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122 results for “Emission modeling”
Modeling Thermal Emission Under Lunar Surface Environmental Conditions
<p>Codes and data required to reproduce results from Prem et al. (2022), Modeling Thermal Emission Under Lunar Surface Environmental Conditions, Planetary Science Journal (https://doi.org/10.3847/PSJ/ac7ced).</p> <p>The file flowchart.pdf contains an overview of the workflow to model ambient and anisothermal thermal emission spectra using the codes contained in codes.zip. The file lab_spectra.xlsx contains the laboratory spectra used in the publication, together with citation information. Please feel free to contact lead author Dr. Parvathy Prem (parvathy.prem@jhuapl.edu) with any questions. </p>
Dataset - modelled CO2 emissions from tropical peat-draining rivers and coastal waters based on enhanced weathering scenarios.
<p>Dataset related to the manuscript "Destabilization of carbon in tropical peatlands by enhanced weathering" (DOI: <a href="https://doi.org/10.1038/s43247-022-00544-0">10.1038/s43247-022-00544-0</a>).</p> <p>Model runs for enhanced leaching of dissolved inorganic carbon (DIC) and of dissolved organic carbon (DOC) were conducted.</p> <p>Results include in-river carbon dioxide (CO2), DIC, DOC, oxygen (O2) and pH as well as CO2 emissions from rivers and from coastal waters.</p> <p>Main results are in "River_And_Coastal_Response_To_Enhanced_Weathering.xlsx".<br> Results for uncertainty study are in "River_And_Coastal_Response_To_Enhanced_Weathering_Uncertainty_Scenarios.xlsx".</p>
Model simulation data used in "An inconsistency in aviation emissions between CMIP5 and CMIP6 and the implications for short-lived species and their radiative forcing" (Thor et al., GMD, 2022)
<p>This archive contains files that were used to produce the results published in the article "An inconsistency in aviation emissions between CMIP5 and CMIP6 and the implications for short-lived species and their radiative forcing" by Thor et al.</p> <p>The directory nml contains namelist setups (configuration files) used for each of the simulations that were performed for this study.<br> The used MESSy version is d2.54.0.3-pre2.55-02-2077-g6eca90858-dirty_6eca90858ecb4ee8fb8900681a94a79ac3d612af_2021-01-26T11:10:08+01:00_2021-02-18T09:14:01+0100 for the QCTM simulations and d2.54.0.3-pre2.55-02-1466-g1fb086944_1fb0869442bf4f1bb10a7dd5f36e4bde5c0cf6d7_2020-10-27T18:17:50+01:00_2020-10-27T18:24:16+0100 for the aerosol simulations (http://www.messy-interface.org).</p> <p>The directory figures contains ipython scripts that were used to produce the figures in the paper.</p>
Dataset for "Effects of two different biogenic emission models on modelled ozone and aerosol concentrations in Europe"
<p>Data for figures in the manuscript "Effects of two different biogenic emission models on modelled ozone and aerosol concentrations in Europe"</p> <p>Jiang, J., Aksoyoglu, S., Ciarelli, G., Oikonomakis, E., El-Haddad, I., Canonaco, F., O'Dowd, C., Ovadnevaite, J., Minguillón, M. C., Baltensperger, U., and Prévôt, A. S. H.: Effects of two different biogenic emission models on modelled ozone and aerosol concentrations in Europe, Atmos. Chem. Phys., 2019.</p> <p> </p> <p> All the data are stored in .mat file, and the variable names are self-explanatory.</p> <p> </p> <p> </p> <p> </p>
Datafiles supporting the article: Impact of methane and other precursor emission reductions on surface ozone in Europe: Scenario analysis using the EMEP MSC-W model
<p>This repository contains the input and output files of the EMEP model related to the article "Impact of methane and other precursor emission reductions on surface ozone in Europe: Scenario analysis using the EMEP MSC-W model" submitted to ACP.</p> <p>The folder Python_scripts includes the .py files used to generate the MAGICC7 model input files and simulation results, as well as the files used to create the figures in both the manuscript and supplementary information. For users, the paths in these scripts will have to be adjusted to point to the data files included in the EMEP_output folder. The latter folder contains the 5-year average scenario simulations as discussed in the main body of the text, as well as the ozone sensitivity to methane concentrations.</p> <p>The EMEP_input folder contains the IIASA scenario emission files for the baseline, 2050 CLE, 2050 MFR, and 2050 LOW scenarios, including separate international shipping files. The regional 0.1 by 0.1 degree emission files are a combination of IIASA files for EECCA countries and EMEP reported emissions scaled by the included 'femis' files. The EMEP model code and reported emissions are available from the open-source release, available here: https://zenodo.org/record/8431553.</p>
Impacts of on-road vehicular emissions on U.S. air quality: A comparison of two mobile emission models (MOVES and FIVE)
<p>Here presented CMAQ outputs used for paper of "MImpacts of on-road vehicular emissions on U.S. air quality: A comparison of two mobile emission models (MOVES and FIVE)"</p>
MOVES-Matrix 3.0: On-Road Energy and Emission Modeling with High-Performance Supercomputing
<p><span>This is the dataset for the NCST project "</span><span>MOVES-Matrix 3.0: On-Road Energy and Emission Modeling with High-Performance Supercomputing</span><span>"</span></p> <p> </p> <p><span>The Georgia Tech research team developed MOVES-Matrix 3.0 based on the EPA's MOVES3 (version 3.1.0) energy use and emission rate model by running MOVES3 thousands of times on the PACE supercomputing cluster across all combinations of input variables and storing the output as lookup tables.<span> </span>MOVES-Matrix 3.0 allows on-road energy consumption and emissions modeling to be conducted more than 800 times faster than running MOVES, while it generates the exact same results, as verified in this report. <span> </span>MOVES-Matrix 3.0 was designed similarly to its predecessor, MOVES-Matrix 2014, but required extensive code modifications to accommodate changes in the MOVES3 environment (including a shift from MySQL to MariaDB and incorporation of new vehicle source sub-types and operating parameters). <span> </span>The review of the fuel and I/M scenarios indicated that MOVES3 now defines 122 modeling regions, as compared with 109 regions in MOVES 2014b (different matrices need to be developed each modeling region).<span> </span>The development of matrices for each modeling region takes approximately 15-20 days on the PACE supercomputing cluster given our assigned resources (compared with only 5-7 days to develop matrices for MOVES 2014). <span> </span>A case study of 3,000 roadway links using Atlanta's matrices confirmed that MOVES-Matrix 3.0 produces the exact same energy consumption and emissions results as MOVES3, but execution modules operate 800 times faster using MOVES-Matrix lookups than running MOVES for any single run.</span></p>
Supplementary data on car company reduction target analysis and GHG emission model
<p>This dataset contains supplementary information for the Master's thesis "<em>Greenhouse gas emission coverage and reduction potentials of scope 3 emissions in car manufacturing"</em> published on 16 November 2022 at Utrecht University. The data consists of two separately files, including the car company reduction target analysis and the GHG emission calculation model that were covered in the thesis report. The supplementary data can be used in combination with the final thesis report.</p>
EAMv2 anthropogenic aerosol emissions data in model-native spectral-element grid
<p>Anthropogenic aerosol emissions data from surface and elevated sources in E3SM Model-native grid. Data available for standard uniform resolution in EAMv2 (ne30pg2) and North America Regionally Refined Model (NA RRM). Data were prepared as a part of the improved emission treatment in E3SMv2 (available at: https://doi.org/10.5281/zenodo.7823633).</p>
Dataset for JGR Atmospheres manuscript " An Observational Constraint of VOC Emissions for Air Quality Modeling Study in the Pearl River Delta Region"
<p>The file includes hourly observations of ground-level ozone (O<sub>3</sub>) and nitrogen dioxide (NO<sub>2</sub>) concentrations at 56 environmental monitoring stations in the Pearl River Delta (PRD) region in China during June 2018. The units are in μg/m<sup>3</sup>.</p>
Model and code for "On the influence of hydroxyl radical and ocean sinks on estimated HCFC and HFC emissions and banks"
<p>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":</p> <p>plotting_code.zip: <br> This directory contains python code that plot all the figures in the paper</p> <p>residence_time_tables.zip: <br> 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)</p> <p>Agage12box_outputs: <br> 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). </p> <p>HCFC-22.tar.gz (similar structure for the other 5 species):<br> 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_'. <br> Folders with prefix 'code_' contain the modified MITgcm code. The AGAGE 12-box atmosphere model is coded in these fortran files. <br> 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. <br> 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/.<br> Note that the plotting_codes can run without installing MITgcm, since the run folders already have all the model outputs.</p> <p>References:<br> 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.<br> Liang, Q., & Rigby, M. (2022). Scientific Assessment of Ozone Depletion: 2022, Chapter 2: Hydrofluorocarbons (No. 278) (p. 509). Geneva: World Meteorological Organization.</p>
Evaluation of models of enteric methane emissions in finishing steers
Open the record for dataset details and reuse information.
AGN and stellar spectra used to produce model molecular emission lines
Open the record for dataset details and reuse information.
GEOS-Chem output for "Lightning NOx Emissions: Reconciling measured and modeled emissions estimates with updated NOx chemistry"
Open the record for dataset details and reuse information.
Data from MECO(n) model simulations on "Urban greenhouse gas emissions from the Berlin area: A case study using airborne CO2 and CH4 in situ observations in summer 2018"
<p>This tar-files contain the results of the MECO(n) model, which are published in</p> <p>T. Klausner, M. Mertens, H. Huntrieser, M. Galkowski, G. Kuhlmann, R. Baumann, A. Fiehn, P. Jöckel, M. Pühl, and A. Roiger: Urban greenhouse gas emissions from the Berlin area: A case study using airborne CO<sub>2</sub> and CH<sub>4</sub> in situ observations in summer 2018, Elementa: Science of the Anthropocene (Ref.: Ms. No. ELEMENTA-D-19-00074R1), 2019.</p>
A model of the subpacket structure of rising tone chorus emissions - wave data
<p>This file contains the wave data obtained from simulation and used in the paper "A model of the subpacket structure of rising tone chorus emissions" submitted to JGR:Space Physics. All data are in Python Numpy binaries. Files hs.npy and ts.npy contain 1D numpy arrays of space and time coordinates of the grid covering the simulation domain. Files mus_full.npy, oms_full.npy, omws_fill.npy and phs_full.npy contain 2D numpy arrays of the refractive index, normalized wave frequency, normalized wave amplitude and wave phase, respectively. File tinds.npy gives the starting and ending points in time of each subpacket, at the spatial coordinate of the source. File input.dat contains a dictionary type data with input parameters used in the simulation.</p>
Model simulation data used in "Attributing ozone and its precursors to land transport emissions in Europe and Germany" (Mertens et al., ACP, 2020)
<p>This dataset contains the output of the MECO(n) regional model simulations analysed and discussed in Mertens et al. (ACP., 2020). An overview of the numerical experiments is given in the article. The README files in the dataset give additional information about the metadata.</p>
Rapid Radiative Transfer Model Output Evaluating Cryospheric Surface Emissivities
<p>Rapid Radiative Transfer Model (RRTM) (Mlawer, et al.,1997) output use to evaluate three cryospheric surface emissivities spectrally across three atmospheric profiles. README file attached provides information regarding output format. </p>
Diagnosing uncertainties in global biomass burning emission inventories and their impact on modeled air pollutants
<p>the data and codes I used to plot figures </p>
Performance and environmental accounting of nutrient cycling models to estimate nitrogen emissions in agriculture and their sensitivity in life cycle assessment
<p>Abstract</p> <p>Purpose</p> <p>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.</p> <p>Methods</p> <p>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.</p> <p>Results and discussion</p> <p>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 N<sub>2</sub>O 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.</p> <p>Conclusions</p> <p>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.</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
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.