A high-definition spatially explicit modelling approach for national greenhouse gas emissions from industrial processes: reducing the errors and uncertainties in global emission modelling

The domestic and industrial gaseous emissions are estimated in order to complete the high resolution inventory of biogenic and pyrogenic emissions performed over the Cote d’Ivoire (Ivory Coast). In this study, domestic emissions are estimated from the regional population density including rural or urban character, the amount of fuel (wood and charcoal) used per cape per year and emission factors. The population density and the amount of fuel are two key parameters that make up the regional emissivity (0.0081 to 0.0685 kg/m2 per year), which discriminates the nineteen regions of Cote d’Ivoire into seven groups. Domestic emissions range from 0.016 to 0.1 g/m2 per year for NOx, 3.9 to 33 g/m2 per year for CO and 35.2 to 297 g/m2 per year for CO2. Regions with high domestic emissions are located in forest areas and are both producers and consumers of charcoal. Cote d’Ivoire emits 1.6 × 107 kg per year of NOx domestic, 3.8 × 109 kg of CO and nine times more CO2. All these results are consistent with the Emission Database for Global Atmospheric Research (EDGAR). Industrial emissions are not calculated but taken from the EDGAR database to produce a distribution map. These emissions come from the release of major industries (Abidjan, Bouake and San Pedro) and the manufacture of charcoal. The maximum of industrial emissions is around Abidjan with 107 kg per year for CO and 106 kg per year for NOx. Key words: Cote d’Ivoire, West Africa, domestic, industrial emissions.

The quantification of fossil-fuel-related emissions of carbon dioxide to the atmosphere is necessary in order to accurately represent carbon cycle fluxes and to understand and project the details of the global carbon cycle. In addition, the monitoring, reporting, and verification (MRV) of carbon dioxide emissions is necessary for the success of international agreements to reduce emissions. However, existing fossil-fuel carbon dioxide (FFCO2) emissions inventories vary in terms of the data and methods used to estimate and distribute FFCO2. This paper compares how the approaches used to create spatially explicit FFCO2 emissions inventories affect the spatial distribution of emissions estimates and the magnitude of emissions estimates in specific locales. Five spatially explicit FFCO2 emission inventories were compared: Carbon Dioxide Information and Analysis Center (CDIAC), Emission Database for Global Atmospheric Research (EDGAR), Fossil Fuel Data Assimilation System (FFDAS), Open-source Data Inventory for Anthropogenic CO2 (ODIAC), and Vulcan. The effects of using specific data and approaches in the creation of spatially explicit FFCO2 emissions inventories, and the effect of resolution on data representation are analyzed using graphical, numerical, and cartographic approaches. We examined the effect of using top-down versus bottom-up approaches, nightlights versus population proxies, and the inclusion of large point sources. The results indicate that the approach used to distribute emissions in space creates distinct patterns in the distribution of emissions estimates and hence in the estimates of emissions in specific locations. The different datasets serve different purposes but collectively show the key role of large point sources and urban centers and the strong relationship between scale and uncertainty.

Abstract. An improved divergence method has been developed to estimate annual methane (CH4) emissions from TROPOspheric Monitoring Instrument (TROPOMI) observations. It has been applied to the period of 2018 to 2021 over the Middle East, where the orography is complicated, and the mean mixing ratio of methane (XCH4) might be affected by albedos or aerosols over some locations. To adapt to extreme changes of terrain over mountains or coasts, winds are used with their divergent part removed. A temporal filter is introduced to identify highly variable emissions and to further exclude fake sources caused by retrieval artifacts. We compare our results to widely used bottom-up anthropogenic emission inventories: Emissions Database for Global Atmospheric Research (EDGAR), Community Emissions Data System (CEDS), and Global Fuel Exploitation Inventory (GFEI) over several regions representing various types of sources. The NOx emissions are from EDGAR and Daily Emissions Constrained by Satellite Observations (DECSO), and the industrial heat sources identified by Visible Infrared Imaging Radiometer Suite (VIIRS) are further used to better understand our resulting methane emissions. Our results indicate possibly large underestimations of methane emissions in metropolises like Tehran (up to 50 %) and Isfahan (up to 70 %) in Iran. The derived annual methane emissions from oil/gas production near the Caspian Sea in Turkmenistan are comparable to GFEI but more than 2 times higher than EDGAR and CEDS in 2019. Large discrepancies in the distribution of methane sources in Riyadh and its surrounding areas are found between EDGAR, CEDS, GFEI, and our emissions. The methane emission from oil/gas production to the east of Riyadh seems to be largely overestimated by EDGAR and CEDS, while our estimates as well as GFEI and DECSO NOx indicate much lower emissions from industrial activities. On the other hand, regions like Iran, Iraq, and Oman are dominated by sources from oil and gas exploitation that probably include more irregular releases of methane, with the result that our estimates, which include only invariable sources, are lower than the bottom-up emission inventories.

Abstract. Multiannual satellite measurements of tropospheric NO2 columns are used for evaluation of CO2 emission changes in China in the period from 1996 to 2008. Indirect top-down annual estimates of CO2 emissions are derived from the satellite NO2 column measurements by means of a simple inverse modeling procedure involving simulations performed with the CHIMERE mesoscale chemistry–transport model and the CO2-to-NOx emission ratios from the Emission Database for Global Atmospheric Research (EDGAR) global anthropogenic emission inventory and Regional Emission Inventory in Asia (REAS). Exponential trends in the normalized time series of annual emissions are evaluated separately for the periods from 1996 to 2001 and from 2001 to 2008. The results indicate that the both periods manifest strong positive trends in the CO2 emissions, and that the trend in the second period was significantly larger than the trend in the first period. Specifically, the trends in the first and second periods are best estimated to be in the range from 3.7 to 8.3 and from 11.0 to 13.2% per year, respectively, taking into account statistical uncertainties and differences between the CO2-to-NOx emission ratios from the EDGAR and REAS inventories. Comparison of our indirect top-down estimates of the CO2 emission changes with the corresponding bottom-up estimates provided by the EDGAR (version 4.2) and Global Carbon Project (GCP) glomal emission inventories reveals that while acceleration of the CO2 emission growth in the considered period is a common feature of both kinds of estimates, nonlinearity in the CO2 emission changes may be strongly exaggerated in the global emission inventories. Specifically, the atmospheric NO2 observations do not confirm the existence of a sharp bend in the emission inventory data time series in the period from 2000 to 2002. A significant quantitative difference is revealed between the bottom-up and indirect top-down estimates of the CO2 emission trend in the period from 1996 to 2001 (specifically, the trend was not positive according to the global emission inventories, but is strongly positive in our estimates). These results confirm the findings of earlier studies that indicated probable large uncertainties in the energy production and other activity data for China from international energy statistics used as the input information in the global emission inventories. For the period from 2001 to 2008, some quantitative differences between the different kinds of estimates are found to be in the range of possible systematic uncertainties associated with our estimation method. In general, satellite measurements of tropospheric NO2 are shown to be a useful source of information on CO2 sources collocated with sources of nitrogen oxides; the corresponding potential of these measurements should be exploited further in future studies.

Emission inventories are one of the most critical inputs for the successful modeling of air quality. The performance of the modeling results is directly affected by the quality of atmospheric emission inventories. Consequently, the development of representative inventories is always required. Due to the lack of regional inventories in Brazil, this study aimed to investigate the use of the particulate matter (PM) emission estimation from the Brazilian top-down vehicle emission inventory (VEI) of 2012 for air quality modeling. Here, we focus on road vehicles since they are usually responsible for significant emissions of PM in urban areas. The total Brazilian emission of PM (63,000tyear-1) from vehicular sources was distributed into the urban areas of 5557 municipalities, with 1-km2 grid spacing, considering two approaches: (i) population and (ii) fleet of each city. A comparison with some local inventories is discussed. The inventory was compiled in the PREP-CHEM-SRC processor tool. One-month modeling (August 2015) was performed with WRF-Chem for the four metropolitan areas of Brazilian Southeast: Belo Horizonte (MABH), Great Vitória (MAGV), Rio de Janeiro (MARJ), and São Paulo (MASP). In addition, modeling with the Emission Database for Global Atmospheric Research (EDGAR) inventory was carried out to compare the results. Overall, EDGAR inventory obtained higher PM emissions than the VEI segregated by population and fleet, which is expected owing to considerations of additional sources of emission (e.g., industrial and residential). This higher emission of EDGAR resulted in higher PM10 and PM2.5 concentrations, overestimating the observations in MASP, while the proposed inventory well represented the ambient concentrations, obtaining better statistics indices. For the other three metropolitan areas, both EDGAR and the VEI inventories obtained consistent results. Therefore, the present work endorses the fact that vehicles are responsible for the more substantial contribution to PM emissions in the studied urban areas. Furthermore, the use of VEI can be representative for modeling air quality in the future.

Abstract. The importance of emission inventory uncertainty on the simulation of summertime tropospheric ozone over China has been analyzed using a regional chemical transport model. Three independent emissions inventories, that are (i) emission estimates from the Emission Database for Global Atmospheric Research (EDGAR) for the year 1995, (ii) a regional emission inventory used in the Transport and Chemical Evolution over the Pacific (TRACE-P) program with emissions for the year 2000 and (iii) a national emission inventory used in the China Ozone Research Program (CORP) with emission estimates for the year 1995, are used for model simulation over a summer period. Methods used for the development of the inventories are discussed and differences in simulated ozone and its precursors with these emission inventories are analyzed. Comparison of the emission inventories revealed large differences in the emission estimates (up to 50% for NOx, ~100% for NMVOC and ~1000% for CO). Application of the different emission inventories in three model simulations showed minor differences in both surface O3 in rather unpolluted areas in China and at higher altitudes (500mbar). In polluted areas, differences in surface O3 are 30-50% between the different model simulations which seem rather small taking into account the large differences in the emission inventories. Additional sensitivity runs showed that the difference in NOx emissions as well NMVOC emissions is a dominant factor which controls the differences in simulated O3 concentrations while the impact of differences in CO emissions is relatively small. Although the CO emission estimate by CORP seems to be underestimated, there is no confidence to highlight one emission inventory better than the others.

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).

Abstract. Top-down greenhouse gas measurements can be used to independently assess the accuracy of bottom-up emission estimates. We report atmospheric methane (CH4) mole fractions and δ13CH4 measurements from Imperial College London from early 2018 onwards using a Picarro G2201-i analyser. Measurements from March 2018 to October 2020 were compared to simulations of CH4 mole fractions and δ13CH4 produced using the NAME (Numerical Atmospheric-dispersion Modelling Environment) dispersion model coupled with the UK National Atmospheric Emissions Inventory, UK NAEI, and a global inventory, the Emissions Database for Global Atmospheric Research (EDGAR), with model spatial resolutions of ∼ 2, ∼ 10, and ∼ 25 km. Simulation–measurement comparisons are used to evaluate London emissions and the source apportionment in the global (EDGAR) and UK national (NAEI) emission inventories. Observed mole fractions were underestimated by 30 %–35 % in the NAEI simulations. In contrast, a good correspondence between observations and EDGAR simulations was seen. There was no correlation between the measured and simulated δ13CH4 values for either NAEI or EDGAR, however, suggesting the inventories' sectoral attributions are incorrect. On average, natural gas sources accounted for 20 %–28 % of the above background CH4 in the NAEI simulations and only 6 %–9 % in the EDGAR simulations. In contrast, nearly 84 % of isotopic source values calculated by Keeling plot analysis (using measurement data from the afternoon) of individual pollution events were higher than −45 ‰, suggesting the primary CH4 sources in London are actually natural gas leaks. The simulation–observation comparison of CH4 mole fractions suggests that total emissions in London are much higher than the NAEI estimate (0.04 Tg CH4 yr−1) but close to, or slightly lower than, the EDGAR estimate (0.10 Tg CH4 yr−1). However, the simulation–observation comparison of δ13CH4 and the Keeling plot results indicate that emissions due to natural gas leaks in London are being underestimated in both the UK NAEI and EDGAR.

<strong class="journal-contentHeaderColor">Abstract.</strong> Top-down greenhouse gas measurements can be used to independently assess the accuracy of bottom-up emission estimates. We report atmospheric methane (CH<span class="inline-formula">₄</span>) mole fractions and <span class="inline-formula"><i>δ</i>¹³</span>CH<span class="inline-formula">₄</span> measurements from Imperial College London from early 2018 onwards using a Picarro G2201-i analyser. Measurements from March 2018 to October 2020 were compared to simulations of CH<span class="inline-formula">₄</span> mole fractions and <span class="inline-formula"><i>δ</i>¹³</span>CH<span class="inline-formula">₄</span> produced using the NAME (Numerical Atmospheric-dispersion Modelling Environment) dispersion model coupled with the UK National Atmospheric Emissions Inventory, UK NAEI, and a global inventory, the Emissions Database for Global Atmospheric Research (EDGAR), with model spatial resolutions of <span class="inline-formula">∼</span> 2, <span class="inline-formula">∼</span> 10, and <span class="inline-formula">∼</span> 25 km. Simulation–measurement comparisons are used to evaluate London emissions and the source apportionment in the global (EDGAR) and UK national (NAEI) emission inventories. Observed mole fractions were underestimated by 30 %–35 % in the NAEI simulations. In contrast, a good correspondence between observations and EDGAR simulations was seen. There was no correlation between the measured and simulated <span class="inline-formula"><i>δ</i>¹³</span>CH<span class="inline-formula">₄</span> values for either NAEI or EDGAR, however, suggesting the inventories' sectoral attributions are incorrect. On average, natural gas sources accounted for 20 %–28 % of the above background CH<span class="inline-formula">₄</span> in the NAEI simulations and only 6 %–9 % in the EDGAR simulations. In contrast, nearly 84 % of isotopic source values calculated by Keeling plot analysis (using measurement data from the afternoon) of individual pollution events were higher than <span class="inline-formula">−</span>45 ‰, suggesting the primary CH<span class="inline-formula">₄</span> sources in London are actually natural gas leaks. The simulation–observation comparison of CH<span class="inline-formula">₄</span> mole fractions suggests that total emissions in London are much higher than the NAEI estimate (0.04 Tg CH<span class="inline-formula">₄</span> yr<span class="inline-formula">^−1</span>) but close to, or slightly lower than, the EDGAR estimate (0.10 Tg CH<span class="inline-formula">₄</span> yr<span class="inline-formula">^−1</span>). However, the simulation–observation comparison of <span class="inline-formula"><i>δ</i>¹³</span>CH<span class="inline-formula">₄</span> and the Keeling plot results indicate that emissions due to natural gas leaks in London are being underestimated in both the UK NAEI and EDGAR.

This study establishes a regional inverse framework to refine methane (CH4) emission inventories for Melbourne, Australia. Methane is a long-lived greenhouse gas and the second most significant contributor to radiative forcing from greenhouse gases after carbon dioxide. Improved understanding of methane emissions from different sectors in Australia is necessary to focus and prioritise mitigation efforts and to track progress towards emissions reduction; however, methane emissions are uncertain, especially at fine resolution (urban and regional scales) needed for mitigation. Moreover, improving predictions of atmospheric methane mole fractions requires precise and accurate emission estimates; However, previous studies indicate a mismatch between current emission estimates and atmospheric observations.Here, we use a combination of surface atmospheric measurements of methane and an inversion approach based on Bayes&amp;#8217; theorem to improve urban-scale methane emission estimates for Melbourne, Australia. Our inversion system is a Python-based four-dimensional variational (Py4DVar) data assimilation system. Due to lack of local methane inventories, prior emission estimates for Melbourne are compiled from globally-accessible datasets, including (1) anthropogenic emissions from the Emissions Database for Global Atmospheric Research (EDGAR), (2) fire emissions from the Global Fire Assimilation System (GFAS) dataset and (3) biogenic emissions from the Model of Emissions of Gases and Aerosols from Nature (MEGAN). Boundary condition adjustments are made using Kennaook/Cape Grim continuous in-situ CH4 mole fraction measurements and the Whole Atmosphere Community Climate Model (WACCM) dataset. The boundary condition adjustments are necessary to develop the efficiency of the regional inversion. The main goal of our inversion system is to provide more precise estimates of regional methane emissions. Independent satellite measurement comparisons are used to assess the system.The comparison with assimilated data shows improvements in modelling methane mole fraction at the suburban Aspendale site with a bias reduction from ~70 ppb (prior) to ~3 ppb (posterior). Our detailed investigations indicate that although the prior results in a reasonable match of modelled mole fraction with observations, the EDGAR dataset does not provide a realistic spatial pattern for the main anthropogenic sources (enteric fermentation and landfills) around Melbourne. The possibility of improving the spatial distribution of the prior emissions has been tested using available local/global datasets, including national maps of livestock and landfills. Eventually, to obtain more comprehensive improved emission inventories in Melbourne, more CH4 mole fraction observational data are required in this area. The results of this study are being used to expand the methane monitoring network for Melbourne.

Abstract. The Emissions Database for Global Atmospheric Research (EDGAR) estimates the human-induced emission rates on Earth. EDGAR collaborates with atmospheric modelling activities and aids policy in the design of mitigation strategies and in evaluating their effectiveness. In these applications, the uncertainty estimate is an essential component, as it quantifies the accuracy and qualifies the level of confidence in the emission. This study complements the EDGAR emissions inventory by providing an estimation of the structural uncertainty stemming from its base components (activity data, AD, statistics and emission factors, EFs) by (i) associating uncertainty to each AD and EF characterizing the emissions of the three main greenhouse gases (GHGs), namely carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O); (ii) combining them; and (iii) making assumptions regarding the cross-country uncertainty aggregation of source categories. It was deemed a natural choice to obtain the uncertainties in EFs and AD statistics from the Intergovernmental Panel on Climate Change (IPCC) guidelines issued in 2006 (with a few exceptions), as the EF and AD sources and methodological aspects used by EDGAR have been built over the years based on the IPCC recommendations, which assured consistency in time and comparability across countries. On the one hand, the homogeneity of the method is one of the key strengths of EDGAR, on the other hand, it facilitates the propagation of uncertainties when similar emission sources are aggregated. For this reason, this study aims primarily at addressing the aggregation of uncertainties' sectorial emissions across GHGs and countries. Globally, we find that the anthropogenic emissions covered by EDGAR for the combined three main GHGs for the year 2015 are accurate within an interval of −15 % to +20 % (defining the 95 % confidence of a log-normal distribution). The most uncertain emissions are those related to N2O from waste and agriculture, while CO2 emissions, although responsible for 74 % of the total GHG emissions, account for approximately 11 % of global uncertainty share. The sensitivity to methodological choices is also discussed.

Abstract. Tracking greenhouse gas (GHG) emissions is essential for understanding the drivers of climate change and guiding global mitigation strategies. The Emissions Database for Global Atmospheric Research (EDGAR) and submissions by Parties to the United Nations Framework Convention on Climate Change (UNFCCC) are two key sources of GHG emissions data. While EDGAR provides comprehensive and globally consistent estimates, UNFCCC submissions are based on nationally reported inventories, which adhere to specific guidelines and reflect country-specific circumstances and practices. This study presents a detailed comparison between EDGAR and UNFCCC GHG emissions inventories, focusing on G20 countries, which account for nearly 80 % of global emissions, as well as Annex I countries, including the EU27. By examining sectoral discrepancies, methodological variations, and the impact of reporting timelines, the paper identifies key areas of alignment and divergence in emissions estimates. While CO2 emissions show strong agreement between the datasets, CH4 and N2O estimates exhibit substantial discrepancies due to differences in methodologies, emission factors, uncertainties, and reporting practices. Our findings emphasise the need for enhanced methodological harmonization and more frequent reporting, particularly in non-Annex I countries, where limited capacity and irregular updates reduce comparability. Addressing these inconsistencies is crucial for improving transparency, aligning national and independent datasets, and strengthening climate policy decisions under the Paris Agreement (UNFCCC Secretariat, 2021b).

Screening differences between a local inventory and the Emissions Database for Global Atmospheric Research (EDGAR)

Over the last three decades, socio-economic, demographic and technological transitions have been witnessed throughout the world, modifying both sectorial and geographical distributions of greenhouse gas (GHG) emissions. Understanding these trends is central to the design of current and future climate change mitigation policies, requiring up-to-date methodologically robust emission inventories such as the Emissions Database for Global Atmospheric Research (EDGAR), the European Commission’s in-house, independent global emission inventory. EDGAR is a key tool to track the evolution of GHG emissions and contributes to quantifying the global carbon budget, providing independent and systematically calculated emissions for all countries.According to the results of the EDGAR v.5.0 release, total anthropogenic global greenhouse gas emissions (excluding land use, land use change and forestry) were estimated at 49.1 Gt CO2eq in 2015, 50 % higher than in 1990, despite a monotonic decrease in GHG emissions per unit of economic output. Between 1990 and 2015, emissions from developed countries fell by 9%, while emissions from low to medium income countries increased by 130%, predominantly from 2000 onwards. The 27 Member States of the European Union and the United Kingdom led the pathway for emission reductions in industrialised economies whilst, in developing countries, the rise in emissions was driven by higher emissions in China, India, Brazil and nations in the South-East Asian region. This diversity of patterns shows how different patterns for GHG emissions are and the need for identifying regionally tailored emission reduction measures.

Emission inventory of primary air pollutants in 2010 from industrial processes in Turkey