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2,208 results for “emissions”
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>
Dataset for the paper: "Di Felice, L.J.; Ripa, M.; Giampietro, M. Deep Decarbonisation from a Biophysical Perspective: GHG Emissions of a Renewable Electricity Transformation in the EU."
<p>Dataset used for the development of scenarios in the publication "Di Felice, L.J.; Ripa, M.; Giampietro, M. Deep Decarbonisation from a Biophysical Perspective: GHG Emissions of a Renewable Electricity Transformation in the EU. Sustainability 2018, 10, 3685." and used for a case study in "Di Felice L., Dunlop T., Giampietro M., Kovacic Z., Renner A., Ripa M., Velasco-Fernández R. – Report on the Quality Check of the Robustness of the Narrative behind Energy Directives. MAGIC (H2020–GA 689669) Project Deliverable 5.4, 30 November 2018". (link: https://magic-nexus.eu/documents/d54-report-narratives-behind-energy-directives).</p> <p>Sources of other secondary data (from papers, reports) specified in the dataset (under tab "input codes")</p>
Nocturnal Light Emitting Diode Induced Fluorescence (LEDIF): A new technique to measure the chlorophyll a fluorescence emission spectral distribution of plant canopies in situ
<p>This repository contains data reported in the below study:</p> <p>Atherton, J., Liu, W. and Porcar-Castell, A., 2019. Nocturnal Light Emitting Diode Induced Fluorescence (LEDIF): A new technique to measure the chlorophyll a fluorescence emission spectral distribution of plant canopies in situ. <em>Remote Sensing of Environment</em>.</p> <p>Each text file contains the data-set used to produce the relevant figure (see file name). You can find the data to produce A.4. online at https://avaa.tdata.fi/web/smart/smear/ </p> <p>Please pay attention to the following before using this data.</p> <ol> <li><strong>Figure2_lampRadPanel_Wm2srnm.txt</strong>: Note that the shapes are of interest here. The magnitude is not the same as the incident light at top of canopy, as these spectra were measured in a laboratory. See paper section A.1. for more details. </li> <li><strong>Figure3_LEDIFspectra_Wm2srnm.txt</strong>: This data contains the whole observed spectrum including the non-fluorescence regions, which were saturated (warped) in the visible. The fluorescence region is approximately > 650 nm. </li> <li><strong>Figure4_AQYspectra_nm.txt</strong>: As with Figure3 the whole spectrum is included here.</li> <li><strong>FigureA3_repLEDIFspectra_[pmay/psep/usep]._nm.txt</strong>: Data from which the mean spectra (Figure3) were calculated, including the uncorrected red spectra. I have split these by canopy type to avoid name conflicts.</li> </ol> <p> </p>
CO2 emissions, water table and temperature time series from an undrained tropical peatland
<p>Supplement to: Hoyt, A. M., Gandois, L. , Eri, J. , Kai, F. M., Harvey, C. F. and Cobb, A. R. (2019), CO2 emissions from an undrained tropical peatland: Interacting influences of temperature, shading and water table depth. <em>Global Change Biology</em>. https://doi.org/10.1111/gcb.14702</p>
DMS emission dataset (latitudes >45N)
<p># README file for (sub)Arctic DMS emission dataset (latitudes >45N), v1.0.0<br> ===========================================================================</p> <p>Martí Galí Tàpias, 2019-06-12<br> http://orcid.org/0000-0002-5587-1271</p> <p>Address requests for additional data and questions to:<br> marti.gali.tapias@gmail.com</p> <p>This dataset contains satellite-derived time series of:<br> * dmspt (sea-surface dimethylsulfoniopropionate -DMSPt- concentration, in µmol/m3 or equivalent nmol/L)<br> * dms (sea-surface dimethylsulfide -DMS- concentration, in µmol/m3 or equivalent nmol/L)<br> * fdms (sea-air DMS flux, in µmol/m2/d)<br> * ice concentration (0 to 1)</p> <p>The DMSPt dataset was produced as described by:<br> Galí, M., Devred, E., Levasseur, M., Royer, S. J., & Babin, M. (2015). A remote sensing algorithm for planktonic dimethylsulfoniopropionate (DMSP) and an analysis of global patterns. Remote Sensing of Environment, 171, 171-184. https://doi.org/10.1016/j.rse.2015.10.012</p> <p>The DMS dataset was produced as described by:</p> <p>Galí, M., Devred, E., Babin, M., & Levasseur, M. (2019). Decadal increase in Arctic dimethylsulfide emission. Accepted in PNAS. doi:10.1073/pnas.1904378116.</p> <p>Galí, M., Levasseur, M., Devred, E., Simó, R., & Babin, M. (2018). Sea-surface dimethylsulfide (DMS) concentration from satellite data at global and regional scales. Biogeosciences, 15(11), 3497-3519. https://doi.org/10.5194/bg-15-3497-2018</p> <p> </p> <p>Code for the DMSPt_SAT and DMS_SAT algorithms: https://github.com/mgali/DMS-SAT_ALGORITHM</p> <p><br> ## File format</p> <p>MetCDF files are self-describing: they contain one “variable of interest” and all the corresponding temporal and geographic dimensions.</p> <p>## File naming convention</p> <p>variable_sensorYYYY_nD_28km.nc</p> <p>Both the DMSPt and DMS satellite datasets are based on ocean color data obtained from NASA Ocean Color website. Chlorophyll was derived from the GSM algorithm, hence _gsm was appended to the variable names.</p> <p>DMSPt and DMS were produced for 2 satellite sensors.<br> * S = SeaWiFS, years 1998 to 2007<br> * A = MODIS-Aqua, years 2003 to 2016 (the last year is empty from October on)</p> <p>FDMS was calculated using ERA-Interim wind speed and SST, and ice cover from NSIDC. Two different gas exchange parameterizations were used, whose names are appended to the variable names:<br> * W97 (Woolf 1997)<br> * N00GBC (Nightingale et al. 2000 GBC –do not confound with Nightingale et al. 2000 GL)</p> <p>FDMS was also calculated using climatological DMS fields (W97 parameterization) while retaining the interannual variability of all the other variables involved in FDMS calculation:<br> * S19982007 DMS climatology<br> * A20032016 DMS climatology<br> * L11 DMS climatology (interpolation based climatology of Lana et al. 2011 GBC)</p> <p>Ice cover (ice) is also provided for the full period 1998-2016.</p> <p>## Directory naming convention</p> <p>Each directory is named using the names of variable it contains.</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>
Validation of Emission Spectroscopy Gas Temperature Measurements Using a Standard Flame Traceable to the International Temperature Scale of 1990 (ITS-90)
<p>Data underpinning the associated publication (https://doi.org/10.1007/s10765-019-2557-6) on accurate traceable measurement of post-flame temperatures.</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>
PEPT data for Understanding the effect of fluid viscosity in Vertical Stirred Mills using the Positron Emission Particle Tracking (PEPT) approach
<p>The raw PEPT data collected for the paper "Understanding the effect of fluid viscosity in vertical stirred mills using the positron emission particle tracking (PEPT) approach." The paper is the first to use the PEPT technique to investigate the effect of fluid viscosity on the efficiency of the grinding process.</p> <p>This data can be post-processed using the PEPT-ML library and used in isolation or it can be used to calibrate an equivalent simulation. The simulation template is available on GitHub and the link to this is under the Software tab. Each file is labelled by the fluid viscosity and attritor speed used in the experiment, The data for a single run is often split across files but can be combined by the PEPT-ML library.</p>
Dataset from: Correlation between proprioception, functionality, patient-reported knee condition and joint acoustic emissions
<p>Measures of functionality, proprioception, self reported status and joint acoustic emissions (AE) were recorded for a sample of general population. Specifically, threshold to detect passive motion (TTDPM), Knee Osteoarthritis Outcome Scores (KOOS) and 5 times sit-to-stand test (5STS) were collected from 51 participant. Knee AE were recorded using two sensors in different frequency ranges and three modes of AE event detection were investigated during cycling with 30 and 60 rpm cadences.</p>
Residential gasification of solid biomass: Influence of raw material on emissions
<p><strong>Submitted data was used to write an article: </strong>Drobniak, A., Jelonek, Z., Mastalerz, M., Jelonek, I., 2023. Residential gasification of solid biomass: Influence of raw material on emissions. International Journal of Coal Geology 271C, 104247. <a href="https://doi.org/10.1016/j.coal.2023.104247">https://doi.org/10.1016/j.coal.2023.104247</a></p> <p> </p> <p><strong>Funding acknowledgments: </strong>The project is co-financed by the Polish National Agency for Academic Exchange within the Polish Returns Programme (BPN/PPO/2021/1/00005/DEC/1), the National Science Center, Poland (2022/01/1/ST10/00024), funds granted under the Research Excellence Initiative of the<br>University of Silesia in Katowice, Poland and the Green Horizon Program, Poland.</p> <p> </p> <p><strong>Article Abstract: </strong>With interest rising in biomass use, biomass gasification has the potential to become an imperative mechanism to deliver clean conversion of various types of solid biomass to gas. But as biomass gasification attracts growing interest, it is important to focus not only on the technological feasibility but also fully understand its environmental impact to eliminate avoidable air pollution. In this study, we investigated relationships between the composition of 14 types of solid biomass fuels and their gasification emissions in a small-scale residential outdoor setting. Our results show that the amount and type of produced emissions are strongly influenced by the gasified feed. Combining chemical and petrographic analysis proved to be a robust quality assessment method of solid biomass fuels, allowing for quick detection of their contaminants. These impurities can be directly correlated with elevated particulate matter emissions, CO, H2S, HCHO, NH3, SO2, NOx, and respiratory tract irritants. These observations show that quality testing of biomass fuels is critical not only for ensuring their high quality but also for predicting avoidable air pollution during their utilization. Although our data revealed relationships between the type of biomass fuel and gasification emissions, in general, our experiments show that small-scale gasification in a residential setting is a safe technology, and potential hazards can be eliminated by using certified fuels and ensuring appropriate distance from the source of emissions.</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>
[NGC5084] SAUNAS II: Discovery of Cross-shaped X-ray Emission and a Rotating Circumnuclear Disk in the Supermassive S0 Galaxy NGC 5084
<p>The contained FITS files represent the processed Chandra/ACIS X-ray surface brightness maps of the NGC5084 galaxy, observed with Chandra/ACIS and analyzed with the SAUNAS pipeline as described in Borlaff et al. 2024b (https://ui.adsabs.harvard.edu/abs/2024arXiv240810449B/abstract). All the images have the photometric calibrations (in units of photons cm-2 s-1 pixel-1) and have been astrometrically aligned. </p> <div>Each file contains four FITS extensions as detailed below: </div> <div>----</div> <div>EXTENSION NAME TYPE SIZE DETAILS </div> <div>----</div> <div>0 INFO no-data 0 BLANK EXTENSION. <br>1 SB_FLUX float64 512x512 X-RAY SURFACE BRIGHTNESS MAP. [photons cm-2 s-1 pixel-1] <br>2 STD_SB_FLUX float64 512x512 X-RAY SURFACE BRIGHTNESS NOISE MAP [photons cm-2 s-1 pixel-1]<br>3 SNR float64 512x512 SIGNAL-TO-NOISE RATIO [ - ]</div> <div>-----------</div> <div> </div> <div>Use the SNR extension (extension #3) to determine if your source of interest in the SB_FLUX map (extension #1) is statistically significant over the background limit.</div> <div> </div> <div> </div> <div> </div> <div> </div>
Posterior CO emissions
<p>The dataset provides CO emissions from anthropogenic sources resulting from a global inversion of multispectral CO retrieval profiles (V9J) from the Measurements of Pollution in the Troposphere (MOPITT) presented in Gaubert et al., (2024). The analysis is performed using a quantile-conserving ensemble filter framework (QCEFF), specifically with a bounded normal rank histogram (BNRH) distribution for the prior, using the Data Assimilation Research Testbed (DART). The posterior emissions are derived on the global Community Atmosphere Model with Chemistry (CAM-Chem) model grid at the horizontal resolution is 0.9° latitude by 1.25° longitude. The prior emissions are CAMS-GLOB-ANT version 5.3 (Soulié et al., 2024) and the Fire Inventory from NCAR version 2.5 (Wiedinmyer et al., 2023).</p>
Emissions for individual housing in the Western Balkans
<p>Emissions for individual housing in the Western Balkans<br>-----------------------------------------------------------------<br>Version: Open data version 1<br>Date: 2024-10-09<br>Spatial reference system: ETRS89 / ETRS-LAEA (EPSG:3035)<br>Grid resolution: 500x500 m<br>DOI: 10.5281/zenodo.13906810</p> <p>Files<br>-------------------<br>emission_sector-C2_wb6_500m_2019_NOx.tif Gridded emissions for NOx<br>emission_sector-C2_wb6_500m_2019_PM10.tif Gridded emissions for PM10<br>emission_sector-C2_wb6_500m_2019_PM25.tif Gridded emissions for PM2.5<br>emission_sector-C2_wb6_500m_2019_SOx.tif Gridded emissions for SOx<br>readme.txt This readme-file</p> <p>Sector<br>-------------------<br>SNAP: 020200<br>GNFR: C2 (residential stationary combustion)<br>NFR: 1.A.4.b.i (Residential plants)</p> <p>Substances<br>-------------------<br>NOx: Nitrogen oxides as NO2<br>PM10: Particulate matter up to 10 µm size<br>PM2.5: Particulate matter up to 2.5 µm size<br>SOx: Sulphuric oxides (as SO2)</p> <p><br>Years<br>-------------------<br>2019</p> <p><br>Units<br>-------------------<br>ton/year</p> <p>Fileformat<br>-------------------<br>geotiff</p>
Reproduction package for: 'Exploring Waveform Variations among Neutron Star Ray-tracing Codes for Complex Emission Geometries'
<p>Data files, python scripts and notebooks to reproduce the code output comparisons performed in "Exploring Waveform Variations among Neutron Star Ray-tracing Codes for Complex Emission Geometries" by Choudhury et al. (2024; <a href="https://doi.org/10.3847/1538-4357/ad7255" target="_blank" rel="noopener"><em>ApJ</em> <strong>975</strong> 202</a>, <a href="https://doi.org/10.48550/arXiv.2406.07285" target="_blank" rel="noopener">arXiv.2406.07285</a>).</p> <p>Please refer to the README for detailed information.</p> <p>N.B. The neutral hydrogen column density (${\rm N}_{\rm H}$) value is mentioned in the paper to be $0.2 \times 10^{20} {\rm cm}^{-2}$, whereas all the analyses in the paper, as reflected in this Zenodo package, actually uses ${\rm N}_{\rm H} = 2 \times 10^{20} {\rm cm}^{-2}$.</p>
Stellar Evolution Models from "Finding the Fuse: Prospects for the Detection and Characterization of Hydrogen-Rich Core-Collapse 5 Supernova Precursor Emission with the LSST"
<p>These data consist of all runs from the Modules for Experiments in Stellar Astrophysics (MESA; Paxton et al. 2011, 2013, 2015, 2018, 2019) code, used to construct radius priors for modeling supernova precursor emission in<em> <a href="https://arxiv.org/abs/2408.13314">Finding the Fuse: Prospects for the Detection and Characterization of Hydrogen-Rich Core-Collapse 5 Supernova Precursor Emission with the LSST</a></em> (Gagliano+2024, submitted). </p> <p>The contents of the data files are detailed in the file <strong>ReadmeMESA.txt</strong>. Additional detail concerning the simulations can be found in Section 2.2 of the linked publication. </p>
Carbon emission and lifecycle costs supporting digital twins for managing railway maintenance and resilience
<p>The development of railway construction increases the system complexity, which results in difficulty in management with traditional methods. Building Information Modelling (BIM) as an interoperable concept is benefits via whole life-cycle assessment (LCA) of the project, and it has been widely adopted in architecture, construction, and engineering (ACE) fields. This dataset of lifecycle cost and carbon footprint supports the digital twins for managing railway maintenance and resilience.</p>
Greenhouse gas emissions (lifecycle) of each compared vehicle, Tesla 3 (283 HP), and Infiniti Q50 (300 HP).
<p>We compared two vehicles with similar horsepower, Tesla 3 (283 HP), and Infiniti Q50 (300 HP). The CO_2 emissions for these vehicles were: </p> <ul> <li> <p>for the model Tesla 3, CO_2 emissions were 161.8 gCO_2 eq/mile, including 31.8 gCO_2 eq/mile in vehicle production, 25 gCO_2 eq/mile in battery production, and 105 gCO_2 eq/mile in electricity production.</p> </li> <li> <p>for the model Infiniti Q50, CO_2 emissions were 503.8 gCO_2 eq/mile , including vehicle production 40.5 gCO_2 eq/mile, fuel production 91.3 gCO_2 eq/mile, in-service combustion 372 gCO_2 eq/mile.</p> </li> </ul>
Emission factors and chemical composition of particulate matter from residential biomass combustion
<p>Emission factors and chemical composition of particulate matter from residential biomass combustion.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.