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65 results for “emission estimates”
Estimating global ammonia (NH3) emissions based on IASI observations from 2008 to 2018
<p>The dataset ia produced based on the Infrared Atmospheric Sounding Interferometer (IASI) observations in Luo et al.,(2022), by updating the prior ammonia (NH3) emission fluxes with the ratio between biases in simulated NH3 concentrations and effective NH3 lifetimes against the loss of the NHx family (NHx ≡ NH3 + NH4+). We then include sulfur dioxide (SO2) column to correct the NH3 emission trends over India and China, where SO2 emissions have changed rapidly in recent years. Finally, we quantify the uncertainty of NH3 emission by a series of perturbation and sensitivity experiments. The GEOS-Chem simulation driven by top-down estimates has lower bias with the IASI observations than prior emissions, demonstrating the consistency of our estimates with observations.</p>
Monthly methane emissions estimated with the atmospheric inversion model CarbonTracker Europe - CH4
<p>Monthly estimates of global methane emissions from CarbonTracker Europe - CH4 (CTE-CH4). CTE-CH4 is a Bayesian inversion framework based on an ensemble Kalman filter algorithm using the Eulerian global atmospheric transport model TM5. The gridded fluxes are available with a resolution of 1.0x1.0 degrees and in units of kgCH4/m2/month. The gridded flux file contains variables for posterior fluxes from soils (bio_flux_opt) and anthropogenic sources (anth_flux_opt) and the total posterior flux (total_flux_opt). Priors used: Anthropogenic: EDGAR v6, biosphere/wetlands (soils): LPX-Bern DYPTOP v1.4, Ocean: Weber et al. (2019), Biomass burning: GFED v4.1, Termites: VISIT. A more detailed setup of the inversion is documented in Erkkilä, A., Tenkanen, M., Tsuruta, A., Rautiainen, K., and Aalto, T.: Environmental and Seasonal Variability of High Latitude Methane Emissions Based on Earth Observation Data and Atmospheric Inverse Modelling, Remote Sensing, 15, https://doi.org/10.3390/rs15245719, 2023. Note: Fluxes are optimised at 1.0x1.0 degrees in northern high latitudes (USA, Canada, Europe and Russia), but are also provided here at the same resolution for other regions.</p>
Model outputs: Historical (1700–2012) Global Multi-model Estimates of the Fire Emissions from the Fire Modeling Intercomparison Project (FireMIP)
<p>This dataset contains the fire model outputs of emissions for 34 species (elements, compounds, and classes of compounds) as described in the following:</p> <p>Li, F., Val Martin, M., Hantson, S., Andreae, M. O., Arneth, A., Lasslop, G., Yue, C., Bachelet, D., Forrest, M., Kaiser, J. W., Kluzek, E., Liu, X., Melton, J. R., Ward, D. S., Darmenov, A., Hickler, T., Ichoku, C., Magi, B. I., Sitch, S., van der Werf, G. R., Wiedinmyer, C., and Rabin, S.: Historical (1700–2012) Global Multi-model Estimates of the Fire Emissions from the Fire Modeling Intercomparison Project (FireMIP), <em>Atmos. Chem. Phys. Discuss.</em>, https://doi.org/10.5194/acp-2019-37, accepted pending technical corrections, 2019.</p> <p>See Readme for more information.</p>
Estimated individual methane emission rates for oil and gas facilities from the continental United States in 2021
<p>File containing 500 separate estimates of 673,940 individual facility-level methane emission rates for oil and gas facilities for the year 2021 in the continental United States. Each column contains one full estimate of the individual facility-level emissions, presented in units of kilograms per hour of methane per facility. The facility categories included in these estimates are production well sites, gathering and boosting compressor stations, transmission and storage compressor stations, processing plants, and flares. This data can be used to recreate the 500 emission distributions presented in Figure 3 in the following manuscript (link: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1402) which is currently under review. This dataset may be updated as the review stages progress</p>
Output of global termite CH4 emission estimation (unit corrected: g CH4/m2/yr)
<p>NetCDF file: grid map of annual emissions from 1901 to 2021</p>
First estimation of global trends in nocturnal power emissions reveals acceleration of light pollution
<p>The power emitted by different countries at night is based on DMSP and VIIRS data. Inclued also, some extra data from Spain, Portugal, Italy, UK and Greece.</p>
Code and data for "KGML-ag: A Modeling Framework of Knowledge-Guided Machine Learning to Simulate Agroecosystems: A Case Study of Estimating N2O Emission using Data from Mesocosm Experiments "
<p>This is code and data for manuscript: <br> "KGML-ag: A Modeling Framework of Knowledge-Guided Machine Learning to Simulate Agroecosystems: <br> A Case Study of Estimating N<sub>2</sub>O Emission using Data from Mesocosm Experiments"<br> Licheng Liu, Shaoming Xu, Zhenong Jin*, Jinyun Tang, Kaiyu Guan, Timothy J. Griffis, <br> Matt D. Erickson, Alexander L. Frie, Xiaowei Jia, Taegon Kim, Lee T. Miller, Bin Peng, Shaowei Wu, Yufeng Yang, Wang Zhou, Vipin Kumar</p> <p>All the files belong to Prof. Zhenong Jin, University of Minnesota, UA. jinzn@umn.edu<br> "code" foler includes code for data processing, model training, and results plotting.<br> "trained_model_saved" includes all trained model so you can use to reproduce the results showed in the study;<br> "data" includes all data presented in the study. Finetuning data is refering to Miller, L.T. , Griffis, T. J., Erickson, M. D., Turner, P. A., Deventer, M. J., Chen, Z., Yu, Z., Venterea, R.T., Baker, J. M., and Frie, A. L. (2021). Response of nitrous oxide emissions to future changes in precipitation and individual rain events. Journal of Environmental Quality, In review</p>
An exploratory approach to estimate point emission sources
<p>The main purpose of this dataset is to improve the spatial and temporal distribution of the public power, refineries, manufacturing, and construction emissions in the European emission inventories using Portugal as a case study. More information about it can be found in the published article "An exploratory approach to estimate point emission sources" (<a href="https://doi.org/10.1016/j.atmosenv.2023.120026">https://doi.org/10.1016/j.atmosenv.2023.120026</a>). This dataset includes the following folders and files:</p> <p>1. Source_Info</p> <p> 1.1 Sources_Info_NFR_1_A_1_a.xlsx: this excel file includes the public plant names, situation, region, location, start, fuel used, power (MW), technology, treatment of gas effluents, stack height (m), and facility ID.</p> <p> 1.2 Sources_Info_NFR_1_A_1_b.xlsx: this excel file includes the refineries' names, situation, region, location, start, fuel power (MW), stack height (m), and facility ID.</p> <p> 1.3 Sources_Info_NFR_1_A_2.xlsx: this excel file includes the manufacturer's names, situation, region, NFR code, energy balance (PT_code), capital (€), stack height (m), production index code (PT_code), and facility ID.</p> <p>2. Spatial_Location</p> <p> 2.1. NFR_1_A_1_a.gdb: geodatabase with the public power locations and their facility ID (polygons and points shapefiles).</p> <p> 2.2. NFR_1_A_1_b.gdb: geodatabase with the refineries' locations and their facility ID (polygons and points shapefiles).</p> <p> 2.3. NFR_1_A_2.gdb: geodatabase with the manufacturer's locations and their facility ID (polygons and points shapefiles).</p> <p> 2.4. UrbanAreas.gdb: geodatabase with the Portuguese urban areas (polygons shapefiles).</p> <p>3. Temporal_Profiles</p> <p> 3.1. Hourly</p> <p> 3.1.1. Hourly_NFR_1_A_1_a.xlsx: excel file with the hourly profiles of the public power sites in Portugal's mainland (using wood waste, natural gas, hard coal, another thermal, “Central de Ciclo Combinado do Pego”, and “Central Termoeléctrica de Lares”) and Madeira island (using natural gas, fuel, and urban solid waste).</p> <p> 3.1.2. Hourly_NFR_1_A_2_construction.xlsx: excel file with the hourly profiles for construction activities by Portuguese municipalities (305 municipalities).</p> <p> 3.2. Monthly</p> <p> 3.2.1. Monthly_NFR_1_A_1_a.xlsx: excel file with the monthly profiles of the public power sites in the Azores (using fuel oil and diesel).</p> <p> 3.2.2. Monthly_ProductionIndex.xlsx: excel file with the monthly profiles of the Portuguese industries (including information for twenty-five types of activities).</p> <p>4. Emission factors</p> <p>4.1. EF_NFR1_A_1_a.xlsx: excel file with the emission factors for the public power sector.</p> <p>4.2. EF_NFR1_A_2.xlsx: excel file with the emission factors for the refinery activities.</p> <p>4.3. EF_NFR1_A_b.xlsx: excel file with the emission factors for the manufacturing and construction combustion activities.</p> <p>4.4 Fuels_to_EF.xlsx: excel file linking the fuels with available emission factors.</p> <p>5. Data_Sources.docx: this file provides the sources of the dataset.</p>
Estimation of biomass combustion carbon emissions data for 2018 in Africa based on GABAM burned area products.
<p>Estimated biomass combustion carbon emissions data for the African region in 2018, based on the GABAM 30m burned area product.The product is geographically (latitude/longitude) projected with a resolution of 0.00025° (approximately 30 meters) using the WGS84 horizontal datum and the EGM96 vertical datum, and consists of 10° x 10° tiles covering the entire African region.</p>
Estimation of biomass combustion carbon emissions data for 2020 in Africa based on GABAM burned area products.
<p>Estimated biomass combustion carbon emissions data for the African region in 2020, based on the GABAM 30m burned area product.The product is geographically (latitude/longitude) projected with a resolution of 0.00025° (approximately 30 meters) using the WGS84 horizontal datum and the EGM96 vertical datum, and consists of 10° x 10° tiles covering the entire African region.</p>
Estimation of biomass combustion carbon emissions data for 2019 in Africa based on GABAM burned area products.
<p>Estimated biomass combustion carbon emissions data for the African region in 2019, based on the GABAM 30m burned area product.The product is geographically (latitude/longitude) projected with a resolution of 0.00025° (approximately 30 meters) using the WGS84 horizontal datum and the EGM96 vertical datum, and consists of 10° x 10° tiles covering the entire African region.</p>
Daily European biospheric methane emissions estimated with the ecosystem model JSBACH-HIMMELI.
<p>Daily estimates of European biospheric methane emissions from JSBACH-HIMMELI model from year 1990 to year 2023. JSBACH-HIMMELI is an ecosystem process model based on JSBACH land surface model, YASSO soil carbon model and HIMMELI methane emission model. The gridded fluxes are available with a resolution of 0.1x0.1 degrees and in units of mol m-2 s-1 (m-2 refers to grid cell area). The gridded flux file contains a variable for methane fluxes, including a sum of methane fluxes from peatlands, inundated lands and mineral soils. More information of the model set-up is documented in Petrescu, A. M. R., et al., The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom: 1990–2019, Earth Syst. Sci. Data, 15, 1197–1268, https://doi.org/10.5194/essd-15-1197-2023, 2023, Tyystjärvi, V., 2024. Future methane fluxes of peatlands are controlled by management practices and fluctuations in hydrological conditions due to climatic variability. EGUsphere 1–37. https://doi.org/10.5194/egusphere-2023-3037 and Raivonen, M. et al., 2017. HIMMELI v1.0: HelsinkI Model of MEthane buiLd-up and emIssion for peatlands. Geoscientific Model Development 10, 4665–4691. <a href="https://doi.org/10.5194/gmd-10-4665-2017">https://doi.org/10.5194/gmd-10-4665-2017</a></p>
Non-methane volatile organic compound emissions over China estimated using TROPOMI HCHO retrievals
<p>We used the Regional multi-Air Pollutant Assimilation System (RAPAS) with the EnKF algorithm to optimize daily NMVOC emissions in China by assimilating TROPOMI HCHO retrievals. </p><p>airqual.qc.csv includes assimilated and verified surface NO2 observations.</p><p>HCHO.tar.gz includes assimilated TROPOMI HCHO retrievals.</p><p>posterior_emission_27km.nc and posterior_emission_mg_27km.nc includes inferred daily posterior anthropogenic and biogenic NMVOC emissions respectively for August 2022.</p>
Estimation of Air Pollution with Remote Sensing Data: Revealing Greenhouse Gas Emissions from Space
<p><strong>Description</strong></p> <p>This dataset contains remote sensing data from the ESA Copernicus missions Sentinel-2 and Sentinel-5P (tropsopheric NO2 column density) in the 2018-2020 timespan. The satellite measurements each cover ~3100 locations in Europe and ~100 on the US Westcoast, each with a size of 1.2x1.2km. The locations are selected such that each measurement is centered at the location of an air quality measurement station on the ground (from the European Environment Agency or the US Environmental Protection Agency, measuring NO2). This makes it possible to analyze spatiotemporally aligned remote sensing and ground-based measurements.</p> <p> The 13 Sentinel-2 bands are upsampled (bilinear) to 10m resolution and cropped to 120x120 pixel. For some locations multiple Sentinel-2 images are available. The images are stored as binary numpy `.npy` files organized into directories based on their locations. </p> <p>The Sentinel-5P data was pre-processed by mapping the measurements from consecutive satellite overpasses onto a common rectangular grid of 0.05×0.05◦(∼5×5km) across Europe. To harmonize the Sentinel-2 (10m to 60m, upscaled to 10m) and Sentinel-5P (5×3.5km, rescaled to 5×5km) imaging resolutions, the Sentinel-5P data is linearly interpolated to 10m resolution and cropped to 120×120 pixel around the locations of interest. Additionally, all measurements with a QA flag (qa_value) below 75 were discarded, following ESA recommendations. The Sentinel-5P data are stored as `.netcdf` file, organized by location. For each location, three such files are available, containing averaged Sentinel-5P measurements at different temporal frequencies (2018-2020, quarterly, monthly).</p> <p>The <p>samples_{frequency}_{area}.csv</p> files provide a list of observations with the corresponding file paths to a (cloud-free) Sentinel-2 image, the Sentinel-5P measurement, and the average NO2 concentration measurement by the EEA or EPA ground station. These files can be used for easy data-loading.</p> <p><strong>Content</strong></p> <p>The data is organized into the following files:</p> <ul> <li>README.md - this file</li> <li>sentinel-2-eea.tar.gz [33.1GB]</li> <li>sentinel-5p-eea.tar.gz [80.1GB]</li> <li>samples_2018_2020_eea.csv </li> <li>samples_quarterly_eea.csv</li> <li>samples_monthly_eea.csv</li> <li>sentinel-2-epa.tar.gz [0.15GB]</li> <li>sentinel-5p-epa.tar.gz [1.8GB]</li> <li>samples_2018_2020_epa.csv</li> <li>samples_quarterly_epa.csv</li> <li>samples_monthly_epa.csv</li> </ul> <p><strong>Acknowledgement</strong></p> <p>If you use this data set, please cite our publication:</p> <p><em>Scheibenreif, L., Mommert, M., Borth, D., "</em>Estimation of Air Pollution with Remote Sensing Data: Revealing Greenhouse Gas Emissions from Space<em>", Tackling Climate Change with Machine Learning workshop at ICML 2021.</em></p> <p>Please refer to this publication for additional information on the data set.</p> <p>This data set contains modified Copernicus Sentinel data acquired in 2018-2020, processed by ESA.</p> <p> </p> <p><strong>Responsible Author</strong></p> <p>Linus Scheibenreif<br> University of St. Gallen, Institute of Computer Science<br> Chair Artificial Intelligence and Machine Learning<br> linus.scheibenreif ( at ) unisg.ch</p>
Estimating drivers and pathways for hydroelectric reservoir methane emissions using a new mechanistic model (estimated methane emissions for hydropower reservoir surfaces and potential dam emissions)
<p>Methane emissions data from hydropower reservoir surfaces and dams, as estimated with the ResME model. Emissions estimates available for hydropower reservoirs in the GRanD database (Lehner et al., 2011). </p> <p> </p> <p>References:</p> <p>Lehner, B., Liermann, C. Reidy, Revenga, C., Vörösmarty, C., Fekete, B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J.C., Rodel, R., Sindorf, N., and Wisser, D. (2011). High-resolution mapping of the world’s reservoirs and dams for sustainable river-flow management. Frontiers in Ecology and the Environment, 9 (9): 494-502. https://doi.org/10.1890/100125.</p>
Container flows on road, rail and waterways along Rhine-Alpine corridor (Rhine section) at NUTS-2 level with cost-time-emissions estimates and accessibility-frequency-availability of modes
<p>The present dataset is used to estimate the heterogeneous mode choice preferences of shippers, that are presented in the following article :<br> "A Logit Mixture Model Estimating the Heterogeneous Mode Choice Preferences of Shippers Based on Aggregate Data"<br> (Nicolet, A., Negenborn, R. R. & Atasoy, B., A Logit Mixture Model Estimating the Heterogeneous Mode Choice Preferences of Shippers Based on Aggregate Data. IEEE Open Journal of Intelligent Transportation Systems, Vol. 3, 2022, pp. 650-661.)</p>
Network-wide Emissions Estimation Using the Macroscopic Fundamental Diagram
<p>This is the dataset for the NCST project of Network-wide Emissions Estimation Using the Macroscopic Fundamental Diagram.</p> <p>Estimation of emissions and energy consumption using MOVES (Motor Vehicle Emission Simulator) model is cumbersome for large-scale network and intractable for real-time analysis and control purposes. This project aims to develop a simplified method for estimation of emissions and energy consumption in large-scale networks using the macroscopic fundamental diagram (MFD). In the proposed method, the network will be divided up into a number of homogeneous regions and the emissions and energy consumption from all links inside each region will be approximated by MFD-based variables in the MOVES model. Later, the developed method will be used to propose real-time traffic control policies optimizing the network-wide emissions and energy consumption.</p>
Fig. 1 in Microbial Respiration of Organic Carbon in Freshwater Microcosms: The Potential for Improved Estimation of Microbial CO Emission from Organically Enriched Freshwater Ecosystems
Fig. 1. Densities (ordinate) of ciliates (N × 104 L–1) black bars, and densities of bacteria (N × 109 ml–1) grey bars; for Experiment One and Experiment Two with carbon-enriched (E) and control (C) preparations (abscissa).
Dataset for the article 'Estimating countries' additional carbon accountability for closing the mitigation gap based on past and future emissions'.
<p>Dataset for the article 'Estimating countries’ additional carbon accountability for closing the mitigation gap based on past and future emissions', published in Nature Communications. DOI: <a href="https://doi.org/10.1038/s41467-024-54039-x">10.1038/s41467-024-54039-x</a></p> <p>TablesInManuscriptandCalculations.xlsx includes a calculations sheet where the main results can be estimated using only Excel, and each respective table found in the article.</p> <p>PlannedEmissions.xlsx includes estimated pathways for all analyzed countries during 2023-2070. Results are given in million tonnes of carbon dioxide (MtCO₂).</p> <p>DataForSensitivityAnalysis.xlsx is a full database with all the results used in the article, both the main approach and sensitivity cases.</p> <p>These files are generated using R-code available at:</p> <p><a href="https://github.com/morfeldt/AdditionalCarbonAccountability">https://github.com/morfeldt/AdditionalCarbonAccountability</a></p> <p> </p>
Parameters of dielectric properties and emissivity estimated for Earth arid areas.
<p>Dataset gives the parameters estimated from passive microwave observations of the dielectric properties and emissivities of arid areas at 10 to 89 GHz.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.