROPOMI and their association with untreated wastewater

Even though methane concentrations have contributed an estimated 23% of climate forcing, part of the recent increases in the global methane background concentrations remain unexplained. Satellite remote sensing has been used extensively to constrain emission inventories, for example with the TROPOspheric Monitoring Instrument which has been measuring methane since November 2017. We have identified enhancements of methane over 61 urban areas around the world and estimate their emissions using a two-dimensional Gaussian model. We show that methane emissions from urban areas may be underestimated by a factor of 3–4 in the Emissions Database for Global Atmospheric Research (EDGAR) greenhouse gas emission inventory. Scaling our results to the 385 urban areas with more than 2 million inhabitants suggests that they could account for up to 22% of global methane emissions. The emission estimates of the 61 urban areas do not correlate with the total or sectoral EDGAR emission inventory. They do however correlate with estimated rates of untreated wastewater, varying from 33 kg person−1 year−1 for cities with zero untreated wastewater to 138 kg person−1 year−1 for the cities with the most untreated wastewater. If this relationship were confirmed by higher resolution remote sensing or in situ monitoring, we estimate that reducing discharges of untreated wastewater could reduce global methane emissions by up to 5%–10% while at the same time yielding significant ecological and human co-benefits.

CO2 emissions from C40 cities: citywide emission inventories and comparisons with global gridded emission datasets

Under the leadership of the C40 Cities Climate Leadership Group (C40), approximately 1100 global cities have signed to reach net-zero emissions by 2050. Accurate greenhouse gas emission calculations at the city-scale have become critical. This study forms a bridge between the two emission calculation methods: (a) the city-scale accounting used by C40 cities—the Global Protocol for Community-Scale Greenhouse Gas Emission Inventories (GPC) and (b) the global-scale gridded datasets used by the research community—the Emission Database for Global Atmospheric Research (EDGAR) and Open‐Source Data Inventory for Anthropogenic CO2 (ODIAC). For the emission magnitudes of 78 C40 cities, we find good correlations between the GPC and EDGAR (R 2 = 0.80) and the GPC and ODIAC (R 2 = 0.72). Regionally, African cities show the largest variability in the three emission estimates. For the emission trends, the standard deviation of the differences is ±4.7% yr−1 for EDGAR vs. GPC and is ±3.9% yr−1 for ODIAC vs. GPC: a factor of ∼2 larger than the trends that many C40 cities pledged (net-zero by 2050 from 2010, or −2.5% yr−1). To examine the source of discrepancies in the emission datasets, we assess the impact of spatial resolutions of EDGAR (0.1°) and ODIAC (1 km) on estimating varying-sized cities’ emissions. Our analysis shows that the coarser resolution of EDGAR can artificially decrease emissions by 13% for cities smaller than 1000 km2. We find that data quality of emission factors (EFs) used in GPC inventories vary regionally: the highest quality for European and North American and the lowest for African and Latin American cities. Our study indicates that the following items should be prioritized to reduce the discrepancies between the two emission calculation methods: (a) implementing local-specific/up-to-date EFs in GPC inventories, (b) keeping the global power plant database current, and (c) incorporating satellite-derived CO2 datasets (i.e. NASA OCO-3).

Socio-economic inclusion and sustainable economic growth: Empirical analysis of Nigeria and South Africa

In Africa, socio-economic inclusion is often predicated on the degree of access to finance that individuals have to consummate economic activities. This study is centered on this interlink with a focus on Nigeria and South Africa. From the 17 years of data (2004–2020) sourced from the Emissions Database for Global Atmospheric Research (EDGAR), International Monetary Fund (IMF) financial statistics, and the World Bank Development Indicator, the study attempted to empirically validate the existence or absence of a long-run significance of the variables used. Result confirmed that proximity to bank branches and access to credit by the private sector are vital ingredients of sustainable economic growth in both countries, while automated teller machine does not. Thus, this concluding evidence provides an avenue for expansionary policy drive for concerned authorities.

Declining, seasonal-varying emissions of sulfur hexafluoride from the United States

Abstract. Sulfur hexafluoride (SF6) is the most potent greenhouse gas (GHG), and its atmospheric abundance, albeit small, has been increasing rapidly. Although SF6 is used to assess atmospheric transport modeling and its emissions influence the climate for millennia, SF6 emission magnitudes and distributions have substantial uncertainties. In this study, we used NOAA's ground-based and airborne measurements of SF6 to estimate SF6 emissions from the United States between 2007 and 2018. Our results suggest a substantial decline of US SF6 emissions, a trend also reported in the US Environmental Protection Agency's (EPA) national inventory submitted under the United Nations Framework Convention on Climate Change (UNFCCC), implying that US mitigation efforts have had some success. However, the magnitudes of annual emissions derived from atmospheric observations are 40 %–250 % higher than the EPA's national inventory and substantially lower than the Emissions Database for Global Atmospheric Research (EDGAR) inventory. The regional discrepancies between the atmosphere-based estimate and EPA's inventory suggest that emissions from electric power transmission and distribution (ETD) facilities and an SF6 production plant that did not or does not report to the EPA may be underestimated in the national inventory. Furthermore, the atmosphere-based estimates show higher emissions of SF6 in winter than in summer. These enhanced wintertime emissions may result from increased maintenance of ETD equipment in southern states and increased leakage through aging brittle seals in ETD in northern states during winter. The results of this study demonstrate the success of past US SF6 emission mitigations and suggest that substantial additional emission reductions might be achieved through efforts to minimize emissions during servicing or through improving sealing materials in ETD.

Analysis of Worldwide Greenhouse and Carbon Monoxide Gas Emissions: Which Countries Exhibit a Special Pattern? A Closer Look via Twitter.

There is significant global concern about the harmful effects of greenhouse gas and carbon monoxide emissions (deforestation, air pollution, global warming, etc.). The 2015 Paris Agreement on climate change aspires to reduce global warming by achieving a climate-neutral world. Research has been carried out to calculate and diminish the aforementioned emissions in waste, power industry, transport, building, in addition to other areas. The aim of this paper is to analyse the carbon and greenhouse gas emissions across countries around the globe in order to find patterns and correlate them to socio-economic indicators [gross national income (GNI), industrial production (IPI) and human development indexes (HDI)] as well as Twitter interactions regarding climate change. For this purpose, time series and socio-economic data have been downloaded from different repositories including EDGAR (Emissions Database for Global Atmospheric Research), World Bank and UNDP (United Nations Development Programme). Although classical clustering algorithms have already been used in the examination of some environmental issues, we use a non-parametric time series clustering method, which has been suggested in certain scientific literature as a more flexible approach, since any ad hoc parametric assumptions are required. The chosen socio-economic indicators have also demonstrated their relevance in pieces of research related to various fields. With respect to Twitter, which is one of the most popular social networks nowadays, significant analysis has also been performed on the basis of capturing citizens' perceptions on a multitude of matters. We found that several countries such as Brazil, India, China, Nigeria, Russia, United States, Spain, Andorra, Greece, and Qatar show differences in carbon and greenhouse gas emissions patterns. Besides, there does not seem to be a correlation between GNI, IPI and HDI as well as the above mentioned emissions Regarding Twitter interactions, a dissimilarity in the distribution of hashtags was detected between the aforementioned countries and the rest of the world. This research can help to identify countries in which more governmental measures are needed to reduce the type of emissions analysed in certain industrial sectors. In addition, it points out the topics related to climate change that seem to generate the most debate on Twitter for countries with an unusual pattern. The online version contains supplementary material available at 10.1007/s41742-023-00510-4.

Assessment of livestock greenhouse gases in Colombia between 1995 and 2015

The livestock sector in Colombia significantly participates in national economic dynamics but makes significant worldwide contributions to greenhouse gas emissions. Hence, climate change mitigation in this sector is essential. This study aims to assess the greenhouse gas in the livestock sector. The results are reported in methane emissions (CH4) and nitrous oxide (N2O) from enteric fermentation, and N2O by manure management based on the information from the Emission Database for Global Atmospheric Research (EDGAR), in all cases expressed as dioxide of carbon (CO2eq). The emissions obtained from the EDGAR database for 2015 were proportional to the values of the National Inventory of Greenhouse Gases published by the Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM) in 2016. Colombia is the 12th on global, 4th in America and 2nd in South America position by livestock GHG emission, and is the dominant source in all economic sector. The results showed higher records for CH4 emissions during the years 2010 and 2015, while the N2O emissions were higher during 2015. The regions with the highest emissions of CH4 and N2O corresponded to the northwestern area of Colombia. The Spearman correlation test showed a positive correlation between the CH4 emissions, and the age groups studied. The post hoc analysis of the Kruskal–Wallis test showed a more significant influence on CH4 emissions.

Ethylene industrial emitters seen from space

Volatile organic compounds are emitted abundantly from a variety of natural and anthropogenic sources. However, in excess, they can severely degrade air quality. Their fluxes are currently poorly represented in inventories due to a lack of constraints from global measurements. Here, we track from space over 300 worldwide hotspots of ethylene, the most abundant industrially produced organic compound. We identify specific emitters associated with petrochemical clusters, steel plants, coal-related industries, and megacities. Satellite-derived fluxes reveal that the ethylene emissions of the industrial sources are underestimated or missing in the state-of-the-art Emission Database for Global Atmospheric Research (EDGAR) inventory. This work exposes global emission point-sources of a short-lived carbonated gas, complementing the ongoing large-scale efforts on the monitoring of inorganic pollutants.

Exports Widen the Regional Inequality of Health Burdens and Economic Benefits in India

Both the ever-complex international and subnational supply chains could relocate health burdens and economic benefits across India, leading to the widening of regional inequality. Here, we simultaneously track the unequal distribution of fine particle matter (PM2.5) pollution, health costs, and value-added embodied in inter- and intranational exports for Indian states in 2015 by integrating a nested multiregional input-output (MRIO) table constructed based on EXIOBASE and an Indian regional MRIO table, Emissions Database for Global Atmospheric Research (EDGAR), the Community Multi-Scale Air Quality (CMAQ) model, and a concentration-response function. The results showed that the annual premature deaths associated with PM2.5 pollution embodied in inter- and intranational exports were 757,356 and 388,003 throughout India, accounting for 39% and 20% of the total premature deaths caused by PM2.5 pollution, respectively. Richer south and west coastal states received around half of the national Gross Domestic Product (GDP) induced by exports with a quarter of the health burden, while poorer central and east states bear approximately 60% of the health burden with less than a quarter of national GDP. Our findings highlight the role of exports in driving the regional inequality of health burdens and economic benefits. Therefore, tailored strategies (e.g., air pollution compensation, advanced technology transfer, and export structure optimization) could be formulated.