National Inventory Research Articles

Estimation of concentrations of climate-active substances in emissions from processing of solid animal by-products

The need to obtain reliable data on greenhouse gas emissions from the livestock sector is becoming increasingly urgent. Actual measurement of gas concentrations from animal byproducts will improve the accuracy of data submitted to the National Inventory Report.The aim of the study was to determine the concentration of climatically active substances in emissions from solid by-products of animal husbandry at an experimental laboratory facility. All types of animal by-products studied came from large agricultural organisations. The initial raw material for the study was animal by-products as bedding poultry manure, solid fraction of pig manure and solid fraction of cattle manure. Concentrations of climate-active substances in emissions were investigated on the experimental laboratory set-up designed at IEEP — branch of FSAC VIM, simulating temperature conditions of composting and periodic aeration operation. a four-channel ELAN plus gas analyser measured the concentrations of CO2, CH4, H2S and NH3. The concentration of nitrous oxide in the gas-air mixture was determined in the laboratory of the Agrophysical Research Institute using a “Kristalluks-4000M” gas chromatograph. The obtained results were analysed by the method of mathematical statistics. The results of the study proved the potential of the designed laboratory facility to provide a representative data on emissions of climate-active substances from animal by-products, comparable to the results of other studies in this field.

Expanding cryospheric landform inventories – quantitative approaches for underestimated periglacial block- and talus slopes in the Dry Andes of Argentina

There is a clear spatial discrepancy between the area potentially underlain by permafrost and the landforms recorded in the national inventory of cryospheric landforms in the Dry Andes of Argentina (∼22°–35°S). In the periglacial belt around 30°S, these areas are often covered by extensive block- and talus slopes, whose distribution and potential permafrost content have received little attention so far. We present the first geomorphological mapping and predictive modeling of these underestimated landforms in a semi-arid high Andean catchment with representative cryospheric landform cover (30°S, 69°W). Random forest models produce robust and transferable predictions of both target landforms, demonstrating a high predictive power (mean AUROC values ≥0.95 using non-spatial validation and ≥0.83 using spatial validation). By combining geomorphological mapping, predictive modeling, and geostatistical analysis of block- and talus slopes, we enhance our knowledge of their distribution characteristics, formative controls and potential ground ice content. While both landforms provide suitable site conditions for permafrost occurrence, talus slopes are expected to contain significantly higher ground ice content than blockslopes due to their more favorable characteristics for ice formation and preservation. Given their widespread distribution across almost 79% of the modeled area, block- and talus slopes constitute potentially important ground ice storages and runoff contributors that are not included in current hydrological assessments of mountain permafrost. Our results underscore the need to expand existing cryospheric landform inventories to achieve a more comprehensive quantification of underrepresented periglacial landforms and thus a realistic acquisition of cryospheric water resources in high mountain environments. The newly compiled inventories can serve as a basis for further investigations (e.g., geophysical surveys, hydrochemical analysis, permafrost distribution models) at different spatial scales.

Quantifying Methane Emission Rates Using Downwind Measurements

This entry describes the methods used to quantify methane emissions from either point or area sources using downwind methods. The methods described could be used as a practical guide to quantify emissions of any trace gas type from either a point or area emission source. Methane is a relatively strong greenhouse gas, its GWP is 25 times larger than CO2 over a 100-year period, and an increase in methane anthropogenic emissions has been correlated to a changing global climate. Emission estimates that are calculated and used for national inventories are usually derived from bottom-up approaches, however there is now an increasing pressure for these to be validated by direct measurement. Calculating emission rates from downwind measurements has proven to be a versatile and relatively simple approach for direct measurement. Downwind measurement method descriptions are presented here as a practicable guide to quantifying point and area source emissions. Emission quantification is a two-stage process where methane concentration and meteorological data must be measured downwind of a source and then converted to emissions using an atmospheric dispersion model. Only four technology types currently measure in the range typical of downwind methane concentrations: metal oxide sensors, non-dispersive infrared sensors, tunable diode laser absorption spectrometers and optical cavity instruments. The choice of methane measurement is typically determined by the size of the emission source, location and the budget of the project. Meteorological data are essential to quantifying emissions, especially regarding wind speed and direction. In most cases, simple atmospheric dispersion approaches can be used to quantify both area and point emissions using these downwind measurements. Emissions can be generated using limited data (only methane concentration, wind speed, wind direction, and locations are necessary), but quantification uncertainty can be reduced using more input data. Site selection and location of instrument deployment are essential because quantification approaches assume a flat fetch (no aerodynamic obstructions) and constant wind fields. When modeling assumptions are violated, quantification uncertainty can range between +250% and −100% of the actual emission rate. At present there, is no happy medium between modeling complexity and computational time, and this remains the biggest challenge for downwind emission quantification.

A field-measured dataset for soil organic carbon stock in South Korean grasslands

Grasslands, covering about one-quarter of global land use, are crucial for carbon storage, holding approximately 10% of global soil organic carbon (SOC). In South Korea, SOC stock estimates for grasslands currently rely on Tier 1 from the Intergovernmental Panel on Climate Change Guidelines (IPCC GL) for National Greenhouse Gas Inventory, lacking country-specific SOC data. Grassland managements also change SOC stocks, but are not reflected in the current estimation. To advance the accuracy of SOC stock estimation, this study provided a field-measured dataset from 46 grassland sites in South Korea, accounting for variations in soil type (high-activity clay, low-activity clay, sandy, and volcanic soils) and management intensity (improved, nominally managed, and degraded). The dataset, including 208 soil core samples with SOC stock, C/N, and H/C ratios, offers a foundational resource for developing country-specific reference SOC stock values and incorporating management factors to influence SOC stock. This dataset will support improvements to South Korea’s national greenhouse gas inventory for grasslands to Tier 2 and Tier 3, aligned with the IPCC GL.

Assessing disparities in cancer resources distribution in Mexico

BackgroundGiven the rising cancer burden, the capacity of Mexico’s healthcare system to effectively address cancer care through its current available infrastructure becomes increasingly critical. Limited availability of diagnostic and therapeutic infrastructure leads to delays in diagnosis and treatment. Countries like Mexico, should undertake comprehensive assessments of infrastructure and human resources available for cancer, including its quantification and geolocation, to understand the service gaps. This study seeks to map oncological infrastructure in Mexico in five types of cancer: breast, lung, prostate, colon, and cervix.MethodsThrough a realist evaluation of publicly available databases from the High Specialty Medical Equipment National Inventory and the General Direction of Health Information, a comprehensive identification and classification of cancer resources was conducted with the intended outcome to map cancer care infrastructure in Mexico. Guided by the literature, resources necessary for diagnosis and treatment were selected by an expert consensus. Thereafter, facilities were classified by service scope into either diagnostic or diagnostic and therapeutic, and by infrastructure level into core or enhanced and then mapped geographically.ResultsFrom N = 14,133 unique healthcare facilities that deliver any type of healthcare, only 5% provided cancer care. Cancer-specific infrastructure that can provide diagnosis and treatment at the national level included N = 10 brachytherapy, N = 11 cobalt-60, N = 21 linear particle accelerators and N = 188 operating rooms. Five issues were found: (1) low availability of core therapeutic infrastructure across all cancer types; (2) regional and national centralization of available therapeutic infrastructure for all cancer types, whilst no centralization found in diagnostic resources; (3) inconsistent allocation of resources in densely populated areas; (4) infrastructure disparities per cancer type i.e., Lung, prostate, and breast cancer require significant investments in diagnostic infrastructure compared to cervical and colon cancer, and (5) lack of precise and updated infrastructure data from the public health system that requires either new codes or subcodes.ConclusionsAddressing disparities in cancer resources distribution in Mexico is a dual imperative—ensuring equity while seizing an opportunity to fortify the overall health system for people without social security coverage.

Climate change mitigation potential of cascading utilization of wood resources

Harvested wood products are a carbon pool that can be included in the national greenhouse gas inventory and are attracting attention as an important tool for reducing greenhouse gas emissions. This study evaluates the carbon reduction potential of harvested wood products by performing policy scenario analysis. It reveals that maximizing wood resource efficiency under existing policies to boost timber and sawn wood production offers the greatest reduction potential. This study found that the greenhouse gas reduction potential of the scenario with increased harvesting, in line with current government policies, is comparable to the scenario of cascading wood resource utilization without increasing harvest levels. This study provides valuable policy insights that promote national greenhouse gas reduction, while leading to a societal transformation that guarantees ecological soundness through the efficient use of domestic timber resources.

Trees outside forests in Ghana: current state, spatial distribution, and future projection

Trees outside forests (TOF) are increasingly recognized for their vital contributions to environmental sustainability and socioeconomic development. TOF are undervalued and seldom included in national inventories and international reporting frameworks, particularly in Africa. There is a need to estimate the state, spatial distribution, and future projection of TOF in Ghana at the national scale using effective tools such as Geographic Information Systems (GIS) and remote sensing techniques. Environmental Systems Research Institute (ESRI) global land cover data at 10 m resolution was used for this study between 2017 and 2023, and Quantum GIS Modules for Land-Use Change Simulation (MOLUSCE) was used to predict future TOF. The results show that the area occupied by TOF declined from 38.2 to 33.8% at an annual rate of −2.02%, whereas the area occupied by trees inside forests increased from 10.4 to 10.7% in Ghana from 2017 to 2023, respectively. The decline is expected to continue in Ghana, with an annual rate of −0.79% by 2030. Ashanti region and Sekyere Afram Plains North had the largest area of TOF, whereas the Greater Accra region, with its four districts, had the least TOF during the study period in Ghana. TOF hold the second largest area in Ghana and should be recognized in climate change mitigation initiatives in Ghana and the nationwide forest monitoring systems.

A nationwide approach to reduction in anaesthetic gas use: the Dutch Approach to decarbonising anaesthesia.

A nationwide approach to reduction in anaesthetic gas use: the Dutch Approach to decarbonising anaesthesia.

An Algorithm for Maximum Concordance to Harmonize Legends of Land Use and Land Cover Maps

Land use and land cover maps (LULC) are abstractions of the physical space of a chosen region. Comparison of LULC maps is essential to understand landscape dynamics, alteration patterns, and environmental implications. This article has the objective of propose an algorithm for harmonizing LULC maps based on the spatial distribution of their classes and applies it in a case study to harmonize the maps of Brazil’s National Inventory of Anthropogenic Emissions by Sources and Removals of Greenhouse Gases (Fourth Version) and MapBiomas (Collection 7) based on their spatial distribution of LULC classes. The purpose of this paper is to compute the agreement between two initiatives. Furthermore, the results highlight the classes and areas of potential inconsistency or ambiguity, allowing to identify and correct discrepancies, proposing a harmonized legend between then. At the national level, we reached maximum agreement 81% between the two maps. Of the 44 equivalences, the algorithm accurately recognized 36 of the connections between the classes. At the biome level, the algorithm achieved its highest concordance within the Amazonia biome, surpassing Brazil’s level by 11%, mainly due to the size and homogeneity of the forest classes. In biomes with a predominance of nonforest vegetation, an increased confusion was observed among the classes ‘Grassland’, ‘Pasture’, and ‘Forest’ was observed between the maps, especially in Pampa and Caatinga.

Revised and updated geospatial monitoring of 21st century forest carbon fluxes

Abstract. Earth observation data are increasingly used to estimate the magnitude and geographic distribution of greenhouse gas (GHG) fluxes and reduce overall uncertainty in the global carbon budget, including for forests. Here, we report on a revised and updated geospatial, Earth-observation-based modeling framework that maps GHG emissions, carbon removals, and the net balance between them globally for forests from 2001 to 2023 at roughly 30 m resolution, hereafter referred to as the Global Forest Watch (GFW) model (see the “Code and data availability” section). Revisions address some of the original model's limitations, improve model inputs, and refine the uncertainty analysis. We found that, between 2001 and 2023, global forest ecosystems were, on average, a net sink of −5.5 ± 8.1 Gt CO2e yr−1 (gigatonnes of CO2 equivalent per year ± 1 standard deviation), which reflects the balance of 9.0 ± 2.7 Gt CO2e yr−1 of GHG emissions and −14.5 ± 7.7 Gt CO2 yr−1 of removals, with an additional −0.20 Gt CO2 yr−1 transferred into harvested wood products. Uncertainty in gross removals was greatly reduced compared with the original model due to the refinement of uncertainty for carbon removal factors in temperate secondary forests. After reallocating GFW's gross CO2 fluxes into anthropogenic fluxes from forest land and deforestation categories to increase the conceptual similarity with national greenhouse gas inventories (NGHGIs), we estimated a global net anthropogenic forest sink of −3.6 Gt CO2 yr−1, excluding harvested wood products, with the remaining net CO2 flux of −2.2 Gt CO2 yr−1 reported by the GFW model as non-anthropogenic. Although the magnitude of GFW's translated estimates aligns relatively well with aggregated NGHGIs, the temporal trends differ. Translating Earth-observation-based flux estimates into the same reporting framework that countries use for NGHGIs helps build confidence around land use carbon fluxes and supports independent evaluation of progress towards Paris Agreement goals. The data availability is as follows: carbon removals (Gibbs et al., 2024a, https://doi.org/10.7910/DVN/V2ISRH), GHG emissions (Gibbs et al., 2024b, https://doi.org/10.7910/DVN/LNPSGP), and net flux (Gibbs et al., 2024c, https://doi.org/10.7910/DVN/TVZVBI).

Global greenhouse gas reconciliation 2022

Abstract. In this study, we provide an update on the methodology and data used by Deng et al. (2022) to compare the national greenhouse gas inventories (NGHGIs) and atmospheric inversion model ensembles contributed by international research teams coordinated by the Global Carbon Project. The comparison framework uses transparent processing of the net ecosystem exchange fluxes of carbon dioxide (CO2) from inversions to provide estimates of terrestrial carbon stock changes over managed land that can be used to evaluate NGHGIs. For methane (CH4), and nitrous oxide (N2O), we separate anthropogenic emissions from natural sources based directly on the inversion results to make them compatible with NGHGIs. Our global harmonized NGHGI database was updated with inventory data until February 2023 by compiling data from periodical United Nations Framework Convention on Climate Change (UNFCCC) inventories by Annex I countries and sporadic and less detailed emissions reports by non-Annex I countries given by national communications and biennial update reports. For the inversion data, we used an ensemble of 22 global inversions produced for the most recent assessments of the global budgets of CO2, CH4, and N2O coordinated by the Global Carbon Project with ancillary data. The CO2 inversion ensemble in this study goes through 2021, building on our previous report from 1990 to 2019, and includes three new satellite inversions compared to the previous study and an improved managed-land mask. As a result, although significant differences exist between the CO2 inversion estimates, both satellite and in situ inversions over managed lands indicate that Russia and Canada had a larger land carbon sink in recent years than reported in their NGHGIs, while the NGHGIs reported a significant upward trend of carbon sink in Russia but a downward trend in Canada. For CH4 and N2O, the results of the new inversion ensembles are extended to 2020. Rapid increases in anthropogenic CH4 emissions were observed in developing countries, with varying levels of agreement between NGHGIs and inversion results, while developed countries showed a slowly declining or stable trend in emissions. Much denser sampling of atmospheric CO2 and CH4 concentrations by different satellites, coordinated into a global constellation, is expected in the coming years. The methodology proposed here to compare inversion results with NGHGIs can be applied regularly for monitoring the effectiveness of mitigation policy and progress by countries to meet the objectives of their pledges. The dataset constructed for this study is publicly available at https://doi.org/10.5281/zenodo.13887128 (Deng et al., 2024).

Improved methodology for the calculated monitoring of greenhouse gas emissions from the activities of road and off-road transport in the Russian Federation

A methodology is proposed to obtain consistent and coherent estimates of greenhouse gas and precursor gas emissions from road transport and off-road vehicles and to quantify their accuracy and uncertainty. Accuracy increase of the estimates is achieved by detailing the initial data, reflecting the specific features of the national vehicle fleet structure and transport activity indicators, for which a wide range of statistical, probabilistic and expert methods are used. Using the methodology implemented in the “Transport Model” software product, it is possible to reconstruct time series of emissions for previous periods with an assessment of their accuracy and uncertainties, as well as to perform scenario modelling in order to forecast future emissions. The methodology was used to obtain quantitative estimates of three types of greenhouse gases emissions and three precursor gases from road transport and off-road vehicles for the period of 2010 to 2022, to compare the results with National Inventory data, to produce emission estimates for four categories of off-road vehicles for the first time, and to estimate fuel consumption and changes in the number of vehicles of all categories for the period of 1990 to 2022. An information array containing official statistical data on indirect indicators of transport activity by vehicle category for the period of 1990 to 2022 was compiled, the results of the emissions calculations were compared with the indirect indicators using multiple regression methods, and the accuracy and uncertainties of these results were quantitatively assessed. The estimated greenhouse gas emissions were considered as random values for which a confidence interval was calculated as an indicator of uncertainty and a median correction as an indicator of accuracy. The high quality of the results of the calculations based on the methodology considered was confirmed.

The Importance of Ditches and Canals in Global Inland Water CO2 and N2O Budgets.

Ditches and canals are omitted from global budgets of inland water emissions, despite research showing them to be emitters of greenhouse gases (GHGs). Here, we synthesize data across climate zones and land use types to show, for the first time, that global ditches emit notable amounts of carbon dioxide (CO2) and nitrous oxide (N2O). Ditches had higher per-area emissions of CO2 and N2O than ponds, lakes, and reservoirs, likely due to high nutrient inputs. Preliminary upscaling showed that the inclusion of ditches would increase global inland water CO2 emissions by 0.6%-1% and N2O emissions by 3%-9%. Trophic state and climate influenced N2O emissions, while CO2 emissions had complex drivers difficult to disentangle at the global scale. This research highlights the importance of including ditches in global inland water GHG budgets and informs more accurate reporting of anthropogenic emissions in national inventories.

Estimating GHG emissions from cloud computing: sources of inaccuracy, opportunities and challenges in location-based and use-based approaches

Estimating GHG emissions from cloud computing: sources of inaccuracy, opportunities and challenges in location-based and use-based approaches

China's greenhouse gas budget during 2000–2023

ABSTRACT National greenhouse gas (GHG) budget, including CO2, CH4 and N2O has increasingly become a topic of concern in international climate governance. China is paying increasing attention to reducing GHG emissions and increasing land sinks to effectively mitigate climate change. Accurate estimates of GHG fluxes are crucial for monitoring progress toward mitigating GHG emissions in China. This study used comprehensive methods, including emission factor methods, process-based models, atmospheric inversions, and data-driven models, to estimate the long-term trends of GHG sources and sinks from all anthropogenic and natural sectors in China's mainland during 2000–2023, and produced an up-to-date China GHG Budget dataset (CNGHG). The total gross emissions of the three GHGs show a 3-fold increase from 5.0 (95% CI: 4.9–5.1) Gt CO2-eq yr−1 (in 2000) to 14.3 (95% CI: 13.8–14.8) Gt CO2-eq yr−1 (in 2023). CO2 emissions represented 81.8% of the GHG emissions in 2023, while 12.7% and 5.5% were for CH4 and N2O, respectively. As the largest CO2 source, the energy sector contributed 87.4% CO2 emissions. In contrast, the agriculture, forestry and other land use sector was the largest sector of CH4 and N2O, representing 50.1% and 66.3% emissions, respectively. Moreover, China's terrestrial ecosystems serve as a net CO2 sink (1.0 Gt CO2 yr−1, 95% CI: 0.2–1.9 Gt CO2 yr−1) during 2012 to 2021, equivalent to an average of 14.3% of fossil CO2 emissions. Our GHG emission estimates showed a general consistency with national GHG inventories, with gridded and sector-specific estimates of GHG fluxes over China, providing the basis for curtailing GHG emissions for each region and sector.

Biodiversity of Bees (Hymenoptera: Apoidea: Anthophila) in Connecticut (USA).

In response to calls for national and regional updated inventories of bee species, we present a county-level checklist for 385 confirmed bee (Apoidea: Anthophila) species for Connecticut, USA, highlighting rare and regionally declining species, species that have specific habitat and/or host requirements, and species whose taxonomy and distribution we wish to clarify. We have compiled a comprehensive, digitized database of historic and current bee records from Connecticut to inform this checklist, which includes specimen records from museums, recent collections, and community science observations from iNaturalist.com. All images of bees from Connecticut on iNaturalist (18,471 observations) have been fully vetted by one or more of the authors, which is unprecedented for a state project. We summarize historical bee research in Connecticut and provide current information regarding the distribution of bee species, changes in status, phenology, habitat usage, and floral associations within the state. At least 43 of 385 species represented in collections or literature have not been detected in Connecticut since the year 2000. These and other species of conservation concern are discussed with reference to a quantitative assessment of changes in range within the state. In addition, we have calculated and report state-level ranks for 124 bee species in Connecticut. We corroborate regional loss of species including Coelioxys funerarius Smith and Holcopasites illinoiensis (Robertson) and clarify and extend the distribution of numerous bee species in the Northeastern United States. Furthermore, we discuss morphospecies, excluded species, and species expected for Connecticut. We also validate synonymies reported previously online based on an unpublished manuscript by Roy Snelling for the following species: Nomada depressa Cresson (= N. hoodiana Cockerell; = N. carinicauda Cockerell; = N. media Mitchell); Nomada obliterata Cresson (= N. decepta Mitchell); Nomada vicina Cresson (= N. beulahensis Cockerell; = N. vicina stevensi Swenk). In addition, we recognize three new synonyms of Nomada xanthura Cockerell (= N. ochlerata Mitchell; = N. detrita Mitchell; = N. mendica Mitchell) and report the first Nomada townesi Mitchell from outside of Maryland. In addition to N. townesi, the following eleven native species are newly reported or recently confirmed for Connecticut: Andrena (Cnemidandrena) parnassi----ae Cockerell; Andrena (Melandrena) sayi Robertson; Andrena (Trachandrena) rehni Viereck; Anthophora bomboides Kirby; Nomada armatella Cockerell; Nomada electella Cockerell; Nomada placida Cresson; Lasioglossum (Dialictus) cattellae (Ellis); Lasioglossum (Dialictus) ellisiae (Sandhouse); Lasioglossum (Dialictus) fattigi (Mitchell); Lasioglossum (Dialictus) trigeminum Gibbs. The following recent arrivals among non-native species are confirmed: Pseudoanthidium (Pseudoanthidium) nanum (Mocsáry); Coelioxys (Allocoelioxys) coturnix Pérez; Osmia (Osmia) taurus Smith. This work is a stepping stone towards a larger, ongoing effort to clarify bee distribution and status in New England. As such, we also report updates for the bee fauna of the following states: Massachusetts-Melissodes communis communis Cresson; Megachile (Eutricharaea) apicalis Spinola), Maine-Chelostoma philadelphi (Robertson), and New Hampshire-Lasioglossum nelumbonis (Robertson).

A harmonized dataset relating alternative farmer management practices to crop yield, soil organic carbon stock, nitrous oxide emissions, and nitrate leaching generated using IPCC methodologies and meta-analyses.

Farming practices such as soil tillage, organic/mineral fertilization, irrigation, crop selection and residues management influence multiple ecosystem services provided by agricultural systems. These practices exhibit complex, non-linear interrelationships that affect crop productivity, water quality, and non-carbon dioxide greenhouse gases (GHG) emissions, possibly offsetting their benefits regarding soil organic carbon (SOC) sequestration. Current methodologies from the Intergovernmental Panel on Climate Change (IPCC) for assessing the impacts of alternative farming practices on GHG emissions rely on global or country-specific coefficients. However, these methods often do not explicitly account for the combined effects of management practices on carbon and nitrogen cycles or productivity, as this is not required for national GHG inventories. Here we present a new dataset featuring 1.8 Mln of agronomic case scenarios, i.e., unique combinations of farming practices and pedoclimatic conditions, which have been associated with values of SOC changes, nitrous oxide emissions, nitrate-nitrogen leaching, and crop yield. To synthesize trade-offs and synergies between farming practices, each case scenario has been ranked with a ∑ommit index (∑i) value, a fuzzy-based measure ranging from 0 (bad) to 1 (good). The four trade-off components have been estimated by combining available information from i) the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, ii) the guidelines for Green Water Footprint Accounting, iii) the Italian National Institute of Statistics, iv) and other international meta-analytic studies. The dataset presents four ∑i series, corresponding to alternative perceptions of sustainability from three potential stakeholder categories (young farmers' cooperative, agrochemical company, public agricultural policy agency) plus one equally weighted option. By providing a harmonized data source and an innovative metric, this dataset allows users to explore trade-offs associated with alternative management practices across four key agricultural components and assess their impact on perceived agroecosystem sustainability.

The unseen carbon: Scope 3 emissions transform understanding of electricity generation in import-dependent nations.

The unseen carbon: Scope 3 emissions transform understanding of electricity generation in import-dependent nations.

The greenhouse gas observation mission with Global Observing SATellite for Greenhouse gases and Water cycle (GOSAT-GW): objectives, conceptual framework and scientific contributions

The Japanese Global Observing SATellite for Greenhouse gases and Water cycle (GOSAT-GW) will be an Earth-observing satellite to conduct global observations of atmospheric carbon dioxide (CO2), methane (CH4), and nitrogen dioxide (NO2) simultaneously from a single platform. GOSAT-GW is the third satellite in the series of the currently operating Greenhouse gases Observing SATellite (GOSAT) and GOSAT-2. It will carry two sensors, the Total Anthropogenic and Natural emissions mapping SpectrOmeter-3 (TANSO-3) and the Advanced Microwave Scanning Radiometer 3 (AMSR3), with the latter dedicated to the observation of physical parameters related to the water cycle. TANSO-3 is a high-resolution grating spectrometer designed to measure reflected sunlight in the visible to short-wave infrared spectral ranges. It aims to retrieve the column-averaged dry-air mole fractions of CO2 and CH4 (denoted as XCO2 and XCH4, respectively), as well as the vertical column density of tropospheric NO2. The TANSO-3 sensor onboard GOSAT-GW will utilize the wavelength bands of 0.45, 0.76, and 1.61 µm for NO2, O2, and CO2 and CH4 retrievals, respectively. GOSAT-GW will fly in a sun-synchronous orbit with a local overpass time of approximately 13:30 and a 3-day ground-track repeat cycle. The TANSO-3 sensor has two observation modes in the push-broom operation: Wide Mode, which provides globally covered maps with a 10-km spatial resolution within 3 days, and Focus Mode, which provides snapshot maps over targeted areas with a high spatial resolution of 1–3 km. The objectives of the GOSAT-GW mission include (1) monitoring atmospheric global-mean concentrations of greenhouse gasses (GHGs), (2) verifying national anthropogenic GHG emissions inventories, and (3) detecting GHG emissions from large sources, such as megacities and power plants. A comprehensive validation exercise will be conducted to ensure that the sensor products’ quality meets the required precision to achieve the above objectives. With a projected operational lifetime of seven years, GOSAT-GW will provide vital space-based constraints on both anthropogenic and natural GHG emissions. These measurements will contribute significantly to climate change mitigation efforts, particularly by supporting the Global Stocktake (GST) mechanism, a key element of the Paris Agreement.

Evaluating Methane Emission Estimates from Intergovernmental Panel on Climate Change Compared to Sentinel-Derived Air–Methane Data

This study compares the methane emission estimates from the Intergovernmental Panel on Climate Change with satellite-based measurements from Sentinel-5P to assess trends in global methane emissions and concentrations. Focusing on the countries listed in Annex I of the United Nations Framework Convention on Climate Change and the key sectors of Agriculture, Energy, industrial processes and product use, land use, land use change, forestry, and Waste, this analysis uses data from 1990 to 2021, evaluated through the Mann–Kendall trend test. The findings reveal a decline in methane emissions reported by the IPCC, particularly in the Energy and Waste sectors, driven by stricter environmental regulations and technological advancements in these regions. However, the satellite data from the TROPOspheric Monitoring Instrument for 2019–2024 indicate an increasing trend in atmospheric methane concentrations, suggesting that the reductions reported in the inventories may be insufficient to offset ongoing or previously accumulated emissions. The discrepancies between the IPCC inventories and the satellite observations highlight the challenges in methane source attribution and the limitations of relying solely on inventory-based methods. This study demonstrates the potential of integrating high-resolution satellite data with the traditional methodologies to improve the accuracy of methane emission estimates. Such an approach provides a more comprehensive understanding of methane dynamics, particularly in regions where natural and anthropogenic sources overlap. The findings of this study contribute to a better understanding of global methane emission trends and their implications for climate change. Integrating satellite observations into national inventories has practical applications for enhancing methane monitoring, improving emission reporting, and supporting global climate goals through the more effective and sustainable management of methane emissions.