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opencc-zeroApr 2022View details →

Data from: High spatiotemporal variability of methane concentrations challenges estimates of emissions across vegetated coastal ecosystems

<div> Coastal methane (CH4) emissions dominate the global ocean CH4 budget and can offset the "blue carbon" storage capacity of vegetated coastal ecosystems. However, current estimates lack systematic, high-resolution, and long-term data from these intrinsically heterogeneous environments, making coastal budgets sensitive to statistical assumptions and uncertainties. Using continuous CH4 concentrations, δ13C-CH4 values, and CH4 sea-air fluxes across four seasons in three globally pervasive coastal habitats, we show that the CH4 distribution is spatially patchy over meter-scales and highly variable in time. Areas with mixed vegetation, macroalgae, and their surrounding sediments exhibited a spatiotemporal variability of surface water CH4 concentrations ranging two orders of magnitude (i.e., 6 – 460 nM CH4) with habitat-specific seasonal and diurnal patterns. We observed (1) δ13C-CH4 signatures that revealed habitat-specific CH4 production and consumption pathways, (2) daily peak concentration events that could change >100% within hours across all habitats, and (3) a high thermal sensitivity of the CH4 distribution signified by apparent activation energies of ∼1 eV that drove seasonal changes. Bootstrapping simulations show that scaling the CH4 distribution from few samples involves large errors, and that ∼50 concentration samples per day are needed to resolve the scale and drivers of the natural variability and improve the certainty of flux calculations by up to 70%. Finally, we identify northern temperate coastal habitats with mixed vegetation and macroalgae as understudied but seasonally relevant atmospheric CH4 sources (i.e., releasing ≥100 μmol CH4 m−2 day−1 in summer). Due to the large spatial and temporal heterogeneity of coastal environments, high-resolution measurements will improve the reliability of CH4 estimates and confine the habitat-specific contribution to regional and global CH4 budgets. </div>

zenodo32/100

Volcanic ash source inversion data for paper "A near-real-time method for estimating volcanic ash emissions using satellite retrievals"

This dataset consists of volcanic ash source inversion data for the paper "A near-real-time method for estimating volcanic ash emissions using satellite retrievals" by Rachel E. Pelley, David J. Thomson, Helen N. Webster, Michael C. Cooke, Alistair J. Manning, Claire S. Witham and Matthew C. Hort, Atmosphere, 2021, 12, 1573, https://doi.org/10.3390/atmos12121573. Satellite retrievals, dispersion model simulations and inversion calculations are included for the eruptions of Eyjafjallajokull in 2010 and Grimsvotn in 2011.

opencc-by-4.0Jun 2019View details →

zenodo32/100

The code of Regional multi-Air Pollutant Assimilation System (RAPAS v1.0) for emission estimates

RAPAS is a Regional multi-Air Pollutant Assimilation System, which can quantitatively optimize gridded source emissions of CO, SO2, NOx, primary PM2.5 and coarse PM10 on regional scale via simultaneously assimilating surface measurements of CO, SO2, NO2, PM2.5 and PM10. These codes correspond to the paper entitled "A Regional multi-Air Pollutant Assimilation System (RAPAS v1.0) for emission estimates: system development and application". 3DVar directory contains original GSI DA codes and extended codes by the author used to optimize CMAQ initial conditions. EnKF directory contains DA codes originally created by the author used to optimize emissions.

opencc-by-4.0Oct 2021View details →

zenodo32/100

Model and code for "On the influence of hydroxyl radical and ocean sinks on estimated HCFC and HFC emissions and banks"

This archive contains the following contents for the manuscript "On the influence of hydroxyl radical and ocean sinks on estimated HCFC and HFC emissions and banks": plotting_code.zip:  This directory contains python code that plot all the figures in the paper residence_time_tables.zip:  This directory contains csv files for each species on their atmospheric residence times under different OH and ocean scenarios discussed in the paper (a list of scenarios is in the header of each file) Agage12box_outputs:  This directory contains AGAGE 12 box model inputs (observed atmospheric mole fraction) and outputs (12-box model simulated atmospheric mole fractions and inferred emissions). This data is used in the WMO (2022) report (Laube & Tegtmeier, 2022; Liang & Rigby, 2022).  HCFC-22.tar.gz (similar structure for the other 5 species): Folders with prefix 'run_' contain the output from the MITgcm ocean and the 12-box coupled model simulation. Different OH and ocean scenarios are followed after the prefix 'run_'.  Folders with prefix 'code_' contain the modified MITgcm code. The AGAGE 12-box atmosphere model is coded in these fortran files.  Folder named 'Input' contains the input emission, atmospheric initial conditions, other lifetime (mainly stratospheric photochemistry), advection and diffusion timescales in the atmosphere, OH concentration, and atmospheric temperature.  These species-specific directories are placed under the folder 'MITgcm/verification/' after installing the MITgcm ocean general circulation model. A detailed instruction on how to install and run the MITgcm can be found at https://mitgcm.readthedocs.io/en/latest/. Note that the plotting_codes can run without installing MITgcm, since the run folders already have all the model outputs. References: Laube, J., & Tegtmeier, S. (2022). Scientific Assessment of Ozone Depletion: 2022, Chapter 1: Update on ozone-depleting substances (ODSs) and other gases of interest to the Montreal Protocol (No. 278) (p. 509). Geneva: World Meteorological Organization. Liang, Q., & Rigby, M. (2022). Scientific Assessment of Ozone Depletion: 2022, Chapter 2: Hydrofluorocarbons (No. 278) (p. 509). Geneva: World Meteorological Organization.

opencc-by-4.0May 2023View details →

ClinicalTrials.gov32/100

A Brain Imaging Study With Positron Emission Tomography and the Radiotracer [11C]UCB-J to Estimate How Fast Brivaracetam and Levetiracetam Enter the Brain in Healthy Volunteers

ClinicalTrials.gov study NCT02602860. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →

publicApr 2022View details →

Data from: High spatiotemporal variability of methane concentrations challenges estimates of emissions across vegetated coastal ecosystems

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publicDec 2025View details →

Data from: Global energy sector methane emissions estimated by using facility-level satellite observations

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publicJun 2017View details →

GEOS-Chem output for "Lightning NOx Emissions: Reconciling measured and modeled emissions estimates with updated NOx chemistry"

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opencc-by-4.0Apr 2020View details →

Profitability, energetics and GHGs emission estimation from rice-based cropping systems in the coastal saline zone of West Bengal, India

This study compares thirteen rice-based cropping systems in the coastal part of West Bengal, India in terms of productivity, profitability, energetics, and emissions. Information on the crop management practices of these systems was collected on 60 farms through a questionnaire survey.

opencc-by-4.0Feb 2021View details →

Performance and environmental accounting of nutrient cycling models to estimate nitrogen emissions in agriculture and their sensitivity in life cycle assessment

Abstract Purpose Several models are available in the literature to estimate agricultural emissions. From life cycle assessment (LCA) perspective, there is no standardized procedure for estimating emissions of nitrogen or other nutrients. This article aims to compare four agricultural models (PEF, SALCA, Daisy and Animo) with different complexity levels and test their suitability and sensitivity in LCA. Methods Required input data, obtained outputs, and main characteristics of the models are presented. Then, the performance of the models was evaluated according to their potential feasibility to be used in estimating nitrogen emissions in LCA using an adapted version of the criteria proposed by the United Nations Framework Convention on Climate Change (UNFCCC), and other relevant studies, to judge their suitability in LCA. Finally, nitrogen emissions from a case study of irrigated maize in Spain were estimated using the selected models and were tested in a full LCA to characterize the impacts. Results and discussion According to the set of criteria, the models scored, from best to worst: Daisy (77%), SALCA (74%), Animo (72%) and PEF (70%), being Daisy the most suitable model to LCA framework. Regarding the case study, the estimated emissions agreed to literature data for the irrigated corn crop in Spain and the Mediterranean, except N2O emissions. The impact characterization showed differences of up to 56% for the most relevant impact categories when considering nitrogen emissions. Additionally, an overview of the models used to estimate nitrogen emissions in LCA studies showed that many models have been used, but not always in a suitable or justified manner. Conclusions Although mechanistic models are more laborious, mainly due to the amount of input data required, this study shows that Daisy could be a suitable model to estimate emissions when fertilizer application is relevant for the environmental study. In addition, and due to LCA urgently needing a solid methodology to estimate nitrogen emissions, mechanistic models such as Daisy could be used to estimate default values for different archetype scenarios.

opencc-by-4.0Jan 2022View details →

Data for the manuscript: Estimating aerosol emission from SPEXone on the NASA PACE mission using an ensemble Kalman Smoother: Observing System Simulation Experiments (OSSEs)

Data for the manuscript: "Estimating aerosol emission from SPEXone on the NASA PACE mission using an ensemble Kalman Smoother: Observing System Simulation Experiments (OSSEs)" at Geoscientific Model Development. The file "Experiments.tar" contains all the ECHAM-HAM simulations. Each experiment has an ensemble mean (ensmean) and an ensemble standard deviation (ensstd) file. The "NatureRuns.tar" contains the Nature Runs used in this study. The file "SPEXone.tar" contains all the SPEXone related data used in this study.

opencc-by-4.0Apr 2024View details →

ExioML: Emission Factor Database for Scope 3 Emission Estimation Machine Learning Benchmarks

<h1>🙋&zwj;♂️ Introduction</h1> ExioML is the first ML-ready benchmark dataset in eco-economic research, designed for global sectoral sustainability analysis. It addresses significant research gaps by leveraging the high-quality, open-source EE-MRIO dataset ExioBase 3.8.2. ExioML covers 163 sectors across 49 regions from 1995 to 2022, overcoming data inaccessibility issues. The dataset includes both factor accounting in tabular format and footprint networks in graph structure. We demonstrate a GHG emission regression task using a factor accounting table, comparing the performance of shallow and deep models. The results show a low Mean Squared Error (MSE), quantifying sectoral GHG emissions in terms of value-added, employment, and energy consumption, validating the dataset's usability. The footprint network in ExioML, inherent in the multi-dimensional MRIO framework, enables tracking resource flow between international sectors. ExioML offers promising research opportunities, such as predicting embodied emissions through international trade, estimating regional sustainability transitions, and analyzing the topological changes in global trading networks over time. It reduces barriers and intensive data pre-processing for ML researchers, facilitates the integration of ML and eco-economic research, and provides new perspectives for sound climate policy and global sustainable development. <h1>📊 Dataset</h1> ExioML supports graph and tabular structure learning algorithms through the Footprint Network and Factor Accounting table. The dataset includes the following factors in PxP and IxI: - Region (Categorical feature) - Sector (Categorical feature) - Value Added [M.EUR] (Numerical feature) - Employment [1000 p.] (Numerical feature) - GHG emissions [kg CO2 eq.] (Numerical feature) - Energy Carrier Net Total [TJ] (Numerical feature) - Year (Numerical feature) <h2>☁️ Factor Accounting</h2> The Factor Accounting table shares common features with the Footprint Network and summarizes the total heterogeneous characteristics of various sectors. <h2>🚞 Footprint Network</h2> The Footprint Network models the high-dimensional global trading network, capturing its economic, social, and environmental impacts. This network is structured as a directed graph, where directionality represents sectoral input-output relationships, delineating sectors by their roles as sources (exporting) and targets (importing). The basic element in the ExioML Footprint Network is international trade across different sectors with features such as value-added, emission amount, and energy input. The Footprint Network helps identify critical sectors and paths for sustainability management and optimization. The Footprint Network is hosted on Zenodo. <h1>🔗 Code and Data Availability</h1> The ExioML development toolkit in Python and the regression model used for validation are available on the GitHub repository: (https://github.com/YVNMINC/ExioML). The complete ExioML dataset is hosted by Zenodo: (https://zenodo.org/records/10604610). <h1>💡 Additional Information</h1> More details about the dataset are available in our paper: *ExioML: Eco-economic dataset for Machine Learning in Global Sectoral Sustainability*, accepted by the ICLR 2024 Climate Change AI workshop: (https://arxiv.org/abs/2406.09046). <h1>📄 Citation</h1> <pre>@article{guo2024exioml, title={ExioML: Eco-economic dataset for Machine Learning in Global Sectoral Sustainability}, author={Guo, Yanming and Guan, Charles and Ma, Jin}, journal={arXiv preprint arXiv:2406.09046}, year={2024} }</pre> <h1>🌟 Reference</h1> Stadler, Konstantin, et al. "EXIOBASE 3." Zenodo. Retrieved March 22 (2021): 2023.

opencc-by-4.0Jul 2024View details →

CO2 emissions estimate from Mexico City using ground and space-based remote sensing

This dataset comprises inversion results for Mexico City using seven FTIR and OCO-3 observations.

opencc-by-4.0Dec 2020View details →

Constraining Emission Estimates of CFC-11 in Eastern China Based on Local Observations at Surface Stations and Mount Tai

Data in the manuscript

dryad28/100

Estimation of methane emissions from the U.S. ammonia fertilizer industry using a mobile sensing approach (supplemental data)

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publicApr 2019View details →

CMS: CO2 Signals Estimated for Fossil Fuel Emissions and Biosphere Flux, California

This data set provides estimated CO2 emission signals for 16 regions (air quality basins) in California, USA, during the individual months of November 2010 and May 2011. The CO2 signals were predicted from simulated atmospheric CO2 observations and modeled fossil fuel emissions and biosphere CO2 fluxes. Data is also provided for the land surface in the larger modeling domain outside California. CO2 signals refer to the local enhancement or depletion in atmospheric CO2 concentration caused by fossil fuel emissions or biospheric exchange occurring within the region.

CMS: Annual Estimates of Global Riverine Nitrous Oxide Emissions, 1900-2016

This dataset provides modeled estimates of annual nitrous oxide (N2O) emissions at a coarse geographic scale (0.5 x 0.5 degree) for two sets of global rivers and streams covering the period of 1900-2016. Emissions (g N2O-N/yr) are provided for higher-order rivers and streams (>=4th order) and headwater streams (<4th order). The estimates were derived from a water transport model, the Model for Scale Adaptive River Transport (MOSART), coupled with the Dynamic Land Ecosystem Model (DLEM) to link hydrology and ecosystem processes pertaining to N2O flux and transport. Factors driving the model included climate, land use and land cover, and nitrogen inputs (i.e., fertilizer, deposition, manure, and sewage). Nitrogen discharges from streams and rivers to the ocean were calibrated from observations from 50 river basins across the globe.

BDSNP Module for Improved Soil NO Emission Estimates for CMAQ Model, Conterminous USA

This model product provides: (1) the source code for the updated Berkeley-Dalhousie Soil Nitric Oxide (NO) Parameterization module (BDSNP, Version 1.0) as implemented with the Community Multi-scale Air Quality model (CMAQ, Version 5.0.2), (2) module input data from historical and new sources of maps for soil biome type, fertilizer, and arid and non-arid climates, and (3) sample CMAQ simulation outputs for three BDSNP module NO parameterizations (standard, historical, and newer inputs). The simulations use a 12-km spatial grid resolution for CMAQ modeling covering the conterminous United States for July 2011.

SAFARI 2000 1-Degree Estimates of Burned Biomass, Area, and Emissions, 2000

A new method is used to generate spatial estimates of monthly averaged biomass burned area and spatial and temporal estimates of trace gas and aerosol emissions from open fires in southern Africa.Global burned area data for the year 2000 (GBA2000) supplemented with the Along Track Scanning Radiometer (ATSR) fire count data are employed to quantify the area burned at 1-km resolution by using a fractional vegetation cover map derived from satellite observations. To account for the spatial heterogeneity in main types of vegetation within each 1-km grid cell, fuel load maps were developed from biomass density data sets for herbaceous and tree covered land together with fractional tree and vegetation cover maps. To represent the fire characteristics, land cover is classified into forests, woodlands, and grasslands, based on satellite-derived data sets for fractional tree cover. Combustion factors for each land cover type takes into account fuel composition, tree cover density, and the dryness of herbaceous vegetation. In addition, the emission factors (EF) are calculated; EF for grassland fires represent spatial and seasonal variations in fuel conditions using a modified combustion efficiency calculation. This data set contains spatial and temporal estimates of trace gas and aerosol emissions from open fires in southern Africa for the following variables:acetic acid (CH3COOH); methanol (CH3OH); methane (CH4); carbon dioxide (CO2); carbon monoxide (CO); formaldehyde (HCHO); non-methane hydrocarbons (NMHC); and particulate matter less than 2.5 um in diameter (PM2.5). Annual amounts of total area burned and CO2 emissions for southern Africa are compared to those from other authors.

SAFARI 2000 Estimated BVOC Emissions for Southern African Land Cover Types

Improved vegetation distribution and emission data for Africa south of the equator were developed for the Southern African Regional Science Initiative (SAFARI 2000) and combined with biogenic volatile organic compound (BVOC) emission measurements to estimate BVOC emissions for the southern African region. BVOC emissions were estimated for southern Africa on a monthly basis over a one-year period by combining GIS layers of vegetation, LAI, and climate with a biogenic emissions model, GLOBEIS (Guenther et al, 1993; Guenther, 1999). Model input data included: vegetation data (Rutherford et al., 2000); species emission capacity data (Greenberg et al., 2003; Guenther et al., 1996; Harley et al., 2003; Klinger et al., 1998; Otter et al., 2002; Serca et al., 2001; Wiedinmyer et al., 2004); LAI data (1987-88 ISLSCP LAI; Sellers et al., 1994); cloud cover (MODIS LAI cloud mask); and temperature data (NOAA NCDC data).Model output includes emissions estimates for isoprene, light-dependent monoterpene, stored monoterpene, and other volatile organic compounds by land cover category and by vegetation type (g C m-2 mo-1). Emissions were modeled for a summer (January) and a winter (July) month in 2001. Monthly and annual total emissions per constituent for the year 2001 were also calculated. The data files containing the model outputs are ASCII comma-delimited files. Graphics (.jpgs) included with this data set show the distribution of light dependent monoterpene emissions across southern Africa during January, the average monthly isoprene emissions over southern Africa in January and in July, and the average monthly stored monoterpene emissions over southern Africa in January and in July.