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122 results for “Emission Modelling”
Dynamic inferential NOx emission prediction model with delay estimation for SCR de-NOx process in coal-fired power plants
<p><span><span>The selective catalytic reduction (SCR) de</span><span>-</span><span>NO<sub>x</sub> </span><span>process in coal-fired power plants not only displays nonlinearity, large inertia, and time variation but also a lag in NO<sub>x</sub> analysis; </span><span>hence,</span><span> it is difficult to obtain an accurate model </span><span>that </span><span>can be used to control NH<sub>3</sub> injection </span><span>during changes in the </span><span>operating state. </span><span>In this work,</span><span> a novel dynamic inferential model with delay estimation was proposed for NO<sub>x</sub> emission prediction. First, k-nearest neighbour mutual information (knnMI) was used to estimate the time-delay of the descriptor variables, followed by reconstruction of the phase space of the model data. Second, multi-scale wavelet kernel partial least square (mwKPLS) was</span><span> used</span><span> to improve the prediction ability, </span><span>and this was followed by verification using </span><span>benchmark dataset experiments. Finally, the delay-time difference (DTD) method and feedback correction strategy </span><span>were </span><span>proposed to deal with the time variation of the SCR de</span><span>-</span><span>NO<sub>x</sub> process.</span> <span>Through the analysis of the </span><span>experimental field data </span><span>in the</span> <span>steady state, </span><span>the variable</span><span> state and </span><span>the </span>NO<sub>x</sub> analyser blowback process<span>, the results proved that</span><span> this dynamic model has </span><span>high prediction accuracy</span><span> during</span><span> state changes and can </span><span>realize</span><span> advance prediction of the NO<sub>x</sub> emission. </span></span></p>
Scenario data, model source code and plotting routine for manuscript: Separating CO2 emission from removal targets comes with limited cost impacts
<p>This data archive contains REMIND model setup, results data and data analysis files for manuscript:<br><strong>Separating CO2 emission reduction from removal targets comes with limited cost impact.<br><br>plotting</strong>(directory) contains results data, manuscript specific data analysis and plotting routine scripts used to generate the figures of the manuscript.<br><strong>remind</strong>(directory) contains REMIND model source code and scenario set-up. Detailed scenario configurations are set in remind/config/scenario_config_SepMark.csv.<br><strong>remind2</strong>(directory) contains the slightly modified R-library package used for post-processing of REMIND output.<br><br>AMENDMENT<br><strong>Plots_SeparateMarkets_afterReviewProcess.Rmd</strong> After the review process, the new plotting script was added including the additional figures in the Supplementary Material. This file should replace the previous R-markdown file SepMark_essential/plotting/Plots_SeparateMarkets.Rmd.</p>
Mapping and modelling global mobility infrastructure stocks, material flows and their embodied greenhouse gas emissions - Data
<p>Dynamics of societal material stocks such as buildings and infrastructures and their spatial patterns drive surging resource use and emissions. Building up and maintaining stocks requires large amounts of resources; currently stock-building materials amount to almost 60% of all materials used by humanity. Buildings, infrastructures and machinery shape social practices of production and consumption, thereby creating path dependencies for future resource use. They constitute the physical basis of the spatial organization of most socio-economic activities, for example as mobility networks, urbanization and settlement patterns and various other infrastructures. </p><p>The data in this repository show the material stocks contained in global mobility infrastructure networks at the country-level and mapped at 5arcmins, as well as country-level estimates of material flows for maintenance, replacement and expansion of those infrastructures, and the associated GHG emissions from materials production. This repository contains all data as shown in figures of the article, including the GeoTIFF files for figure 3, and the supplementary data file containing full country-level results.</p><p><strong>Data</strong><br>This dataset includes the following data:</p><ul><li>Global maps of material stocks in mobility infrastructure networks at 5 arcmins, separate for all roads, all rail-based infrastructure, as well as in total and per capita</li><li>Global country-level material stock estimates for mobility infrastructures</li><li>Global country-level estimates of material flows and associated GHG emissions for materials production</li><li>Material intensity in mass per area of road (kg/m²) per road type</li><li>Material intensity in mass per area of railway track (kg/m²) per railway type</li><li>Material intensity in mass per area (kg/m²) per bridges and tunnels</li></ul><p>Material intensity factors are available for iron and steel, concrete, asphalt, aggregate (sand & gravel), timber, and other.</p><p><strong>Further information</strong><br>This dataset complements the following scientific article:</p><p>Wiedenhofer, Dominik, André Baumgart, Sarah Matej, Doris Virág, Gerald Kalt, Maud Lanau, Danielle Densley Tingley, u. a. "Mapping and Modelling Global Mobility Infrastructure Stocks, Material Flows and Their Embodied Greenhouse Gas Emissions". <i>Journal of Cleaner Production</i>, November 2023, 139742. <a href="https://doi.org/10.1016/j.jclepro.2023.139742">https://doi.org/10.1016/j.jclepro.2023.139742</a>.</p><p>For further information please see the publication. You can also contact Dominik Wiedenhofer <a href="mailto:dominik.wiedenhofer@boku.ac.at">dominik.wiedenhofer(a)boku.ac.at</a> and visit our <a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">website</a> to learn more about our project: <i>MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.</i></p><p><strong>Funding</strong><br>This research was funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). </p>
Model version, input data, results, and processing scripts for the Speizer et al. zero emissions transport paper
<p>Includes the files needed to run the GCAM scenarios, analyze the outputs, and produce the figures for the Speizer et al. zero emissions transport paper.</p>
Data for simultaneous inference of sea ice state and surface emissivity model using machine learning and data assimilation
<h2>Overview</h2> <p>This dataset supports the draft manuscript "Simultaneous inference of sea ice state and surface emissivity model using machine learning and data assimilation" which describes a way to infer the daily maps of the sea ice concentration and empirical properties of the sea ice (relating to its snow cover and its physical properties, such as air inclusions) along with the creation of a new empirical model for the sea ice surface emissivity. This is done using knowledge of the atmosphere state, skin temperature and ocean water emissivity from the European Centre for Medium-range Weather Forecasts (ECMWF) weather forecasting model and the observed radiances at microwave frequencies from the Advanced Microwave Scanning Radiometer 2 (AMSR2). The inverse modelling and state estimation is achieved by combining empirical machine learning elements in a Bayesian-inspired network along with a number of physical components. The work also introduces the idea of an "empirical state", in this case describing the aspects of the sea ice physical state which affect the observations, and which is defined by the inputs to the new empirical model component (in machine learning terms, it is defined by the latent input state of a neural network). This dataset includes the data used in training the model and inferring the sea ice parameters, as well as the outputs from that training process. The software used to perform the training is in Python and uses the Keras and Tensorflow software. See the draft manuscript for full details of this data.</p> <p>The code used in the draft manuscript is archived at <a href="https://doi.org/10.5281/zenodo.10013542">https://doi.org/10.5281/zenodo.10013542</a></p> <p>The data used in the draft manuscript is archived at <a href="https://doi.org/10.5281/zenodo.10033377">https://doi.org/10.5281/zenodo.10033377</a></p> <h2>Training data </h2> <h3>Observation space training and ancillary data</h3> <p>Training is done at the location of AMSR2 superobservations (superobs) over ocean with less than 1% land contamination and polewards of 45 degrees latitude, between 1st July 2020 and 30th June 2021. There are 64,184,021 superobs used. A superob is the average of all raw JAXA level 1B observations from one orbit falling into a grid box on an approximately constant area (reduced Gaussian) grid at approximately 40 km by 40 km resolution (noting that polar regions can thus have up to around 7 superobs per day). The superobs have been computed using the field of view central locations for each channel as derived from the JAXA level 1B data. A subset of 10 of the AMSR2 channels is used, from 10 GHz, V polarised, to 89 GHz, H polarised.</p> <p>At each superob location, the relevant fields from the ECMWF 12 hour 'background' forecast are interpolated to the observation time and location. The atmosphere is represented indirectly by the relevant radiative transfer terms from a scattering radiative transfer model. The sea ice concentration from the ECMWF OCEAN5 analysis is included as a validation reference but is not used in the training itself, except to provide a monthly mean first guess to speed up the training. Each field is provided in a separate netCDF file:</p> <ul> <li>field_v2_JULIAN_DAY.nc - superob time in days since 12 UTC on Nov 24th 4714 BC on the proleptic Gregorian calendar</li> <li>field_v2_LAT.nc - superob central latitude in degrees</li> <li>field_v2_LON.nc - superob central longitude in degrees</li> <li>field_v2_IGRID.nc - corresponding grid number on the map grid used in this work (see below)</li> <li>field_v2_OBSVALUE.nc - observed superob brightness temperature at each of 10 AMSR2 channels.</li> <li>field_v2_TSFC.nc - skin temperature computed by the ECMWF forecast model</li> <li>field_v2_WINDSPEED10M.nc - 10m wind speed computed by the ECMWF forecast model</li> <li>field_v2_EMIS_WATER.nc - Ocean water surface emissivity at 10 AMSR2 channels, simulated from the ECMWF forecast fields using the FASTEM-6 model</li> <li>field_v2_CLOUD_FRACTION.nc - Effective cloud fraction used in the atmospheric radiative transfer model at each of 10 AMSR2 channels</li> <li>field_v2_TAUSFC_CLD.nc - Surface to space transmittance in the cloudy column at each of 10 AMSR2 channels</li> <li>field_v2_TUP_CLD.nc - Upwelling brightness temperature from the atmosphere in the cloudy column at each of 10 AMSR2 channels</li> <li>field_v2_TDOWN_CLD.nc - Downwelling brightness temperature from the atmosphere in the cloudy column at each of 10 AMSR2 channels</li> <li>field_v2_TAUSFC.nc - Equivalently for the clear column</li> <li>field_v2_TUP.nc - Equivalently for the clear column</li> <li>field_v2_TDOWN.nc - Equivalently for the clear column</li> <li>field_v2_SEAICE.nc - Sea ice concentration from the ECMWF OCEAN5 analysis, for validation only (not used in training)</li> </ul> <h3>Grid space data: initial data for training; validation sea ice data</h3> <p>A number of properties are provided to the hybrid physical-empirical model that is being trained, on a special map grid defined in this project, including all 62,499 of the reduced Gaussian 40km grid points that have at least one superob at some point during the year of training data. These are:</p> <ul> <li>ifs_seaice_initials_year.nc - sea ice concentration from OCEAN5, monthly averaged on the grid, and then provided on all days of the relevant month as initial conditions (technically, first guess) for the training. This includes an additional day before the beginning of the training, used for time-lagging (see draft paper).</li> <li>ifs_tsfc_year_dailyx.nc - skin temperature from ECMWF forecast fields at observation locations, averaged onto the daily grid, to help provide constraints on the likelihood of sea ice as part of a sea ice loss function.</li> </ul> <p>For diagnostic and validation purposes, the ECMWF OCEAN5 analysis is also provided on the grid:</p> <ul> <li>ifs_seaice_year.nc - sea ice concentration from OCEAN5 at observation locations, averaged onto the daily grid</li> </ul> <p>All these fields are provided on the following dimensions:</p> <ul> <li>LON - the longitude of the grid point in degrees</li> <li>DAY - the day through the training year (0-364, 1st July 2020 to 30th June 2021) or through the training year extended forward by one day (30th June 2020) for the sea ice (0-365). In practice the days are offset by 3 hours from the UTC day to match the ECMWF data assimilation windows, which start at 21 UTC the day before.</li> </ul> <p>The latitude is also provided</p> <ul> <li>LAT - the latitude of the grid point in degrees</li> </ul> <p>Note that the observation location IGRID is on the custom grid of the ML model that is defined implicitly in these gridded files. The LON and LAT vectors in these files are the longitude and latitude points of the grid and are of 62499 in length. The IGRID number for an observation is the index into these arrays from 0-62498.</p> <h2>Outputs from training</h2> <p>The following files are the output and diagnostics from the year-long training. The python code and the draft paper are the primary documentation for these:</p> <ul> <li>models_year.nc - settings of the model are recorded here, along with the trained values of the smaller empirical components/layers within the hybrid model. For example, the layer weights of the wind speed bias correction, the observation space bias correction, and the empirical surface emissivity model are recorded here. The values of the loss function at each epoch are also recorded here.</li> <li>properties_year.nc - trained values of each of 3 empirical properties of sea ice on the map grid (3 properties by 62499 locations by 365 days from 1st July 2020)</li> <li>seaice_year.nc - inferred values of sea ice fraction on the map grid (62499 locations by 365 days from 1st July 2020, discarding the additional day at the start)</li> <li>tbsim_year.nc - simulated AMSR2 brightness temperatures from the trained network</li> <li>tbsim_initial_year.nc - simulated AMSR2 brightness temperatures using the untrained network</li> </ul> <p>The longitude and latitude of the map grid is found in any of the initial data files described in the previous section. The days are 0-364 corresponding to 1st July 2020 to 30th June 2021.</p> <h3>Sea ice surface emissivity at grid locations</h3> <p>A packaged version of the sea ice surface emissivity is provided at grid locations, alongside the surface emissivity model, the sea ice concentration and the four inputs to the model, i.e. the normalised skin temperature and the three empirical variables:</p> <ul> <li>emissivity_grid_year.nc</li> </ul> <p>Note that in the training, the surface emissivity is computed at observation locations and has not been stored due to memory limitations. For easier comparison to other datasets, the surface emissivity has been recomputed on grid locations in this package, using the year-long trained emissivity model and its trained inputs. The sea ice surface emissivity is only physically meaningful for sea ice concentrations above around 0.25. Also be aware of the "hole at the pole" which is the small region of the Arctic ocean that is sometimes not covered by an AMSR2 overpass, and which is found from 88 degrees N. On days where the hole or part of the hole exists, the sea ice emissivity on the grid is not valid at these locations. These locations can be identified by having all values of the empirical properties zero (because the empirical properties were never constrained by any observations on that day, and remain at their initial values before training).</p> <h2>Sensitivity tests</h2> <p>Extensive sensitivity tests were carried out, as described in the appendices of the draft paper and as documented in the Python code, using the month of August 2020 as an example. These required equivalent month-long training and initial data similar to those described above, but all observation space fields are contained within the same file in this case. Output files follow similar principles to those described above. The full package is provided as a tar file:</p> <ul> <li>sensitivity.tar</li> </ul> <p>This contains the training and initial files:</p> <ul> <li>amsr2_v2_202008.nc</li> <li>ifs_tsfc_dailyx_202008.nc</li> <li>ifs_seaice_202008.nc</li> </ul> <p>as well as directories containing the trained model outputs and diagnostics at each of the sensitivity tests, using the same formats as described for the yearly training, with these names:</p> <ul> <li>nprop - number of empirical properties</li> <li>epoch - number of epochs</li> <li>deep - configuration of the empirical sea ice emissivity model, including multiple layers of nonlinear dense neural network</li> <li>bseaice - background error for the sea ice physical bounds background error (loss) term</li> <li>bemis - background error for the sea ice emissivity background error (loss) term</li> <li>bbias - background error for the bias correction background error (loss) term</li> <li>batchsize - batch size used in training</li> <li>bbatchsize - extended epochs testing of batch size used in training</li> </ul> <h2>Licensing</h2> <p>This data product is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). To view a copy of this licence, visit <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a></p> <p>You are free to:</p> <ul> <li>Share — copy and redistribute the material in any medium or format</li> <li>Adapt — remix, transform, and build upon the material for any purpose, even commercially.</li> </ul> <p>Under the following terms:</p> <ul> <li>You must give appropriate credit (attribution) to ECMWF as outlined below, provide a link to the licence, and indicate if changes were made.</li> <li>No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the licence permits.</li> </ul> <p>The following wording shall be attached to the use of this ECMWF data product: </p> <ol> <li>Copyright statement: Copyright "© 2023 European Centre for Medium-Range Weather Forecasts (ECMWF)".</li> <li>Source <a href="http://www.ecmwf.int/">www.ecmwf.int </a>and <a href="https://doi.org/10.5281/zenodo.10009497">https://doi.org/10.5281/zenodo.10009497</a></li> <li>Licence Statement: This data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a></li> <li>Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.</li> <li>Where applicable, an indication if the material has been modified and an indication of previous modifications.</li> <li>DOI: 10.5281/zenodo.10009498</li> </ol> <p>Original data for this value-added product was provided by Japan Aerospace Exploitation Agency (JAXA). Specifically, this dataset builds on the Advanced Microwave Scanning Radiometer 2 (AMSR2) level 1B data available from the JAXA G-Portal, https://gportal.jaxa.jp/gpr/, which has the following attribution and licensing:</p> <ol> <li>Give credit for the original data to JAXA, i.e. "Original data for this value added data product was provided by Japan Aerospace Exploration Agency"</li> <li>DOI for original JAXA data is L1B-Brightness temperature (TB) GCOM-W/AMSR2 L1B Brightness Temperature: <a href="https://doi.org/10.57746/EO.01gs73ans548qghaknzdjyxd2h">https://doi.org/10.57746/EO.01gs73ans548qghaknzdjyxd2h</a></li> <li>Original terms of data service from JAXA, with highlighted extracts: <ul> <li><a href="https://gportal.jaxa.jp/gpr/index/eula?lang=en">https://gportal.jaxa.jp/gpr/index/eula</a> <ol> <li>The user is entitled to use G-Portal data free of charge without any restrictions (including commercial use) except for the condition about acknowledgement of data credit as stipulated in Article 7.(2). (see above)</li> <li>JAXA is collecting results (papers, theses, reports, etc.) using G-Portal data. If you have any results using G-Portal data, please mail/e-mail a copy of the result to G-Portal Support Desk (Contact Information written at the end of the Terms of Use). We appreciate your cooperation very much.</li> </ol> </li> </ul> </li> </ol>
Global anthropogenic NOx emissions from 2019 to 2022 based on satellite NO2 observations and GEOS-Chem model
<p><a href="../api/records/10947114/draft/files/emissions.nc/content" target="_blank" rel="noopener noreferrer">emissions.nc</a><a href="10052904"> includes the monthly data of global anthropogenic nox emissions from 2019 to 2022, using the GEOS-Chem model combined with TROPOMI satellite observation data</a></p>
Quasilinear Model of Jovian Whistler Mode Emission
<p>This is the supporting data set for the paper by the same title published in AGU JGR Space Physics, <a href="https://doi.org/10.1029/2021JA029930">10.1029/2021JA029930</a></p> <p><strong>Key Points:</strong></p> <ul> <li>Quasilinear model of whistler instability excited by the loss-cone electron distribution is formulated.</li> <li>The theoretical result compares reasonably well against Juno observation.</li> <li>This shows that the quasilinear theory is a useful tool for interpreting Jovian plasma wave emissions and radiations.</li> </ul> <p><strong>Abstract</strong></p> <p>The whistler mode chorus emissions are pervasively detected by the Juno satellite in Jupiter’s magnetospheric environment. The present paper pays particular attention to a sample observation made by the Juno on November 3, 2019, where typical whistler mode chorus waves are measured. The emission is characterized by a broad range of wave frequencies from below f<sub>ce</sub> /2, where f<sub>ce</sub> denotes the local electron cyclotron frequency,down to the lower-hybrid frequency, with a gradually downshifting frequency over time. The excitation appears to coincide with the detection of a “butterfly” pitch-angle distribution and the expected loss-cone feature associated with the energetic electrons. These anisotropic features, especially the butterfly pitch-angle distribution, gradually disappear as the waves are excited and the electron phase space distribution becomes isotropic. The present paper models these characteristics by means of quasilinear kinetic theory of the whistler instability driven by a loss-cone electron distribution function with a narrow loss-cone angle, which is to be expected from low-latitude regions of the Jovian magnetosphere. It is shown that the theoretically constructed dynamic wave spectrum is consistent with the observation made on Nov. 3, 2019. The present finding demonstrates that the quasilinear theory can be a powerful theoretical tool for interpreting various Jovian plasma wave emissions, which includes the whistler waves, but also other wave modes.</p>
Dataset for "All field emission models are wrong, ... but are any of them useful?"
<p>The dataset are in jupyter format for python. It needs for instance to install anaconda</p> <p><strong>I) The value of kappa at 4.6eV.ipynb </strong></p> <p>This file presents the data and calculations of the section III A of the article concerning the different method to calculate kappa. It includes also the data to plot Fig. 1</p> <p> </p> <p><strong>II) The value of kappa in the low range field.ipynb </strong></p> <p>This file presents the data and fit of the section III B of the article. It includes also the data to plot Fig. 2</p> <p><strong>III) The value of kappa at 4.6eV longfield range.ipynb </strong></p> <p>This file presents the data and calculations of the section III C of the article concerning the different method to calculate kappa in a higher field range. It includes also the data to plot Fig. 3</p> <p> </p> <p><strong>IV) Is it possible to separate phi and beta.ipynb </strong></p> <p>This file presents the data and calculations of the section IV of the article concerning the fact that different emitters can have very similar plots. It includes also the data to plot Fig. 4,5,6,7.</p>
MAgPIE model - Land use change and carbon emissions of a transformation to timber cities
<p>MAgPIE source code for reproducibility</p> <p>Land use change and carbon emissions of a transformation to timber cities<br> (Nature Communications, 2022)</p> <p>DOI: 10.1038/s41467-022-32244-w</p> <p>Abhijeet Mishra1,2,*, Florian Humpenöder1, Galina Churkina1, Christopher P.O. Reyer1, Felicitas Beier1,2, Benjamin Leon Bodirsky1, Hans Joachim Schellnhuber1, Hermann Lotze-Campen1,2, and Alexander Popp1</p> <p>1 Potsdam Institute for Climate Impact Research (PIK), Member of Leibniz Association, P.O.Box 60 12 03, 14412,6<br> Potsdam, Germany<br> 2 Humboldt University of Berlin, Department of Agricultural Economics, Unter den Linden 6, 10099 Berlin,8<br> Germany</p> <p>Abhijeet Mishra<br> *mishra@pik-potsdam.de<br> May 2022</p> <p>See README.md for further details.</p>
Global wetland CH4 emissions estimated by LPJ-wsl model for 1980-2021
<p>This dataset contains two files that summarize the regional wetland methane emissions simulated by LPJ-wsl model for the period of 2000-2021. two estimates based on two climate forcing datasets, ground-based CRU and reanalysis-based MERRA2. The ground-based input dataset is a monthly climatic observation based on meteorological stations developed by Climatic Research Unit, University of East Anglia. The reanalysis-based climate dataset is a daily climatic dataset from 1 hourly reanalysis Modern-Era Retrospective analysis for Research and Applications Version 2. For more details about LPJ-wsl model, see Zhang et al., (2017, 2018).</p> <p>References:</p> <p>Zhang, Z., Zimmermann, N.E., Stenke, A., Li, X., Hodson, E.L., Zhu, G., Huang, C., Poulter, B., 2017. Emerging role of wetland methane emissions in driving 21st century climate change. Proceedings of the National Academy of Sciences 114, 9647–9652. <a href="https://doi.org/10.1073/pnas.1618765114">https://doi.org/10.1073/pnas.1618765114</a></p> <p>Zhang, Z., Zimmermann, N.E., Calle, L., Hurtt, G., Chatterjee, A., Poulter, B., 2018. Enhanced response of global wetland methane emissions to the 2015–2016 El Niño-Southern Oscillation event. Environmental Research Letters 13, 074009. <a href="https://doi.org/10.1088/1748-9326/aac939">https://doi.org/10.1088/1748-9326/aac939</a></p>
Mapping Phyllosilicates on the Asteroid Bennu Using Thermal Emission Spectra and Machine Learning Model Applications Datasets
<p>We provide laboratory spectra of pure minerals and mineral mixtures and the corresponding metadata. Derived from the laboratory data, we include the PLS model through coefficients. Through the application of the model, we provide the prediction values in volume% for Mg-rich serpentine, cronstedtite, and saponite for the BBD1, EQ3, and TAG datasets.</p>
Mapping Phyllosilicates on the Asteroid Bennu Using Thermal Emission Spectra and Machine Learning Model Applications
<p>We provide the laboratory spectra and metadata that was used to construct the PLS model coefficients. Through the application of the model, we provide the prediction values in volume% for Mg-rich serpentine, cronstedtite, and saponite for the BBD1, EQ3, and TAG datasets.</p>
Variability of Global Fire Emissions - Data and Model Code
<p>Netcdf files including all relevant data for the manuscript entitled "Trends and variability of global fire emissions due to historical anthropogenic activities", submitted to Global Biogeochemical Cycles in 2017. </p> <p>FINALv2_presentday_2002-2009.nc: Monthly fire area burned and carbon emissions data from FINAL.2 for the years 2002 through 2009</p> <p>FINALv2C_*_1700-2009.ts.nc: Historical time series of monthly area burned and carbon emissions for natural, secondary, crop and pasture land cover for years 1700 to 2009</p> <p>vegn_fire.F90: The main module of FINAL.2 in the GFDL LM3</p>
Results from, "Causal health impacts of power plant emission controls under modeled and uncertain physical process interference."
<p>This repository contains results from the paper, "<a href="https://arxiv.org/abs/2306.05665">Causal health impacts of power plant emission controls under modeled and uncertain physical process interference</a>," by Wikle and Zigler (2024), to appear in <em>Annals of Applied Statistics</em>. The storage of these results helps facilitate access to and replication of the analysis in the paper. The results include output from the:</p> <ol> <li>Sulfate analysis <ul> <li>tx-2016-100k-int-mx.RDS</li> </ul> </li> <li>Asthma analysis <ul> <li>asthma-pois-cut.RDS</li> <li>asthma-pois-plugin.RDS</li> <li>asthma-bart-cut.RDS</li> <li>asthma-bart-plugin.RDS</li> </ul> </li> <li>Medicare analysis <ul> <li>medicare-pois-cut.RDS</li> <li>medicare-pois-plugin.RDS</li> <li>medicare-bart-cut.RDS</li> <li>medicare-bart-plugin.RDS</li> </ul> </li> <li>Simulation study <ul> <li>simstudy-cm1-lm.RDS</li> <li>simstudy-cm1-bart.RDS</li> <li>simstudy-cm2-lm.RDS</li> <li>simstudy-cm2-bart.RDS</li> <li>simstudy-cm3-lm.RDS</li> <li>simstudy-cm3-bart.RDS</li> <li>simstudy-pm1-pois.RDS</li> <li>simstudy-pm1-bart.RDS</li> <li>simstudy-pm2-pois.RDS</li> <li>simstudy-pm2-bart.RDS</li> <li>simstudy-pm3-pois.RDS</li> <li>simstudy-pm3-bart.RDS</li> </ul> </li> <li>Log-linear BART sensitivity analysis <ul> <li>sensitivity-m100.RDS</li> <li>sensitivity-m200.RDS</li> <li>sensitivity-m300.RDS</li> <li>sensitivity-m400.RDS</li> <li>sensitivity-power05.RDS</li> <li>sensitivity-power1.RDS</li> <li>sensitivity-power15.RDS</li> <li>sensitivity-power2.RDS</li> <li>sensitivity-power25.RDS</li> <li>sensitivity-power3.RDS</li> <li>sensitivity-power4.RDS</li> <li>sensitivity-power5.RDS</li> </ul> </li> </ol> <p>A description of these results can be found at <a href="https://github.com/nbwikle/estimating-interference">https://github.com/nbwikle/estimating-interference</a>, along with the R code used to generate these (and other results, such as figures) found in the manuscript and supplementary material.</p>
Dataset related to the manuscript Wagner and Schepanski (submitted to JAMES, 2024): "Quantifying fire-driven dust emissions using a global aerosol model"
<p>This dataset belongs to the manuscript of Wagner and Schepanski (2024) entitled "Quantifying fire-driven dust emissions using a global aerosol model" submitted to the "Journal of Advances in Modeling Earth Systems (JAMES)".</p> <p>It contains the for the 10 year simulation period 2004-2013 the monthly, seasonal, or yearly averaged fields of the variables (variable name in brackets) that were used to prepare the plots and statements made in the manuscript. These are in detail:</p> <ol> <li>GFAS input data of FRP (frp)</li> <li>simulated AOD (tau_2d_550nm) and dust AOD (tau_comp_du_550nm)</li> <li>simulated wind-driven (emi_du_dust) and fire-driven (emi_du_fdust) dust emission fluxes</li> <li>simulated atmospheric dust concentration (du_all) including the soluble/insoluble coarse (du_ci, du_cs) and accumulation (du_ai, du_as) mode together with vertical atmospheric pressure levels (pfull)</li> </ol> <p>The simulated results are provided for both simulations, the <strong>control run</strong> without the additional fire-dust emissions and the actual <strong>firedust simulation</strong> with the new fire-dust emission parameterization.</p>
A Multi-Pollutant Emissions Inventory for Air Pollution Modeling and Supporting Information for Kampala
<p>This paper is under review</p> <p>Abstract:</p> <p>Kampala, the political and economic capital of Uganda and one of the fastest urbanising cities in sub-Saharan Africa, is experiencing a deteriorating trend in air quality with emissions from multiple diffused local sources like transportation, domestic and outdoor cooking, and industries, and sources outside the city airshed like seasonal open fires in the region. PM2.5 (particulate matter under 2.5um size) is the key pollutant of concern in the city with monthly spatial heterogeneity of 60-100 ug/m3. Outdoor air pollution is distinctly pronounced in the global south cities and lack the necessary capacity and resources to develop integrated air quality management programmes including ambient monitoring, emissions and pollution analysis, source apportionment, and preparation of clean air action plans. This paper presents an integrated assessment of air quality in Kampala drawing from ground measurements (from a hybrid network of stations), satellite observations (from NASA’s MODIS and OMI), global reanalysis fields (from GEOS-chem and CAMS simulations), high resolution (~1km) multi-pollutant emissions inventory for the airshed, WRF-CAMx based PM2.5 pollution analysis, and a qualitative review of institutional and policy environment for air quality management in Kampala. The proposed clean air action plans aim for better air quality in the region using a combination of short-, medium-, and long-term emission control measures for all the dominate sources and institutionalize pollution tracking mechanisms (like emissions and pollution monitoring and reporting) for effective management of air pollution.</p> <p>This data archive serves as a supplemenary to the journal article and with a short description of the files below:</p> <ul> <li>File: AQ-Kampala-Analysis-Summary.pptx (Caution: large 60MB) <br>A composite presentation including the following<br> <ul> <li>Grid summaries</li> <li>Snapshots of airshed GIS files, emission activities</li> <li>Summary of meteorology from WRF simulations and historical synoptics</li> <li>Summaries of ambient monitoring data</li> <li>CAMS reanalysis summary</li> <li>Summaries of Emission inventory and WRF-CAMx modelling (annual and monthly)</li> <li>Summaries of PM2.5 Source apportionment (annual and monthly)</li> </ul> </li> <li>File: grids_kampala.rar<br>Grid file (KML and ESRI shapefiles format) for the airshed spanning 0.0N to 0.6N and 32.3E to 32.9E with a spatial resolution of 0.01deg (~1km)</li> <li>File: gis_roads_from_opensteetmaps.rar<br>ESRI shapefiles of primary roads and all roads, extracted from the openstreetmaps<br>Raw data archive @ https://download.geofabrik.de/index.html</li> <li>File: gis-scanned2021image-quarries.kml<br>KML file of quarries scanned using the imagery on Google Earth platform</li> <li>File: population_kampala_2000-2022.csv<br>Gridded population data 2000 to 2022<br>Raw data archive is from LANDSCAN - https://landscan.ornl.gov</li> <li>File: Monitoring-Kampala_USEmbassy_2017-2024.xlsx<br>Summary of monitoring data collected at the US Embassy in Kampala<br>Raw data archive is @ https://www.airnow.gov/international/us-embassies-and-consulates/#Uganda$Kampala</li> <li>File: meteo_wrf_stats.xlsm<br>Summary of output of WRF simulations for the Kampala region. Have to activiate macros to summarize the results by month and update the charts. The tool can be used to other cities also by changing the input data.</li> <li>File: meteo_precip-era5-reanalysis.csv<br>Summary of monthly precipitation date (mm/day) from ERA5 reanalysis fields<br>Raw data archive is @ https://psl.noaa.gov/data/atmoswrit/timeseries</li> <li>File: TROPOMI_EastAfrica_NO2_Maps.zip<br>Images of monthly average TROPOMI NO2 extracts covering East Africa (Uganda and Ethiopia)<br>Extracted from Google Earth Engine, using 10% cloud fraction</li> <li>File: TROPOMI_EastAfrica_CSVs.zip<br>CSV files of gridded monthly average NO2, SO2, HCHO, and Ozone columnar densities<br>Extracted from Google Earth Engine, using 10% cloud fraction<br>Read the data descriptions and applicability of the data for analysis before using (for example, negative numbers in the SO2 file). <br>NO2 - https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_NO2 <br>SO2 - https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_SO2<br>HCHO - https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_HCHO<br>O3 - https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_O3</li> <li>File: composite_emisson_factors_gains.xlsx<br>A composite library of emission factors for reference</li> <li>File: kampala_gridded_emissions_2018.rar<br>Gridded emissions inventory for Kampala - PM25, PM10, SO2, NO, NO2, and CO<br>PM25 is speciated into FPRM, BC, and OC (sum all for PM25)<br>PM10 is speciated into FPRM, CPRM, BC, OC (sum all for PM10)<br>All emissions in tons/year/grid<br>Emissions are seggragted into sectors and fuels - included in the filenames</li> <li>File: kampala_gridded_modelled_monthavgp25.csv<br>Gridded PM2.5 concentrations for 2018, from WRF-CAMx modelling system<br>Monthly averages in ug/m3</li> </ul>
A Multi-Pollutant Emissions Inventory for Air Pollution Modeling and Supporting Information for Addis Ababa
<p>This paper is under review. For additional information or queries, send email to sguttikunda@urbanemissions.info</p> <p>****</p> <p>ABSTRACT: Ground measurements and satellite observations over Addis Ababa airshed show a deteriorating trend of air pollution, especially for PM2.5 (all particulate matter under 2.5um). In this paper, we present a review of available monitoring data; a model-ready multi-pollutant (PM10, PM2.5, SO2, NOx, CO, non-methane VOCs, and CO2) emissions inventory at 0.01º resolution for the designated airshed; a heatmap of PM2.5 concentrations and an estimate of source contributions constructed using WRF-CAMx chemical transport modelling system; and a discussion on proposed actions towards establishing an air quality management plan for the city. Emissions from road transport; residential and commercial cooking; resuspended dust on roads and from construction activities; residential and industrial heating; lighting; open waste burning; and other industrial activities contributed the most to ambient PM2.5 pollution. Particularly, vehicle exhaust is estimated to contribute up to 29% of total PM2.5, followed by biomass combustion in the residential and industrial sectors.</p> <p>File included in this dataset:</p> <ol> <li>Composite presentation of supporting information and analysis results<br>File: Report-Addis-Data-Summary.pptx<br>This presentation includes summary images of<br> <ul> <li>monitoring data</li> <li>the GIS fields</li> <li>google earth scans </li> <li>annual and monthly emissions</li> <li>annual and monthly PM2.5 concentrations</li> <li>annual and monthly source apportionment</li> </ul> </li> <li>Ambient monitoring data<br>File: AddisAbaba_AllEmbassy_Data.xlsx (summary of data till May2024)<br>File: monitoring_addisair_cleaned.xlsx (summary of sensor data)</li> <li>Gridded emissions inventory<br>File: addis_gridded_emissions.rar<br>Format: ix,iy,midlong,midlat,FPRM,CPRM,BC,OC,NO,NO2,CO,SO2<br>Units: tons/grid/year<br>PM2.5 emissions = FPRM + BC + OC<br>PM10 emissions = FRPM + BC + OC + CPRM<br>midlong and midlat are midpoints of grids - see gis_addis_grids.rar</li> <li>TROPOMI UVAI and MODIS AOD<br>Data files extracted from google earth engine as TIF and CSV, for the designated airshed<br>File: ADDISABABA_yearly_tifs.zip</li> <li>Meteorlogical data summary for the airshed, extracted from the WRF sumulations<br>File: meteo_addis.houravg_summary.csv</li> <li>Reference reports<br>File: Report-C40-2016-Addis-Ababa-GHG-Emssions.pdf<br>File: Report-CSE-Ethiopia-Urban-AQM-Guidance.pdf<br>File: Report-WB-Ethiopia-motorization-management.pdf<br>File: Report-UNEP-Addis-AQM-Plan-Draft.pdf</li> <li>GIS files<br>File: gis_addis_grids.rar (shapefile and KML file for the airshed grid)<br>File: gis_addis_osm_roads.rar (shapefiles extracted from openstreetmaps)<br>File: pop_extracts_4selection_domain.xlsm (gridded population data, with macros can be used to extract population totals around a monitoring station)<br>File: gis_addis_multiple_layers.rar (as KML files - districts, townships, main_roads, water_bodies, quarries, landfill, industrial_areas)</li> </ol>
A Multi-Pollutant Emissions Inventory for Air Pollution Modeling and Supporting Information for Bishkek
<p>Full paper is published here<br><a href="https://doi.org/10.3390/air2040021">https://doi.org/10.3390/air2040021</a><br>Mapping PM2.5 Sources and Emission Management Options for Bishkek, Kyrgyzstan<br><br></p> <p>****</p> <p>Harsh winters, aging infrastructure and control technologies, and the increasing demand for urbanization and modernization of amenities are major factors contributing to the deteriorating air quality in Bishkek, the capital city of Kyrgyzstan and a burgeoning economic hub in Central Asia. The heating energy needs are met via combustion of coal at the central heating plant, heat only boilers, and in-situ heating equipment and the mobility needs via combustion of diesel and petrol. Other mapped sources contributing to daily air pollution levels in Bishkek’s airshed include 30 km2 of industrial area, 16 large open combustion brick kilns, a vehicle fleet with average age more than 10 years, 7.5 km2 of quarries, and one landfill. Annual PM2.5 emission load for the airshed is approximately 5,500 tons, resulting in an annual average concentration of 48 ug/m3, which is 9-10 times higher than the World Health Organization (WHO) guideline of 5 ug/m3. Wintertime daily averages range from 200-300 ug/m3. Proposed emissions management policies for the city include shift to clean fuels like gas and electricity at the heating plants and households, restricting the secondhand vehicle imports and incentivizing newer standard vehicles, promotion of public transport system with newer buses, at least doubling of the waste collection efficiency and landfill management capacity and encouraging greening and maintaining road infrastructure to control dust emissions. PM2.5 levels from mid- to long-term implementation of these options is expected to drop by 50-70%. A long-term plan for Bishkek must include an expansion of the ambient monitoring network using a combination of reference-grade and low-cost sensors to track progress of air quality management efforts and to support information dissemination for public awareness.</p> <p>Files included here:</p> <ol> <li>Composite presentation of supporting information and analysis results<br>File: Bishkek_AQ_Analysis_Composite.pptx<br>This presentation includes summary images of<br> <ul> <li>monitoring data</li> <li>the GIS fields</li> <li>google earth scans </li> <li>annual and monthly emissions</li> <li>annual and monthly PM2.5 concentrations</li> <li>annual and monthly source apportionment</li> </ul> </li> <li>Meteorological data summary for the airshed, extracted from the WRF sumulations<br>File: Bishkek_Met_Summary.pptx<br>File: bishkek_meteorology_stats.xlsm (activate macros for stats and making images for final use)</li> <li>GIS files<br>File: gis_bishkek-grids-pop.rar (shapefile and KML file for the airshed grid, csv file for gridded population 20 years, and images)<br>File: gis_bishkek_roads.rar (shapefiles extracted from openstreetmaps)<br>File: gis_bishkek_adm0.rar (shapefiles of administrative boundaries)<br>File: gis_bishkek_multiplelayers.rar (as KML files - districts, townships, main_roads, water_bodies, quarries, landfill, industrial_areas)</li> <li>CAMx output (units is ug/m3)<br>File: bishkek_camx_pm25_monthlyavg.csv (see the grid file for mapping)</li> <li>Gridded model-ready emissions inventory<br>File: bishkek_gridded_emissions_2018.rar (see the grid file for mapping)<br>Format: ix,iy,midlong,midlat,FPRM,CPRM,BC,OC,NO,NO2,CO,SO2<br>Units: tons/grid/year<br>PM2.5 emissions = FPRM + BC + OC<br>PM10 emissions = FRPM + BC + OC + CPRM<br>midlong and midlat are midpoints of grids - see gis_bishkek-grids-pop.rar</li> <li>Ambient monitoring data<br>File: bishkek_monitoring_claritysensor_2021.xlsx<br>File: bishkek_monitoring_us.embassy2019-2024.rar</li> </ol>
Dataset for FEM modelling of emission reference material (ERM) developed in the EMPIR project 20NRM04 MetrIAQ
<p>This dataset contains the numerical data used in the project 20NRM04 MetrIAQ for the FEM model simulations on the emission reference material (ERM) with the software COMSOL 6.2. The data published here are freely accessible with a text editor.<br>The accompanying document '<em>FEM data and file name explanations.pdf'</em> contains additional information for each data file.<br>A detailed description of the development of the FEM model can be found in the '<em>Guideline with recommendations on the customised design and dimensioning of the ERM, including the uncertainty of the numerical model developed for the prediction of the emission rate. The numerical model will (i) support the customised generation of the ERM (ii) simulate the transport processes inside the ERM and the compound release into test chamber air and (iii) enable the prediction of the emissions for each of the selected target VOC</em>' (<a href="https://doi.org/10.5281/zenodo.12749007" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12749007</a>).</p>
Modeling and simulation of a new Urban Lightweight Electric Vehicle concept based on the optimized use of renewable energies and the reduction of CO2 emissions
<p>This work has produced a series of scientifc contributions. This library develops different mathematical expressions and assumptions for the dynamic modelling of an smart-grid located within a solar-powered ULEV are derived. The code was developed using Dymola</p>
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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.