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17 results for “Negative Emissions”
Netcdf of LCN-PyCCS negative emission potentials as described in Werner et al., 2022 "Potential of land-neutral negative emissions through biochar sequestration"
<p><br> - This data includes three netcdfs providing the negative emission potentials<br> of land- and calorie-neutral Pyrogenic Carbon Capture and Storage (PyCCS) as<br> described in WERNER, C., LUCHT, W., GERTEN, D. & KAMMANN, C. 2022.<br> Potential of Land-Neutral Negative Emissions Through Biochar Sequestration.<br> Earth's Future, 10, e2021EF002583.</p> <p>- the negative emission potential is given in tonnes per grid cell<br> - the data ranges from 2020 to 2099 with a linear ramp up of the approach until 2025<br> - the negative emission potentials are given for each yield increase scenario,<br> 15%, 20% and 30% yield increase<br> - the LPJmL model source code and analysis script can be found in:<br> https://doi.org/10.5281/zenodo.6595002</p>
Bern3D model output related to: Hysteresis of the Earth system under positive and negative CO2 emissions
<p>The data below is output from the Bern3D intermediate complexity model and idealized CO2 increase-decrease simulations used in Jeltsch-Thömmes et al., Environ. Res. Lett. 15 (2020) 124026, https://doi.org/10.1088/1748-9326/abc4af</p> <p><br> The data are provided as .csv and .nc files<br> There are different types of data</p> <p><br> 1) TIMESERIES DATA (Fig. 1 and 2)<br> =================================<br> The name of the files indicates the variable:<br> co2_ts.csv change in atm. co2 [ppm]<br> cumulativeEmissions_ts.csv cumulative emissions [GtC]<br> cumulativeAOflux_ts.csv cumulative atm-ocean C flux [GtC]<br> cumulativeABflux_ts.csv cumulative atm-land C flux [GtC]<br> sat_ts.csv change in surface air temperature [degC]<br> ohc_ts.csv change in ocean heat content [10^24 J]<br> amoc_ts.csv change in Atlantic meridional overturning circulation strength [Sv]<br> seaice_ts.csv fraction of pre-industrial sea-ice area remaining [fraction of PI]<br> <br> The first row in the .csv files contains the header, which indicates the experiment. The naming convention is as follows:<br> c4k#_###</p> <p>c4 indicates the maximum co2 as times pre-industrial (4 times)<br> k# indicates the equilibrium climate sensitivity of the respective simulation in degrees C (k2 to k5)<br> ### indicates the rate of CDR:<br> 010: 0.1% yr^-1<br> 010: 0.3% yr^-1<br> 010: 0.5% yr^-1<br> 010: 0.7% yr^-1<br> 100: 1% yr^-1<br> 200: 2% yr^-1<br> 400: 4% yr^-1<br> 600: 6% yr^-1</p> <p><br> 2) HYSTERESIS DATA (Fig. 3)<br> ===========================<br> The name of the files indicates the variables:<br> cumulativeEmissions_sat.csv cumulative emissions and change in surface air temperature [degC]<br> cumulativeEmissions_OHCsurf.csv cumulative emissions and change in upper ocean heat content (0-700 m) [10^24 J]<br> cumulativeEmissions_o2thermo.csv cumulative emissions and change in thermocline (200-600 m) o2 [mmol m^-3]<br> cumulativeEmissions_OM_arag.csv cumulative emissions and fraction of water in the uppermost 175 m with omegar_aragonite saturation state >3 [fraction]</p> <p>each file contains the time (simulation year) as well as cumulative emissions (cumuEmis) and the respective variable (same naming as in filename) for all the experiments (see timeseries data for naming convention)</p> <p><br> 3) SPATIAL DATA (Fig. 4 and 5)<br> ==============================<br> All data for Fig. 4 and 5 are contained in one single .nc file (fig4_5_data.nc) with a varibale for each map shown in Fig. 4 and 5:<br> c4k2_100_sat hysteresis (down-path minus up-path) in surface air temperature at cumulative emissions of 1000 GtC, ECS=2 degC, in [degC]<br> c4k3_100_sat hysteresis (down-path minus up-path) in surface air temperature at cumulative emissions of 1000 GtC, ECS=3 degC, in [degC]<br> c4k5_100_sat hysteresis (down-path minus up-path) in surface air temperature at cumulative emissions of 1000 GtC, ECS=5 degC, in [degC]<br> <br> c4k2_100_o2thermo hysteresis (down-path minus up-path) in thermocline (200-600 m) o2 at cumulative emissions of 1000 GtC, ECS=2 degC, in [mmol m^-3]<br> c4k3_100_o2thermo hysteresis (down-path minus up-path) in thermocline (200-600 m) o2 at cumulative emissions of 1000 GtC, ECS=3 degC, in [mmol m^-3]<br> c4k5_100_o2thermo hysteresis (down-path minus up-path) in thermocline (200-600 m) o2 at cumulative emissions of 1000 GtC, ECS=5 degC, in [mmol m^-3]<br> <br> c4k3_100_Om_arag_up mean aragonite saturation state of the uppermost 175 m at cumulative emissions of 1000 GtC on the up-path, ECS=3 degC, [unitless]<br> c4k3_100_Om_arag_do mean aragonite saturation state of the uppermost 175 m at cumulative emissions of 1000 GtC on the down-path, ECS=3 degC, [unitless]</p> <p> </p> <p> </p> <p><br> The files can be readily importet in python, for example, by:<br> import pandas as pd<br> import xarray as xr<br> <br> # for the .csv files<br> df = pd.read_csv('path+filename', sep=',', header=0, index_col=None)<br> <br> # for the .nc files<br> ds = xr.open_dataset('path+filename')</p> <p><br> For additional information or in case of questions please contact:<br> Aurich Jeltsch-Thömmes<br> aurich.jeltsch-thoemmes@unibe.ch</p>
Negative emissions technologies and pathways database
<p>This database has been developed to support mathematical analyses of different greenhouse gas removal technologies and their associated deployment implications. The technologies considered in this dataset includes bioenergy with CO2 capture and storage (BECCS), direct air capture and storage (DACCS), afforestation (AF), and enhanced weathering (EW) as they are a representative set of the most commonly reported negative emissions technologies. </p> <p> </p>
Assessing Negative Carbon Dioxide Emissions from the Perspective of a National 'Fair Share' of the Remaining Global Carbon Budget: Supplementary Material
<p>Detailed calculations supporting the results in the published paper, <em>Assessing Negative Carbon Dioxide Emissions from the Perspective of a National 'Fair Share' of the Remaining Global Carbon Budget</em>, <a href="https://link.springer.com/journal/11027">Mitigation and Adaptation Strategies for Global Change</a>, DOI: <a href="https://doi.org/10.1007/s11027-019-09881-6">10.1007/s11027-019-09881-6</a>.</p> <ul> <li><strong>IE-CO2-Quota-2015.ods</strong>: Spreadsheet/workbook in <a href="http://opendocumentformat.org/">Open Document</a> format. Includes table and charts as presented in the paper. Prepared using <a href="http://www.libreoffice.org">LibreOffice</a> (v 5.0+). Should also be accessible also in Microsoft Excel, but some formatting or functionality may be lost.</li> <li><strong>IE-CO2-Quota-2015.ipynb</strong>: Mathematical background and cross-check of detailed calculations in interactive <a href="https://jupyter.org/">Jupyter notebook</a> format (coding in <a href="https://www.python.org/">python</a>).</li> <li><strong>IE-CO2-Quota-2015-ipynb.html</strong>: Static HTML version of the <strong>IE-CO2-Quota-2015.ipynb</strong> suitable for simple viewing/printing.</li> <li><strong>IE-CO2-Quota-2015-ipynb.pdf</strong>: Static version of the <strong>IE-CO2-Quota-2015.ipynb</strong> suitable for simple viewing/printing.</li> </ul>
Raw data set for Negative Emissions in the Chemical Sector: Lifecycle CO2 Accounting for Biomass and CCS Integration into Ethanol, Ammonia, Urea, and Hydrogen Production.
<p>This repository contains the raw data and code used to generate the results in the paper:</p> <p>Tanzer S.E., Blok K., Ramirez Ramirez A. Negative Emissions in the Chemical Sector: Lifecycle CO2 Accounting for Biomass and CCS Integration into Ethanol, Ammonia, Urea, and Hydrogen Production. 15th International Conference on Greenhouse Gas Control Technologies, GHGT-15. 2021. doi: 10.2139/ssrn.3819778.</p> <p>also published as chapter 4 in the PhD dissertation ”Negative Emissions in the Industrial Sector”. The PhD was the department of Engineering Systems and Services, Faculty of Technology Policy, Management at the Delft University of Technology, between 2017-2022. </p> <p>This is intended to be a record of the exact data and code used to generate the results and graphics used in this publication. It is not necessarily designed for user-friendliness or tested to work on other machines and may contain extraneous data and files.</p> <p>To make use of the python black box modelling library for your own work, please check out the most recent public release, which can be found at https://zenodo.org/record/5800104#.YjUTnC8w30o</p>
Dataset used in publication titled " Dependence of climate and carbon cycle response in net zero emission pathways on the magnitude and duration of positive and negative emission pulses"
<p>Essential model data use to produce figures and tables for the publication titled " Dependence of climate and carbon cycle response in net zero emission pathways on the magnitude and duration of positive and negative emission pulses"</p>
Biochar NET-2-U. Negative Emission Technologies are nice to the environment.
<p>Stable Isotope Data of the citizen science project "Biochar NET-2-U".</p>
Biochar NET-2-U-2. Negative Emission Technologies are nice to the environment part two.
<p>Stable Isotope Data of the citizen science project "Biochar NET-2-U-2".</p>
The role of the discount rate for negative emissions under a carbon budget
<p>Data of the figures from the 'The role of the discount rate for negative emissions under a carbon budget'.</p>
Supplementary data - Biochar and its potential to deliver negative emissions and better soil quality in Europe
<p>Additional data from </p> <p>- Matlab script of the logistic transport model for the transportation of forest and crop residues to biochar production plants</p> <p>- Gridded data from the maps in the publication and its supplementary information as geotiff, projection system "ETRS89-extended / LAEA Europe"</p>
Data from: Perceptions of enhanced weathering as a biological negative emissions option
Open the record for dataset details and reuse information.
Study of Zevalin Versus Observation in Participants at Least 60 Years Old With Newly Diagnosed Diffuse Large B-cell Lymphoma in Positron Emission Tomography (PET)-Negative Complete Remission After Rit
ClinicalTrials.gov study NCT01510184. IPD Sharing: NO. Countries: 13. Publications: 0.
Impact of negative and positive CO2 emissions on global warming metrics using an ensemble of Earth system model simulations
<p>The data provided here has been used to create the figures in the paper submitted to Biogeosciences titled <em>Impact of negative and positive CO2 emissions on global warming metrics using an ensemble of Earth system model simulations.</em></p>
[18F]-DPA-714 PET/CT (Positron Emission Tomography/Computerized Tomography) in Triple Negative Breast Cancers
ClinicalTrials.gov study NCT04320030. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Gene expression profiles of the standardized uptake value in 18F-fluorodeoxyglucose positron emission tomography in tripple negative breast cancer
GEO Series GSE135565. Homo sapiens. 84 samples. Type: Expression profiling by array.
Negative Emissions Technologies and Practices Life Cycle Inventories (NEGEM-WP1)
Open the record for dataset details and reuse information.
Dataset related to article: "68Ga-PSMA Positron Emission Tomography/Computerized Tomography for Primary Diagnosis of Prostate Cancer in Men with Contraindications to or Negative Multiparametric Magnetic Resonance Imaging: A Prospective Observational Study"
<p>This record contains raw data related to article 68Ga-PSMA Positron Emission Tomography/Computerized Tomography for Primary Diagnosis of Prostate Cancer in Men with Contraindications to or Negative Multiparametric Magnetic Resonance Imaging: A Prospective Observational Study</p> <p>PURPOSE:</p> <p><sup>68</sup>Ga labeled prostate specific membrane antigen positron emission tomography/computerized tomography may represent the most promising imaging modality to identify and risk stratify prostate cancer in patients with contraindications to or negative multiparametric magnetic resonance imaging.</p> <p>MATERIALS AND METHODS:</p> <p>In this prospective observational study we analyzed <sup>68</sup>Ga labeled prostate specific membrane antigen positron emission tomography/computerized tomography in a select group of patients with persistently elevated prostate specific antigen and/or Prostate Health Index suspicious for prostate cancer, negative digital rectal examination and at least 1 negative biopsy. The cohort comprised men with equivocal multiparametric magnetic resonance imaging (Prostate Imaging-Reporting and Data System, version 2 score of 2 or less), or an absolute or relative contraindication to multiparametric magnetic resonance imaging. Sensitivity, specificity and CIs were calculated compared to histopathology findings. ROC analysis was applied to determine the optimal cutoff values of <sup>68</sup>Ga labeled prostate specific membrane antigen uptake to identify clinically significant prostate cancer (Gleason score 7 or greater).</p> <p>RESULTS:</p> <p>A total of 45 patients with a median age of 64 years were referred for <sup>68</sup>Ga labeled prostate specific membrane antigen positron emission tomography/computerized tomography between January and August 2017. The 25 patients (55.5%) considered to have positive positron emission tomography results underwent software assisted fusion biopsy. We determined the uptake values of regions of interest, including a median maximum standardized uptake value of 5.34 (range 2.25 to 30.41) and a maximum-to-background standardized uptake value ratio of 1.99 (range 1.06 to 14.42). Mean and median uptake values on <sup>68</sup>Ga labeled prostate specific membrane antigen positron emission tomography/computerized tomography (ie the maximum standardized uptake value or the maximum-to-background standardized uptake value ratio) were significantly higher for Gleason score 7 lesions than for Gleason score 6 or benign lesions (p <0.001). On ROC analysis a maximum standardized uptake value of 5.4 and a maximum-to-background standardized uptake value ratio of 2 discriminated clinically relevant prostate cancer with 100% overall sensitivity in each case, and 76% and 88% specificity, respectively.</p> <p>CONCLUSIONS:</p> <p>Our findings support the use of <sup>68</sup>Ga labeled prostate specific membrane antigen positron emission tomography/computerized tomography for primary detection of prostate cancer in a specific subset of men.</p>
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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