Emission Estimates Research Articles

Uncertainties of biogenic VOC emissions caused by land cover data and implications on ozone mitigation strategies for the Yangtze river Delta region

Biogenic volatile organic compounds (BVOC) play a crucial role in ground-level ozone formation, yet emission quantification faces considerable uncertainties stemming from input data. Land cover data, which determines the distribution of growth forms, is an essential driving input of BVOC emissions. This study explores the uncertainty in BVOC emission estimates arising from using two land cover datasets, specifically the MODIS and ESA land cover data, and the implications for ozone mitigation strategies in the Yangtze River Delta (YRD) region. By employing the MEGAN model for the year 2021, we estimated that ESA land cover data, with a finer 10 m resolution, yields a total BVOC emission estimate of 2299 Gg, which is over 52.9% higher than the MODIS dataset with a resolution of 500 m, attributed to the higher tree coverage (33.9% in ESA vs. 17.6% in MODIS) identified in the ESA dataset. We then simulated the ozone concentrations by both emission estimates with the WRF-CMAQ model. The elevated BVOC emissions based on the ESA data improved the underestimation of simulated isoprene concentrations and consequently resulted in more than 30% higher domain-averaged MDA8 ozone concentration compared to the MODIS-derived BVOC emission. Regarding ozone mitigation strategy, simulation with the ESA-based BVOC emissions results in 0.3–1.2 μg/m3 less ozone reductions compared to that with the MODIS-based emissions when reducing anthropogenic VOC emissions, but 0.7–2.9 μg/m3 higher ozone reductions when controlling anthropogenic NOx emissions. These disparities are more pronounced at the city level, highlighting the importance of accurate BVOC emission estimates for effective ozone control strategies.

Accurate and efficient daily carbon emission forecasting based on improved ARIMA

Major nations across the globe are increasingly concerned about the rising trends in carbon dioxide (CO2) emissions, particularly in societies of varying scales. Against this backdrop, precise prediction of carbon emissions becomes critically important, especially for the formulation and adjustment of near-term carbon reduction policies. However, the non-linear, non-stationary, and complex nature of daily carbon emission data poses a great challenge for daily-level forecasting especially in the big data context. To address this issue, we propose a novel composite forecasting approach named DCEF dedicated to the estimation of daily carbon emissions. In concrete, our approach employs the Empirical Mode Decomposition (EMD) for data stabilization and the Auto-regressive Integrated Moving Average (ARIMA) model for forecasting, while integrating the Truncated singular value decomposition(TSVD) technique for data compression and mitigating noise. Finally, DCEF is empirically validated with real daily carbon emission datasets collected from 6 sectors in 13 countries of varying sizes. Experimental results demonstrate the advantages of our approach compared to other baseline models in terms of prediction accuracy and efficiency.

Estimation of Greenhouse Gas Emissions of Taxis and the Nonlinear Influence of Built Environment Considering Spatiotemporal Heterogeneity

The fuel consumption and greenhouse gas (GHG) emission patterns of taxis are in accordance with the urban structure and daily travel footprints of residents. With taxi trajectory data from the intelligent transportation system in Xi’an, China, this study excludes trajectories from electric taxis to accurately estimate GHG emissions of taxis. A gradient boosting decision tree (GBDT) model is employed to examine the nonlinear influence of the built environment (BE) on the GHG emissions of taxis on weekdays and weekends in various urban areas. The research findings indicate that the GHG emissions of taxis within the research area exhibit peak levels during the time intervals of 7:00–9:00, 12:00–14:00, and 23:00–0:00, with notably higher emission factors on weekends than on weekdays. Moreover, a clear nonlinear association exists between BE elements and GHG emissions, with a distinct impact threshold. In the different urban areas, the factors that influence emissions exhibit spatial and temporal heterogeneity. Metro/bus/taxi stops density, residential density, and road network density are the most influential BE elements impacting GHG emissions. Road network density has both positive and negative influences on the GHG emissions in various urban areas. Increasing the road network density in subcentral urban areas and increasing the mixed degree of urban functions in newly developed urban centers to 1.85 or higher can help reduce GHG emissions. These findings provide valuable insights for reducing emissions in urban transportation and promoting sustainable urban development by adjusting urban functional areas.

Low methane emissions from Australian estuaries influenced by geomorphology and disturbance

Estuaries are a globally important source of methane, but little is known about Australia’s contributions to global estuarine methane emissions. Here we present a first-order Australia-wide assessment of estuarine methane emissions, using methane concentrations from 47 estuaries scaled to 971 Australian estuaries based on geomorphic estuary types and disturbance classes. We estimate total mean (±standard error) estuary annual methane emissions for Australia of 30.56 ± 12.43 Gg CH4 yr−1. Estuarine geomorphology and disturbance interacted to control annual methane emissions through differences in water–air methane flux rates and surface area. Most of Australia’s estuarine surface area (89.8%) has water–air methane fluxes lower than global means, contributing 80.3% of Australia’s total mean annual estuarine methane emissions. Australia is a good analogue for the ~34% of global coastal regions classified as less than moderately disturbed (>40% intact), suggesting that these regions may also have lower methane fluxes. On this basis, recent global estuarine methane emission estimates that do not consider disturbance in their upscaling, probably overestimate global estuarine methane emissions.

Quantifying CO2 emissions of power plants with Aerosols and Carbon Dioxide Lidar onboard DQ-1

Transparent, accurate, and affordable monitoring of CO2 emissions from power plants is crucial for realization of transition away from fossil fuels. Satellite remote sensing has emerged as a recent research focus, enabling emission estimation without the cooperation of emitters. In 2022, China launched the world's first CO2 monitoring satellite based on laser detection, DQ-1. Equipped with ACDL, it enables accurate detection even under nighttime and high aerosol load conditions, serving as a significant complement to existing optical satellite systems. However, the feasibility of laser detection with narrow swath width for monitoring power plant emissions remains uncertain. This work presents, for the first time, the practical capability of using spaceborne LIDAR to estimate CO2 emissions from power plants. We employ the Gaussian dispersion model as the transport model and quantify emission rates using genetic and trust-region algorithms. Through satellite observations from June to December 2022, we conducted emission estimation experiments for 15 globally distributed power plants. Results indicate that using ACDL's XCO2 yields approximately unbiased estimates (∼2%) of power plant emissions overall, despite a single-overpass uncertainty of 18%. This performance is comparable to OCO-2/3, suggesting consistent benchmarks for different satellite mechanisms in reporting power plant emissions. Consequently, combining their results enhances monitoring of global power plants. We advocate the joint use of diverse CO2 monitoring satellites employing various detection mechanisms to gather a more comprehensive XCO2 dataset, aiming to achieve routine satellite-based verification of power plant emissions.

Drone CO2 measurements during the Tajogaite volcanic eruption

Abstract. We report in-plume carbon dioxide (CO2) concentrations and carbon isotope ratios during the 2021 eruption of Tajogaite volcano, island of La Palma, Spain. CO2 measurements inform our understanding of volcanic contributions to the global climate carbon cycle and the role of CO2 in eruptions. Traditional ground-based methods of CO2 collection are difficult and dangerous, and as a result only about 5 % of volcanoes have been directly surveyed. We demonstrate that unpiloted aerial system (UAS) surveys allow for fast and relatively safe measurements. Using CO2 concentration profiles we estimate the total flux during several measurements in November 2021 to be 1.76±0.20×103 to 2.23±0.26×104 t d−1. Carbon isotope ratios of plume CO2 indicate a deep magmatic source, consistent with the intensity of the eruption. Our work demonstrates the feasibility of UASs for CO2 surveys during active volcanic eruptions, particularly for deriving rapid emission estimates.

Atmospheric observation and emission estimation of HFC-125 and HFC-32 in China from four representative cities

HFC-125 and HFC-32 are fluorinated greenhouse gases of great concern due to their high GWPs and increasing background atmospheric concentrations. Long-term atmospheric observations of HFC-125 and HFC-32 were carried out in four representative cities of China (Beijing, Guangzhou, Hangzhou, and Lanzhou) from January 2012 to October 2019. Overall, the annual mean atmospheric concentrations of HFC-125 and HFC-32 both showed increasing trends, with average rates of 4.8 ppt yr−1 and 7.9 ppt yr−1. The average concentrations of HFC-125 and HFC-32 in urban areas were significantly higher than those in suburban areas. Significant differences in atmospheric concentrations of the two HFCs were observed among the four cities. HFC-125 and HFC-32 emissions were estimated accordingly, averaging 6.2 Gg yr−1 (23.6 Mt. CO2-eq) and 5.7 Gg yr−1 (4.3 Mt. CO2-eq) during 2012 and 2019 and growing at rates of 0.8 Gg yr−1 (3.1 Mt. CO2-eq) and 0.8 Gg yr−1 (0.6 Mt. CO2-eq), respectively, with an increasing contribution to global radiative forcing. The bottom-up inventories of HFC-125 and HFC-32 in the four cities increased annually from 2012 to 2019, with the highest emissions in Beijing, while the top-down emissions fluctuated during the research period. SynopsisThe atmospheric concentrations of HFC-125 and HFC-32 were measured from 2012 to 2019 in four representative cities of China. Both HFC emissions at national and city levels were estimated using observation-based and inventory methods.

Application of the wildland fire emissions inventory system to estimate fire emissions on forest lands of the United States

BackgroundForests are significant terrestrial biomes for carbon storage, and annual carbon accumulation of forest biomass contributes offsets affecting net greenhouse gases in the atmosphere. The immediate loss of stored carbon through fire on forest lands reduces the annual offsets provided by forests. As such, the United States reporting includes annual estimates of direct fire emissions in conjunction with the overall forest stock and change estimates as a part of national greenhouse gas inventories within the United Nations Framework Convention on Climate Change. Forest fire emissions reported for the United States, such as the 129 Tg CO2 reported for 2022, are based on the Wildland Fire Emissions Inventory System (WFEIS). Current WFEIS estimates are included in the Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2022 published in 2024 by the United States Environmental Protection Agency. Here, we describe WFEIS the fire emissions inventory system we used to address current information needs, and an analysis to confirm compatibility of carbon mass between estimated forest fire emissions and carbon in forest stocks.ResultsThe summaries of emissions from forests are consistent with previous reports that show rates and interannual variability in emissions and forest land area burned are generally greater in recent years relative to the 1990s. Both emissions and interannual variability are greater in the western United States. The years with the highest CO2 emissions from forest fires on the 48 conterminous states plus Alaska were 2004, 2005, and 2015. In some years, Alaska emissions exceed those of the 48 conterminous states, such as in 2022, for example. Comparison of forest fire emission to forest carbon stocks indicate there is unlikely any serious disconnect between inventory and fire emissions estimates.ConclusionsThe WFEIS system is a user-driven approach made available via a web browser. Model results are compatible with the scope and reporting needs of the annual national greenhouse gas inventories.

Long-term changes of methane emissions from rice cultivation during 2000 – 2060 in China: Trends, driving factors, predictions and policy implications

Regional budget assessments of methane (CH4) are critical for future climate and environmental management. CH4 emissions from rice cultivation (CH4-rice) constitute one of the most significant sources. However, previous studies mainly focus on historical emission estimates and lack consideration of future changes in CH4-rice under climate change or anthropogenic policy intervention, which hampers our understanding of long-term trends and the implementation of targeted emission reduction efforts. This study investigates the spatiotemporal variations of CH4-rice over the past two decades, using an integrated method to identify the major drivers and predict future emissions under climate change scenarios and policy perspectives. Results indicate that the CH4-rice emissions in China ranged between 6.21 and 6.57 Tg yr−1 over the past two decades, with a spatial distribution characterized by decreases in the south and increases in the north, associated with economic development, dietary shifts, technological advancements, and climate change. Factors such as the rate of straw added (RSA), fertilization, soil texture, temperature, and precipitation significantly influence CH4 emissions per unit rice production (CH4-urp), with RSA identified as the most significant tillage management factor, explaining 32 % of the variance. Lowering RSA to 8 % is beneficial for reducing CH4-urp. Scenario analysis indicates that under policies focusing on production or demand, CH4-rice is expected to increase by 0.3 % to 5.6 %, while adjusting RSA can reduce CH4-rice by 9.4 % to 10.0 %. Structural adjustments and regional cooperation serve as beneficial starting points for controlling and reducing CH4-rice in China, while optimizing industrial layouts contributes to regional development and CH4-rice control. Implementing policies related to maintaining field and crop yields can achieve a balance between rice supply and demand ahead of schedule. Dynamic adjustment of rice cultivation based on supply–demand balance can effectively reduce CH4-rice from excess rice production. By 2060, the reduction effect could reach 8.95 %–12.01 %. Introducing policy-driven tillage management measures as reference indicators facilitates the reduction of CH4-rice.

A Comprehensive Integration and Synthesis of Methane Emissions from Canada's Oil and Gas Value Chain.

Methane emissions from the global oil and gas value chain are a major contributor to climate change, and their mitigation could avoid 0.1 °C of warming by 2050. Here, we synthesize nearly a decade of research encompassing thousands of multiscale methane measurements along the oil and gas value chain (production to end use) to better constrain estimates of methane emissions from Canada's energy sector and to identify research gaps contributing to uncertainty in current estimates. We find that total value chain methane emissions are 2,600 (2,100-3,700) kt, which broadly agrees with Canada's latest official inventory that now includes atmospheric measurement data in some of their oil and gas methane estimates. Accurate understanding of emission magnitudes is critical because Canada committed to a 75% reduction of oil and gas methane emissions by 2030. We also identify and discuss information gaps in both emissions and activity data, namely, from the midstream, downstream, and end-use sectors. While they make up a smaller portion of the total inventory, accurate quantification of these emissions is still important and could point to more cost-effective mitigation solutions. This work emphasizes the need for frequent, comprehensive measurements to better constrain the climate impacts of the oil and gas sector and to validate reductions and commitments pledged by industry and governments.

Carbon footprint tracing and pattern recognition framework based on visual analytics

With growing concerns about global warming, industrial carbon footprints have garnered increased attention due to the energy-intensive and uninterrupted operation of industrial equipment. Fine-grained modeling and visual analytics of industrial carbon footprints can reveal the mechanisms behind the formation and evolution of carbon chains. However, the mechanisms underlying industrial carbon emissions remain unclear, leading to a lack of accuracy and specificity in current carbon quantification models. To address these gaps, we developed a comprehensive quantitative model that considers specific pathways involved in industrial processes, providing more accurate estimations of carbon emissions. We also designed an innovative visual analytical framework that uncovers implicit patterns and spatiotemporal distributions of industrial carbon footprints. By comparing our approach with state-of-the-art studies, we validated the superiority of our method in terms of its intuitiveness and interactivity. Empirical studies revealed potential emission patterns and spatiotemporal dynamics that traditional studies could not identify. We identified four consistent patterns in industrial carbon emissions: normal, high-emission, low-emission, and dedicated patterns. Our findings also led to optimization suggestions for different emission patterns, highlighting the system’s capability in extracting valuable insights from workshop carbon emission data. Our research showcases a unified visual analytical approach that supports exploratory analysis, and we believe it will uncover implicit knowledge within industrial carbon data, providing valuable insights for optimization.

Unraveling Spatially Diverse and Interactive Regulatory Mechanisms of Wetland Methane Fluxes to Improve Emission Estimation.

Methane fluxes (FCH4) vary significantly across wetland ecosystems due to complex mechanisms, challenging accurate estimations. The interactions among environmental drivers, while crucial in regulating FCH4, have not been well understood. Here, the interactive effects of six environmental drivers on FCH4 were first analyzed using 396,322 half-hourly measurements from 22 sites across various wetland types and climate zones. Results reveal that soil temperature, latent heat turbulent flux, and ecosystem respiration primarily exerted direct effects on FCH4, while air temperature and gross primary productivity mainly exerted indirect effects by interacting with other drivers. Significant spatial variability in FCH4 regulatory mechanisms was highlighted, with different drivers demonstrated varying direct, indirect, and total effects among sites. This spatial variability was then linked to site-specific annual-average air temperature (17.7%) and water table (9.0%) conditions, allowing the categorization of CH4 sources into four groups with identified critical drivers. An improved estimation approach using a random forest model with three critical drivers was consequently proposed, offering accurate FCH4 predictions with fewer input requirements. By explicitly accounting for environmental interactions and interpreting spatial variability, this study enhances our understanding of the mechanisms regulating CH4 emissions, contributing to more efficient modeling and estimation of wetland FCH4.

Materials Carbon Budget in Road Projects: A Case Study from The Greater Oslo Region

The estimation of carbon dioxide emissions in relation to the life cycle of road pavements is pivotal to quantify their sustainability at the design stage. This preliminary study assesses the carbon budgets concerning the production of aggregates and asphalt to build a new roadway stretch located close to Oslo (Norway), leveraging digital tools and environmental product declarations. First, the software Trimble Novapoint estimates the necessary quantity take-offs. Afterwards, the amounts of CO2 generated during the production of the construction materials are appraised for the four main industries operating nearby. The results based on the corresponding environmental product declarations indicate that the carbon budgets show very small discrepancies among the considered suppliers.

Estimation of wood biomass boiler use in cold climate regions on CO2 emissions of light-frame timber structure

Considering Nagano City, as the cold climate region of Japan, the annual heating load was simulated using a model of a two-story timber structure. The results of simulations were used to compare the CO2 emissions according to the energy consumption of five methods of heating as well as primary and secondary energy sources. Actual data from power and gas companies in Nagano and Japanese energy standards were used for the estimations of CO2 emissions of electric heat pump air conditioners, LNG-powered city gas boilers, LPG boilers, kerosene stoves, and wood biomass boilers using pellets as an energy source. Wood biomass boilers showed CO2 emission savings of up to 85 %, 75 %, 75 %, and 76 %, respectively. The operating and equipment costs were estimated based on secondary energy calculations. The kerosene stove had the lowest cost, but CO2 emissions were lowest in the case of using pellet boilers for 30 years. These results clearly show the potential and merit of pellets used in wood biomass boilers as a renewable energy source when compared to fossil fuels. In addition to the CO2 reduction capabilities, the low operating costs of wood biomass heating systems incentivize their use in cold climates.

High-resolution satellite estimates of coal mine methane emissions from local to regional scales in Shanxi, China

Coal mines are significant anthropogenic sources of methane emissions, detectable and traceable from high spatial resolution satellites. Nevertheless, estimating local or regional-scale coal mine methane emission intensities based on high-resolution satellite observations remains challenging. In this study, we devise a novel interpolation algorithm based on high-resolution satellite observations (including Gaofen5-01A/02, Ziyuan-1 02D, PRISMA, GHGSat-C1 to C5, EnMAP, and EMIT) and conduct assessments of annual mean coal mine methane emissions in Shanxi Province, China, one of the world's largest coal-producing regions, spanning the period 2019 to 2023 across various scales: point-source, local, and regional. We use high-resolution satellite observations to perform interpolation-based estimations of methane emissions from three typical coal-mining areas. This approach, known as IPLTSO (Interpolation based on Satellite Observations), provides spatially explicit maps of methane emission intensities in these areas, thereby providing a novel local-scale coal mine methane emission inventory derived from high-resolution top-down observations. For regional-scale estimation and mapping, we utilize high-resolution satellite data to complement and substitute facility-level emission inventories for interpolation (IPLTSO+GCMT, Interpolation based on Satellite Observations and Global Coal Mine Tracker). We evaluate our IPLTSO and IPLTSO+GCMT estimation with emission inventories, top-down methane emission estimates from TROPOMI observations, and TROPOMI's methane concentration enhancements. The results suggest a notable right-skewed distribution of methane emission flux rates from coal mine point sources. Our IPLTSO+GCMT estimates the annual average coal mine methane emission in Shanxi Province from 2019 to 2023 at 8.9 ± 0.5 Tg/yr, marginally surpassing top-down inversion results from TROPOMI (8.5 ± 0.6 Tg/yr in 2019 and 8.6 ± 0.6 Tg/yr in 2020). Furthermore, the spatial patterns of methane emission intensity delineated by IPLTSO+GCMT and IPLTSO closely mirror those observed in TROPOMI's methane enhancements. Our comparative assessment underscores the superior performance and substantial potential of the developed interpolation algorithm based on high-resolution satellite observations for multi-scale estimation of coal mine methane emissions.

Exploring high-emission driving behaviors of heavy-duty diesel vehicles based on engine principles under different road grade levels

To reveal the outstanding high-emission problems that occur when heavy-duty diesel vehicles (HDDV) pass uphill and downhill, this study proposes a method to depict the nitrogen oxides (NOx) and carbon dioxide (CO2) high-emission driving behaviors caused by slopes from the perspective of engine principles. By calculating emission and grade data of HDDV based on on-board diagnostic (OBD) data and digital elevation model (DEM) data, the 262 short trips including uphill, flat-road and downhill are firstly obtained through the rule-based short trip segmentation method, and the significant correlation between the road grade and emissions of the short trips is verified by Kendall's Tau and K-means clustering. Secondly, by comparing the distribution changes of three speed categories (acceleration state, constant speed state and deceleration state), the differences in HDDV operating states under different grade levels are discussed. Finally, the machine learning models (Random Forest, XGBoost and Elastic Net), are used to develop the NOx and CO2 emission estimation model, identifying high-emission driving behaviors, particularly during uphill driving, which showed the highest proportion of high-emission. Explained by the feature importance and SHapley Additive exPlanations (SHAP) model that large accelerator pedal opening, frequent aggressive acceleration, and high engine load have positive effects both on NOx and CO2 emissions. The difference is in the air-fuel ratio that the engine in the rich or slightly lean burning state will increase CO2 emissions and the lean burning state will increase NOx emissions. In addition, due to the uncertainty of the actual uphill, drivers often undergo a rapid “deceleration-uniform-acceleration” process, which significantly contributes to high NOx and CO2 emissions from the engine perspective. The findings provide insights for designing driving strategies in slope scenarios and offer a novel perspective on depicting driving behaviors.

A novel scheme for shore power data to enhance containership-at-berth emission estimation

Ship-at-berth emissions significantly affect air quality and health of human beings in a port and its neighbourhood. However, it is challenging to estimate these emissions precisely due to stringent data requirements. Shore Power (SP) data, including its actual energy consumption and duration, offers useful insights to refine these estimates, but has yet to be fully explored. This study proposes a novel scheme incorporating SP data to improve the accuracy of containership-at-berth emission estimates and evaluate emission reduction measures. The findings reveal substantial differences among existing emission estimates from identical case studies, highlighting the importance of SP data. Additionally, it demonstrates significant emissions from low-load main engines and confirms the efficacy of SP in emission reduction. These findings provide valuable insights into emission estimation methods and their potential applications in estimating emission reduction measures, underlining the importance of policy support in facilitating the SP implementation.

Digital twins for managing bridge climate change adaptation

Background Bridges are vital construction infrastructures that almost every nation needs in order to function. Climate change is a significant issue, that especially affects the construction industry. It is very important that bridges are able to withstand the impacts of climate change and adaptation measures will be required to achieve this. Methods The digital twin will be developed using BIM to manage the climate change adaptation measures for the bridges. A 6D BIM model will be created that includes the 3D Revit model of a bridge featuring climate change measures, the climate change adaptation measures timeline schedule, climate change adaptation cost estimation, and carbon emission estimation, which will be produced using Revit software, Navisworks, and Granta EduPack. The results will show how 6D BIM can be used to support the adaptation of bridges to the effects of climate change. Results The findings underscore the efficacy of 6D BIM in enhancing bridge resilience against climate change impacts. The 3D model demonstrates integration of adaptation measures without compromising bridge functionality. Moreover, the 4D model's timeline scheduling facilitates hazard anticipation, project planning, communication enhancement, collaborative efforts, and project visualization. Cost estimations from the 5D model reveal varying costs among adaptation measures, while the 6D model highlights differences in carbon footprints. These BIM dimensions enable stakeholders to analyze effects on project costs and energy consumption, aiding sustainability and cost-efficiency considerations. Conclusions The study exhibits the literature review analysis, the risk assessment, research on climate change adaptation strategies, and implementation using Revit 2022, Navisworks 2022, and Granta EduPack software. By contributing to the adaptation of bridges to climate change effects, the research has provided valuable insights and practical implications for enhancing bridge resilience globally.

Advancing Volcanic Activity Monitoring: A Near-Real-Time Approach with Remote Sensing Data Fusion for Radiative Power Estimation

The global, near-real-time monitoring of volcano thermal activity has become feasible through thermal infrared sensors on various satellite platforms, which enable accurate estimations of volcanic emissions. Specifically, these sensors facilitate reliable estimation of Volcanic Radiative Power (VRP), representing the heat radiated during volcanic activity. A critical factor influencing VRP estimates is the identification of hotspots in satellite imagery, typically based on intensity. Different satellite sensors employ unique algorithms due to their distinct characteristics. Integrating data from multiple satellite sources, each with different spatial and spectral resolutions, offers a more comprehensive analysis than using individual data sources alone. We introduce an innovative Remote Sensing Data Fusion (RSDF) algorithm, developed within a Cloud Computing environment that provides scalable, on-demand computing resources and services via the internet, to monitor VRP locally using data from various multispectral satellite sensors: the polar-orbiting Moderate Resolution Imaging Spectroradiometer (MODIS), the Sea and Land Surface Temperature Radiometer (SLSTR), and the Visible Infrared Imaging Radiometer Suite (VIIRS), along with the geostationary Spinning Enhanced Visible and InfraRed Imager (SEVIRI). We describe and demonstrate the operation of this algorithm through the analysis of recent eruptive activities at the Etna and Stromboli volcanoes. The RSDF algorithm, leveraging both spatial and intensity features, demonstrates heightened sensitivity in detecting high-temperature volcanic features, thereby improving VRP monitoring compared to conventional pre-processed products available online. The overall accuracy increased significantly, with the omission rate dropping from 75.5% to 3.7% and the false detection rate decreasing from 11.0% to 4.3%. The proposed multi-sensor approach markedly enhances the ability to monitor and analyze volcanic activity.

Improving estimations of GHG emissions and removals from land use change and forests in Brazil

Abstract Brazil ranks fifth in greenhouse gas emissions globally due to land use change. As a signatory to the Paris Agreement, Brazil must periodically report its GHG emissions as well as present mitigation targets set in the Nationally Determined Contribution (NDC). The SEEG Brazil Initiative (Greenhouse Gas Emission and Removal Estimating System) generates independent estimates of GHG emissions and removals since 2013, and in 2020, the estimation method for the land use change sector has been improved. This study aimed to (1) present these methodological advancements, including the spatial allocation of annual emissions and removals due to land use change (LUC) in Brazil at a 30 m spatial scale, and (2) explore the emission and removal patterns observed in Brazil from 1990 to 2019. The method presented here is built upon—but improves—the approach used by Brazil’s official National Inventories to estimate GHG emissions and removals. The improvements presented here include exploring emissions to the municipality level and using an annual updated time series of land use and land cover maps. Estimated greenhouse gas emissions from the LUC sector ranged from 687 Mt of CO2e in 2011 to a peak of 2150 Mt of CO2e in 2003. In 2010, removals nearly offset gross emissions in the sector, with a net emission of 116 Mt of CO2e. The trend observed in recent years was an increase in emissions, decreasing Brazil’s likelihood of meeting its NDC targets. Emission profiles vary across the country, but in every biome, the conversion of primary native vegetation is the predominant transition type. If Brazil managed to curb deforestation, the total GHG emissions from the land use change sector would decrease by 96%, mitigating around 44% of total emissions.