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2,208 results for “emission”
Global energy use and carbon emissions from irrigated agriculture
<p>This repository contains supporting data for: "<strong>Global energy use and carbon emissions from irrigated agriculture"</strong></p><p>Email: qinjingxiu17@mails.ucas.ac.cn and duanweili@ms.xjb.ac.cn</p><p>The dataset contains:</p><p>-Global energy consumption and CO2 emissions from irrigation . </p><p>-Global CO2 emissions from groundwater degassing . </p><p>-Energy consumption and CO2 emissions with different irrigation and pumping systems and irrigation water sources. </p><p>-Global energy consumption and CO2 under drip and sprinkler scenarios. </p><p>-Global energy consumption and CO2 under mix electricity scenarios. </p><p>-Energy units: Terajoule (TJ); CO2 emissions units: (Tonnes CO2)</p><p>-Files are uploaded in .tif raster data. </p>
Dataset related to publication: Robust radiative cooling via surface phonon coupling-enhanced emissivity from SiO2 micropillar arrays
<p>Dataset related to the publication:</p><p>Zhenmin Ding, Xin Li, Hulin Zhang, Dukang Yan, Jérémy Werlé, Ying Song, Lorenzo Pattelli, Jiupeng Zhao, Hongbo Xu, Yao Li. Robust radiative cooling via surface phonon coupling-enhanced emissivity from SiO2 micropillar arrays. <i>International Journal of Heat and Mass Transfer</i>, 220, 125004 (2024). doi: <a href="https://doi.org/10.1016/j.ijheatmasstransfer.2023.125004">10.1016/j.ijheatmasstransfer.2023.125004</a></p><p>The repository contains MATLAB/Octave scripts to perform rigorous coupled-wave analysis (RCWA) simulations for a SiO2 layer decorated with micropillars.</p><p>The main script runs a series of rigorous electromagnetic simulations over the atmospheric transparency window wavelength range (8-13 µm) for all combination of three main structural parameters (pillar diameter, spacing and height), within a user-defined range.</p><p>Running the code requires the RETICOLO v9 RCWA code:</p><blockquote><p>Jean-Paul Hugonin, & Philippe Lalanne. (2021). Light-in-complex-nanostructures/RETICOLO: V9. Zenodo. <a href="https://doi.org/10.5281/zenodo.4419063">https://doi.org/10.5281/zenodo.4419063</a></p></blockquote>
Synthetic JWST MIRI-MRS Observations of Mid-IR Noble Gas Emission from T Cha
<p>Synthetic detctor images for the continuum + line emission from [Ne II], [Ne III], [Ar II], [Ar III] for T Cha made with MIRISim (Klaassen et al. 2021) for the overall best fitting model with r_in = 0.1 rG (both with and without a cavity). Also provided are backgrounds for the source observation (applicable to both with and without a cavity) and models for a synthetic standard star and its background. </p> <p>The .fits files are the underlying data cubes for the [Ne II] and [Ar II] lines which were provided as inputs to the simulator. </p> <p>For a full description, see Sellek et al. (2024a).</p>
High-resolution air pollution emission inventory for the Nordic countries
<p>This common Nordic (Denmark, Finland, Iceland, Norway, and Sweden) air pollution emission inventory was compiled using country total emissions from national emission inventories that the countries submit to the CLRTAP. Our inventory was based on the 2016-2018 submissions. The inventory contains annual emissions for 1990, 1995, 2000, 2005, 2010, 2012 and 2014. Components included in the inventory are: particulate matter (PM10 and PM2.5), black carbon (BC), organic carbon (OC), sulphur oxides (SOx), nitrogen oxides (NOx), carbon monoxide (CO), non-methane volatile organic compounds (NMVOC) and ammonia (NH3). The gridding was done separately for each country, using national data and gridding methods. The emissions were harmonized to the same sector nomenclature, i.e. SNAP, and to the EEA reference grid. Spatial resolution for the inventory is 1 km × 1 km in the European grid ETRS89-LAEA (EPSG: 3035). Large point source emissions are provided with locations and stack heights included. Two modifications to the CLRTAP submissions were made: (1) road transport non-exhaust PM emissions were adjusted to better conform with Nordic traffic dust assessments; and (2) for OC emission, that are not included in the inventories, rough estimates were calculated based on expert estimates on OC/PM2.5-ratios on main SNAP level. The inventory was originally created for the NordicWelfAir-project (<a href="https://projects.au.dk/nordicwelfair">https://projects.au.dk/nordicwelfair</a>). The main aim of developing this new inventory was to provide air pollution modelers and health scientists a harmonized dataset to be used for studies on the link between air pollution exposure and negative impacts on the human health.<br>Description of the data can be found in this data article, which can be referenced when using the data: <a href="https://doi.org/10.5194/essd-16-1453-2024">https://doi.org/10.5194/essd-16-1453-2024</a>.</p>
Data of emission of floral volatiles and damage induced emissions of plants
<p>The file Emissiondata_plants_GCIMS contains a broad data set on the emissions of plant volatile organic compounds from different taxa. This allows cross species comparison of recorded emission pattern. Furthermore, distribution of identified (or unidentified) compounds can be traced for diferent species. </p> <p>All measurements were conducted performed with a mobile ppq-tec-GC-IMS (ION-GAS GmbH, Dortmund, Germany) based on hardware provided by STEP GmbH (Pockau-Lengefeld, Germany). GC pre-separation was performed under isothermal conditions (80°C) for 1500 s on a MXT-200 capillary column (30 m x 0.53 mm, 1.5 µm coating) with a carrier gas flow (filtered air from internal gas circuit) of 21 mL min<sup>-1</sup>. Ionization of pVOCs for mobility separation was performed using a tritium source of β-radiation (100 MBq). Mobility separation and subsequent detection were performed with a drift-tube IMS (drift length of 5.61 cm) at 70 °C and at a field strength of 300 V cm<sup>-1</sup>.</p> <p> </p> <p>The dataset contains 14 Variables and a total of 1866 observations (status: 10/06/2023, Version 1.0.0)</p> <p>4 variables describe the plant material. This includes the variables Species, Genus, Family and Order </p> <p>4 variables describe the sampled species. This includes plant part (flower or leaves), plant status (damaged or undamaged), the sample location and the Accession (only if the sample was provided by the Bonn University Botanical Gardens)</p> <p>6 variables describe the recorded emission patterns. Compound refers to the substance (unidentified compounds are abbreviated with UNK-n), retention time and relative ion mobility (parameters that allow cross species comparison and identification of substances), signal type (for some substances, ion clusters can be observed at higher concentrations. signal type refers to these ion clusters), and signal intensity (semi-quantitative measure for the abundance of a substance) and relative abundance (rel_abund; proportional contribution of a substance within a species, where the strogest signal is 1). </p> <p> </p> <p>The reference_compounds table is added to this. This contains information on substances that have already been identified (CAS, mass weight, retention time and relative ion mobility, dimerisation). These data were collected by direct injection of pure substances into the GC-IMS used. </p> <p> </p> <p> </p>
Dataset of paper "Applying Density-Based Clustering for the Analysis of Emission Events in Real Driving Emissions"
<p>This dataset includes the signal traces for the events used in the publication "Applying Density-Based Clustering for the Analysis of Emission Events in Real Driving Emissions Calibration", MDPI Future Transportation, 2024 (doi: 10.3390/futuretransp4010004).</p> <p>The data is available in a frequency of 1 Hz and the signals are arranged in separate tables. Please note, that the order of the events is not consistent for the individual signals.</p> <p>The general vehicle specifications are listed below. For further information please refer to the paper.</p> <table> <tbody> <tr> <td><strong>Characteristic</strong></td> <td><strong>Unit</strong></td> <td><strong>Value</strong></td> </tr> <tr> <td>Vehicle weight</td> <td>kg</td> <td>> 2000</td> </tr> <tr> <td>Fuel</td> <td>-</td> <td>Gasoline</td> </tr> <tr> <td>Engine type</td> <td>-</td> <td>Turbo-charged 8 cylinder</td> </tr> <tr> <td>Engine power and torque</td> <td>kW / Nm</td> <td>> 400 / > 600</td> </tr> <tr> <td>Cubic capacity</td> <td>cm^3</td> <td>~ 4000</td> </tr> <tr> <td>Transmission</td> <td>-</td> <td>Automatic transmission (AT)</td> </tr> <tr> <td>Drivetrain</td> <td>-</td> <td>All-wheel drive (AWD)</td> </tr> <tr> <td>Exhaust aftertreatment system (EATS)</td> <td>-</td> <td>Three-way catalytic converter (TWC) and gasoline particulate filter (GPF)</td> </tr> <tr> <td>Condition of EATS</td> <td>-</td> <td>Stabilized EATS (~ 70 % of tests) and aged EATS (~ 30 % tests)</td> </tr> <tr> <td>Emission target</td> <td>-</td> <td>EU6d</td> </tr> </tbody> </table> <p> </p>
On effective spectral wideband models for clear sky atmospheric emissivity and transmissivity
<p><strong>Overview</strong></p> <p>The HDF5 file contains primary measurement data and secondary processing data that was used to assess clear sky effective emissivity and transmissivity estimates and generate the results in the associated manuscript (accepted and forthcoming).</p> <p>Data is indexed by solar time and provided per site for years 2010 through 2015. Sample Python code is provided to reconstruct training and validation sets by concatenating all 'tra' or 'val' samples across sites. Results can be explored by modifying choice of filters and constructing new training and validation sets.</p> <p><strong>Data usage</strong></p> <p>The usage of the data presented here is intended for research and development purposes only and implies explicit reference to the paper:<br><em>Matsunobu, L. M., & Coimbra, C. F. M. (2024). On effective spectral wideband models for clear sky atmospheric emissivity and transmissivity. Journal of Geophysical Research: Atmospheres, 129, e2023JD039798. https://doi.org/10.1029/2023JD039798</em></p> <p><strong>Data description</strong></p> <p>Column names and descriptions are as follows:<br>- dlw_m: measured downwelling longwave [W/m^2]<br>- ghi_m: measured global horizontal irradiance [W/m^2]<br>- dni_m: measured direct normal irradiance [W/m^2]<br>- dhi_m: measured diffuse horizontal irradiance [W/m^2]<br>- rh_m: measured relative humidity [%]<br>- pa_m: measured atmospheric pressure [hPa]<br>- t_m: measured temperature [K]<br>- sza: solar zenith angle [deg]<br>- ghi_c: clear sky global horizontal irradiance [W/m^2]<br>- dni_c: clear sky direct normal irradiance [W/m^2]<br>- dhi_c: clear sky diffuse horizontal irradiance [W/m^2]<br>- cs1: clear sky filter 1<br>- cs2: clear sky filter 2<br>- site_elev: station elevation [m]<br>- clr_pct: fraction of samples identified as clear for the given site and day<br>- clr_num: number of samples identified as clear for the given site and day<br>- pw_hpa: water vapor partial pressure [hPa]<br>- alt_correction: altitude correction<br>- tra: indicate if sample is included in training set<br>- val: indicate if sample is included in validation set<br>- sqrt_pw: square root of non-dimensional water vapor partial pressure<br>- e_sky: effective clear sky emissivity</p> <p>The last two columns, 'sqrt_pw' and 'e_sky' represent the input and target for linear regression, i.e. e_sky = c_1 + (c_2 * sqrt_pw).<br>Altitude corrected sky emissivity, or expected emissivity for a station at sea-level, is found by e_sky - alt_correction.</p> <p><strong>Sample code (Python v3.8)</strong></p> <pre>import pandas as pd site = "GWC" # or other station code df = pd.read_hdf("data.h5", key=site) # import single site</pre> <p>Training and validation sets can be reconstructed as below. Linear regression on 'sqrt_pw' to predict 'e_sky' - 'alt_correction' in the resultant training set will reproduce results in the associated manuscript.</p> <pre>training = [] validation = [] surfrad_sites = ['BON', 'DRA', 'FPK', 'GWC', 'PSU', 'SXF', 'TBL'] for site in surfrad_sites: # loop through sites df = pd.read_hdf("data.h5", key=site) df["site"] = site # add site name training.append(df.loc[df.tra]) # append samples marked as training validation.append(df.loc[df.val]) # append samples marked as validation # join respective set samples across sites training = pd.concat(training, ignore_index=False) validation = pd.concat(validation, ignore_index=False)</pre> <p>Reproduce regression results</p> <pre>from sklearn.linear_model import LinearRegression c1 = 0.6 # set intercept (c1 constant) x = training.sqrt_pw.to_numpy().reshape(-1, 1) y = training.e_sky - training.alt_correction - c1 # adjust for altitude and c1 y = y.to_numpy().reshape(-1, 1) model = LinearRegression(fit_intercept=False) model.fit(x, y) c2 = model.coef_[0][0] print(f"c1={c1:.3f}, c2={c2:.3f}") # output: c1=0.600, c2=1.652</pre>
Dataset of vehicle emission measurements in real-world subfreezing winter conditions
<p>Dataset of vehicle emission measurements in real-world subfreezing winter conditions. Measured by chasing the measured vehicle. See Info.txt for description of the data.</p>
SPATIAL DIFFERENTIATION OF THE EMISSIVITY OF AGRICULTURE IN EUROPE
<p>The file contains the data used in the article: <br>DOI:10.5604/01.3001.0054.4326</p> <p>Replacements included in the file (for 2020):<br>Country<br>Item: IPCC Agriculture<br>Total emissions in tonnes<br>Emissions per hectare of agricultural land<br>Emissions per unit value of goods produced by agriculture<br>Emissions per capita</p> <p><br>Source: FAOSTAT database</p>
Emissions gap NDC and net zero findings after COP27
<p>Analysis of the level and robustness of various government climate pledges, both 2030 NDCs and net zero goals. This repository contains the data required to run <a href="https://github.com/Rlamboll/Emissions_Gap">https://github.com/Rlamboll/Emissions_Gap</a>.</p> <p>"Analysis_update_12_14 (2).xlsx" contains the 2023 assessment of net zero pledges by governments in terms of both their quality and content. "Data_forextensions_PostCOP27_master.xlsx" contains the estimates of emissions until 2030 based on NDCs after COP27, "Data_forextensions_EGR2023_master.xlsx" contains the updated version for the paper "Credibility gap in net-zero climate targets leaves world at high risk".</p> <p>"2022_emission_gap_temp_summary_data.csv" is the output of the complete process. </p> <p>"kyoto_and_co2_emissions_summary_23.65.csv" is the Kyoto total and CO2 emissions for each scenario, "infilled_extended_and_infilled_unep_23.65.csv" is the complete set of emissions for each scenario. </p> <p>Versions 1.0.0 and 1.0.1 are identical in terms of processing, but more data is uploaded for 1.0.1 with scenarios with additional carbon price increase rates. Version 1.1.0 contains the Data_forextensions_EGR2023_master file. Version 1.2.0 contains a bugfix affecting net zero targets applied to the OECD+ regional emissions, and includes emissions data. </p> <p>Data and calculation are associated with the paper at DOI: 10.1126/science.adg6248</p>
Hypersonic Transport: 3D Emission Inventory of STRATOFLY-MR3 Fleet Operated on Brussels to Sydney Route in 2075
<p>High-resolution 3D inventories of future hypersonic transport (HST) are compiled for the year 2075, integrating the gaseous engine emissions of a fleet of 200 hydrogen-powered Mach 8 passenger aircraft*. These aircraft are operated once a day for 360 days on a reference route from Brussels (BRU) to Sydney (MYA) with either NO<sub>x</sub>-optimized (ICA**: 114 000 ft; 34.75 km) or H<sub>2</sub>O-optimized (ICA**: 107 500 ft; 32.77 km) flight profiles, derived to minimize environmental impacts in terms of total emissions. The emissions are spatially gridded at a horizontal resolution of 1° in longitude and latitude, with a vertical resolution of 1000 ft, and are temporally accumulated on an annual basis. Note that the 3D emission inventories encompass detailed data on species-specific HST emissions***, fuel burn, and total distance traveled: </p> <ul> <li>Species: NO, H<sub>2</sub>O; H<sub>2</sub></li> <li>Temporal information: 2075; annually</li> <li>Spatial information: 1° x 1° x 1000 ft</li> <li>Data Format: NetCDF</li> </ul> <p>-----------------------------------------------------------------------------------------------------------------------------------------<br>* The hypersonic aircraft concept under consideration is the <a href="https://arc.aiaa.org/doi/abs/10.2514/6.2021-1877">STRATOFLY-MR3</a> vehicle, which was conceptually developed in <br> the framework of the <a href="https://cordis.europa.eu/project/id/769246">H2020 STRATOFLY project</a>.<br>** Initial Cruise Altitude<br>*** with a unit of kg/km<sup>3 </sup>(corrected in v0.2)</p> <p> </p>
Maps of the detailed spatially and temporally attributed emission for area of Legerova and Sokolska (TURBAN-D18)
<h3>Basic information</h3> <p>This dataset contains six folders with maps of input data for simulations published in project TURBAN as result D17 (see <a href="../records/10982836">https://zenodo.org/records/10982836</a>). Each folder contains air quality inputs for the so-called Legerova domain, an area in the city of Prague, Czech Republic, centred around the traffic-heavy streets Legerova and Sokolská. All times are in UTC (local time in winter, CET, is UTC +01:00, summer time, CEST, is UTC +02:00). In total 6 episodes in 2022 and 2023 were selected:</p> <ol> <li>s1 2022-07-17 00:00:00 - 2022-07-20 00:00:00</li> <li>s2: 2022-08-02 00:00:00 - 2022-08-05 00:00:00</li> <li>s3: 2022-09-22 00:00:00 - 2022-09-25 00:00:00</li> <li>s4: 2022-12-08 00:00:00 - 2022-12-11 00:00:00</li> <li>s5: 2023-01-27 00:00:00 - 2023-01-30 00:00:00</li> <li>s6: 2023-02-13 00:00:00 - 2023-02-16 00:00:00</li> </ol> <p>For more detailed description of the experiments see the <strong>TURBAN</strong> project website at <a href="https://www.project-turban.eu/">https://www.project-turban.eu/</a>.</p> <h3>General organisation, variables and file nomenclature</h3> <p>Each selected epizode (s1-s6) has three subfolders; input files in ASCII (<em>output-ascii</em>) or GeoTiff (<em>output-gis</em>) formats that can be viewed in many GIS applications. In the third subfolder are maps in the PNG format (<em>output-png</em>).</p> <p>Each subfolder includes 4 subfolders with emissions summarized in all layers above ground. Variable <em>vsrc_PM10</em> is the concentration of volume source emissions (VSRC) of the PM10, <em>vsrc_PM25</em> is the concentration of PM2.5, <em>vsrc_NO</em> is the concentration of NO and <em>vsrc_NO2</em> is the concentration of NO2.</p> <p>Each file (PRJ, TIF, ASC or PNG) has the same nomenclature. An example (vsrc_NO_abs-01h_20220717_1200-1300.png) could be parsed as: variable name (vsrc_NO), processed input (abs-01h), date (20220717) and period (1200-1300). So, the result is a map with emission fluxes of NO between 12:00 and 13:00 UTC 24 Jul 2019.</p> <h3>Emissions (see section 2.4.3 in Resler et al., 2024)</h3> <p>The data were processed from datasets published by CHMI, data collected by the Municipality of Prague and its organizations, data obtained by the researcher (ATEM) while providing expert studies in the past, and results of previous research projects. The input data of the used emission sources can be divided into two basic groups: emission from local heating and transport sources.</p> <p>Emissions for local heating were determined by calculations based on data from CHMI and the Czech Statistical Office (CZSO). Emissions from the transport sources were modeled using the MEFA transportation emission model which is recommended for the use in the Czech Republic by the Ministry of Environment of the Czech Republic. The model takes into account factors such as road gradient, the number of vehicles on the road, the flow of traffic, the composition of car types, and the emission characteristics of the individual car types. The emission calculation is based on data from the traffic census provided by the Prague Technical Administration of Roads (TSK Praha) and on data from the census of the composition of the transportation fleet in Prague built in the MEFA emission model. The data are based on regular surveys of the fleet composition carried out in Prague (Karel et al., 2021). The dust resuspension was computed according to the methodology published by the Ministry of Environment (Karel et. al., 2015). This methodology is based on US EPA methodology AP-42 (EPA, 2011) and was adjusted for the conditions of the Czech Republic. For the garages and parking lots, the results of the project TH03030496 (Karel et al., 2020) were used and for the bus stations, publicly available data about transportation were gathered from the Prague Public Transit Company (DPP).</p> <p>The disaggregation of the annual emissions into hourly intervals was then performed according to the type of source. For combustion sources distribution of emissions to days was done according to natural gas supply profiles for category DOM4 were used (OTE, 2024) and complemented by daily profiles for SNAP 2 (van der Gon, 2011). For transport sources, the census data from TSK Praha was utilized for all streets where it was available. For Legerova and Sokolská streets, hourly traffic intensity data were obtained and used directly for the selected episodes. For streets that were not covered by regular traffic surveys, the spatial and temporal distribution of the traffic intensities were based on analysis and evaluation of the relevant studies for the particular area (e.g. urban planning studies, Environmental Impact Assessment (EIA), etc.) and combined with information like street type, location, traffic regime, and pavement type. This approach allowed us to specify the distribution of the transportation intensities on smaller streets. For the detailed modeling of emissions from rail transport (diesel locomotives), the data of train rides were obtained from the Railway Administration (SŽ) and emission factors from the EMEP/EEA Air Pollutant Emission Inventory Guidebook 2019 (EEA, 2019) were used. Emissions from river ships were obtained from the CHMI national database and spatially distributed to the area of the river.</p> <p>Spatial transformation of the line and point emission into the corresponding areas was done with the utilization of the surrogates representing corresponding areas (e.g. areas of the street traffic lines and parking places for traffic emission and areas of the building roofs for local heating sources). This not only ensured the reasonable spatial distribution of the emission in the street canyon but also decreased the gradients of the emission field and with this proneness of the model to numerical inaccuracy of the micro-scale model. The processing of the emission sources into hourly emission flows was done in the emission model FUME recently extended for processing of the PALM emission (Belda et al., 2024).</p> <h3>Acknowledgements</h3> <p>The PALM simulations, and pre- and postprocessing were performed partially on the HPC infrastructure of the Institute of Computer Science of the Czech Academy of Sciences (ICS), supported by the long-term strategic development financing of the ICS (RVO:67985807) and partially on the IT4I HPC infrastructure supported by the Ministry of Education, Youth and Sports of the Czech Republic through the e-INFRA CZ (ID:90254). The work was performed within the project TURBAN (TO01000219; TURBAN – Turbulent-resolving urban modelling of air quality and thermal comfort) supported by Norway Grants and Technology Agency of the Czech Republic.</p> <h3>Literature</h3> <p>Note that some sources are available only in Czech language.</p> <p>Belda, M., et al. (2024) FUME 2.0 – Flexible Universal processor for Modeling Emissions, EGUsphere [preprint]. <a href="https://doi.org/10.5194/egusphere-2023-2740">https://doi.org/10.5194/egusphere-2023-2740</a></p> <p>Karel, J., et al. (2020) Projekt TH03030496 - Zmapování a emisní bilance neevidovaných zdrojů emisí znečišťujících látek na území městských aglomerací. Mapa neevidovaných zdrojů emisí znečišťujících látek na území aglomerace CZ01 Praha. Partially available at: <a href="https://www.atem.cz/neevidovane_zdroje.php">https://www.atem.cz/neevidovane_zdroje.php</a></p> <p>Karel, J., et al. (2015) Metodika pro výpočet emisí částic pocházejících z resuspenze ze silniční dopravy, CENEST, s. r. o., Prague. Available at: <a href="https://www.mzp.cz/C1257458002F0DC7/cz/doprava/$FILE/OOO-resuspenze_metodika-20190708.pdf">https://www.mzp.cz/C1257458002F0DC7/cz/doprava/$FILE/OOO-resuspenze_metodika-20190708.pdf</a></p> <p>Karel J., et. al. (2021) Zpráva o dynamické skladbě vozového parku na území hlavního města Prahy v roce 2020, Prague 2021. Available upon request from the Environmental Protection Division of the Prague Municipality.</p> <p>EPA (2011) Compilation of Air Pollutant Emission Factors, Volume I, AP-42. Section 13.2.1. Paved roads. EPA Research Triangle Park, US, 2003, updated 2011. Available at: <a href="https://www.epa.gov/air-emissions-factors-and-quantification/ap-42-compilation-air-emissions-factors-stationary-sources">https://www.epa.gov/air-emissions-factors-and-quantification/ap-42-compilation-air-emissions-factors-stationary-sources</a></p> <p>van der Gon, H.D., et al. (2011) Description of Current Temporal Emission Patterns and Sensitivity of Predicted AQ for Temporal Emission Patterns. EU FP7 MACC Deliverable Report D_D-EMIS_1.3. Available at: <a href="https://atmosphere.copernicus.eu/sites/default/files/2019-07/MACC_TNO_del_1_3_v2.pdf">https://atmosphere.copernicus.eu/sites/default/files/2019-07/MACC_TNO_del_1_3_v2.pdf</a></p> <p>EEA (2019) European Environment Agency, EMEP/EEA air pollutant emission inventory guidebook 2019 – Technical guidance to prepare national emission inventories, Publications Office. Available at: <a href="https://data.europa.eu/doi/10.2800/293657">https://data.europa.eu/doi/10.2800/293657</a></p> <p>OTE (2024) Gas Load Profiles - temperature and recalculated TDD. Available at: <a href="https://www.ote-cr.cz/en/statistics/gas-load-profiles/normalized-lp?set_language=en">https://www.ote-cr.cz/en/statistics/gas-load-profiles/normalized-lp?set_language=en</a></p> <p> </p> <p> </p>
PDS 111: 1.3mm continuum and 12CO emission - ALMA observations
<p>The self-calibrated ALMA observations at 1.3mm wavelength of the PDS 111 system, published in Derkink et al. (2024). These observations were part of the ALMA Program ID 201.1.01705.S. Please check the README.txt for more details about each file. </p>
Electron Donor-Functionalized Pyrenes with Amplified Spontaneous Emission for Violet-Blue Electroluminescent Devices Beyond the Spin Statistical Limit
<p>Quantum Chemical TD-DFT Data on the <span>M062X-GD3/def2-TZVP level of theory. Ground state geometries, first excited state geometries, and single point calculations for donor functionalized pyrenes. </span> </p>
Dataset for the paper "Combining near-term benefits of climate adaptation with long-term benefits of emissions abatement"
<p>This repo archives all data used in Duan et al. (2024), including model codes, raw model outputs, and post-processing scripts. </p> <p>A Readme file describes the data and structure included here. If you have any questions, please contact the lead author (Lei Duan: leiduan@carnegiescience.edu). </p> <p>We have updated the post-process codes to reflect changes in the revised manuscript</p> <p>==</p> <p>Paper associated with this dataset can be found at: https://www.nature.com/articles/s43247-024-01976-6#:~:text=Adaptation%20deployed%20in%20conjunction%20with,adaptation%20reducing%20near%2Dterm%20damage. </p>
Fair emissions allocations under various global conditions
<h1>Introduction</h1> <p>This dataset contains information on how to fairly distribute the mitigation efforts that countries need to undertake to together achieve certain climate goals. There is no single answer to this question, but we explore this topic by looking at various global emissions pathways, and subsequently allocate these emissions to countries using different effort-sharing rules. This data is applied in a preprint of a <a href="https://www.researchsquare.com/article/rs-5023350/v1">scientific article</a> where we explore implications of justice on NDCs and international mitigation finance.</p> <p>The research behind this dataset is still under development and therefore this dataset is not final. Our scientific work is still under revision so the data is subject to potential changes upon peer review of this publication. Nevertheless, because (a version of) this data is already used in the Carbon Budget Explorer and in scientific projects, we feel it should be available and versioned. Hence these releases of a preliminary version.</p> <h1>Carbon Budget Explorer</h1> <p>We also published this work on a website called the <em>Carbon Budget Explorer</em>: an online interactive tool that allows users to navigate through these results, without having to download and plot the data themselves. It is free and publicly available at <a href="https://www.carbonbudgetexplorer.eu">www.carbonbudgetexplorer.eu</a>. Currently, the Carbon Budget Explorer relies on a previous version of this dataset (version 0.1, unpublished, but available upon request). The Explorer will be updated with new data early 2025 (i.e., with the version presented in this data repository).</p> <h1>Data description</h1> <h3>Default (DefaultAllocations.zip and DefaultReductions.zip)</h3> <p>For many users, these are the main datafiles. Per country and region, allocations and reduction targets are shown for two trajectories, which are associated with 1.5 (with slight overshoot: peak temperature 1.6) and 2.0 degree pathways, and default settings across all other dimensions. The exact parameters used in these precooked pathways are shown in Table 1 (see "Dimensions"). The <em>reductions_default_*.csv</em> files show data along the same structure, also using the default pathways, but contain the emission reductions with respect to 2015 rather than absolute allocations.</p> <h3>Global pathways (GlobalPathways.zip)</h3> <p>Allocating emissions to countries starts with determining global emissions pathways. The files in <em>GlobalPathways.zip</em> contain projected global emissions on GHG, CO2 and non-CO2 levels, constrained by various global settings (see below) such as temperature targets and derived CO2 budgets. The pathway shapes are informed by mitigation scenarios from the IPCC AR6 database. The starting values are all harmonized with 2021 historical datapoints. For convenience, the <em>emissionspathways_default.csv</em> datafile provides the pathways with default settings (see Table 1, column 'Default'). The complete dataset can be found in <em>emissionspathways_all.csv</em>.</p> <h3>Emission allocations (Allocations.zip -> allocations_*.nc)</h3> <p>The emissions from the global pathways can be divided among countries according to different allocation rules (see 'Allocation rules' for more information). Files of the format <em>allocations_region.nc </em>indicate allocations according to all allocation rules, parameters and global choices, for a single region. Because of the high number of parameters and dimensions, these files are shared in NetCDF (.nc) format. NetCDF files are commonly used for storing multidimensional scientific data and can be displayed, analyzed and read/written using GIS systems (such as ArcGIS, QGIS), MATLAB funcions (such as <em>nccreate</em>, <em>ncread</em>), R (e.g. using the <em>ncdf4</em> package) and Python (e.g. using the <em>xarray</em> package).</p> <h3>Input data (Inputdata.zip)</h3> <p>Additional input data coming from third parties, such as population and GDP data, is stored in <em>Inputdata.zip</em>. We prepared these input data sources in the exact same format as the rest for convenience of the user, but we would like to emphasize that the appropriate references should be cited. For further information, please check 'Input data sources'.</p> <h3>CO2 budgets</h3> <p>A file has been added in the version 0.3.1, including cumulative CO2 budgets. How they are calculated, is slightly different for each rule (only PC, AP and ECPC are included here), because of the varying nature of these allocation rules. The PC budget is simply the fraction of the remaining carbon budget determined by a country's 2021 population share. The AP budget is computed by adding all positive CO2 allocations according to the AP rule. The ECPC budget is the full-century budget: that is, historical leftover (or debt) plus a country's fair per capita share between 2021-2100. Note that there is not necessarily a one-to-one relation between these budgets and the CO2 part of the allocation files (<em>Allocations.zip</em>). For example, the PC budget uses 2021 population, while the allocation files use year-to-year population numbers (also if they change in the future). We have the ambition to, in next versions, expand this dataset to account for and vary the choices one can make in this regard.</p> <h1>Allocation rules</h1> <p>Below you can find a summarized description of all allocation rules. More detailed information can be found in <a href="https://link.springer.com/article/10.1007/s10584-019-02368-y" target="_blank" rel="noopener noreferrer">Van den Berg et al. (2020)</a>, as well as in a scientific paper (preprint) expected in summer 2024. The rules have a variety of parameters, each included as dimensions in the data. See Table 1, in "Dimensions", for details.</p> <ul> <li>The (immediate) 'Per Capita' method (PC) uses a country's population share in the global population and allocates future emissions accordingly. Naturally, socio-economic conditions affect this method. Therefore, all five SSPs are used in our analysis. </li> <li>'Grandfathering' (GF) is a method that preserves current emission fractions. In other words, all countries reduce their emissions proportional to their current share. Note that this rule is controversial and is commonly not regarded as fair (see <a href="https://www.tandfonline.com/doi/full/10.1080/14693062.2021.1970504">Rajamani et al. 2021</a>). It is include here for reference only.</li> <li>The 'Per Capita Convergence' (PCC) method starts as 'Grandfathering', but converges over time to a 'Per Capita' basis. An additional important parameter here is the year at which this convergence completes.</li> <li>The 'Per Capita via Budget' (PCB_lin) method is a specific implementation of distributing the total CO2 budget on a per capita basis, and then drawing a linear line from current emissions down to net-zero CO2. A median non-CO2 path is added to end up with a total greenhouse gas emissions line. This is similar to, for example, <a href="https://newclimate.org/resources/publications/what-is-a-fair-emissions-budget-for-the-netherlands">Fekete et al. (2022)</a>.</li> <li>The 'Ability to Pay' (AP) method allocates emissions inversely related to the GDP per capita of countries. Also this method is dependent on the socio-economic scenario.</li> <li>The 'Equal Cumulative Per Capita' (ECPC) method builds on the per-capita convergence method, also accounts for historical responsibility: throughout the convergence period, countries resolve historical 'debt' or 'leftover' from what countries would have emitted if it had emissions according to a per capita share in the past. <em>Note</em>: this method has been significantly revised in version 0.4. In earlier versions, resolving of historical responsibility was only achieved by 2100, postponing most debt.</li> <li>The 'Greenhouse Development Rights' (GDR) method is, in the short run, based on a <a href="https://calculator.climateequityreference.org/" target="_blank" rel="noopener noreferrer">Responsibility-Capability Index</a>, and in the long run based on GDP per capita (similar to 'Ability to Pay').</li> </ul> <h1>Dimensions</h1> <p><em>Table 1 - Data dimensions</em></p> <table> <tbody> <tr> <td><strong>Name</strong></td> <td><strong>Unit</strong></td> <td><strong>Range</strong></td> <td><strong>Default</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td><strong>General</strong></td> <td><strong> </strong></td> <td><strong> </strong></td> <td><strong> </strong></td> <td><strong> </strong></td> </tr> <tr> <td>Time</td> <td>Year</td> <td> <p>Past: 1850-2021</p> <p>Future: 2021-2100 (yearly or 5-year increments)</p> </td> <td>All</td> <td>The historic data reported here ends in 2021, and we start our analysis in 2021. Intentionally, to be able to exactly match historic and future data. The year 2021 is chosen because of limited availability of more recent data sources.</td> </tr> <tr> <td>Region</td> <td>ISO3 code</td> <td> <p>Country-level (ISO3)</p> <p>Country groups (e.g., G20 and Umbrella)</p> <p>World ('EARTH')</p> </td> <td>All</td> <td> </td> </tr> <tr> <td><strong>Global</strong></td> <td><strong> </strong></td> <td><strong> </strong></td> <td><strong> </strong></td> <td><strong> </strong></td> </tr> <tr> <td>Temperature</td> <td>Degrees temperature rise with respect to pre-industrial times</td> <td> <p>1.5 - 2.0 degrees</p> </td> <td>1.6 and 2.0</td> <td>Peak temperature without overshoot</td> </tr> <tr> <td>Climate sensitivity ('Risk' in the data)</td> <td>Risk of exceeding a certain climate target, based on climate sensitivity percentiles.</td> <td> <p>17%, 33%, 50%, 67%, 83%</p> </td> <td> <p>50% (for 1.6 degrees) and 33% (for 2.0 degrees)</p> </td> <td> <p>This governs the uncertainty in climate sensitivity. Because there is still uncertainty about the exact numerical response of temperature to CO2, we have to include this. Low-risk (e.g., 0.17) indicates that we assume a high climate sensitivity: for a given amount of greenhouse gas emissions, temperature rises higher. This means that carbon budgets at a given temperature level have to be lower. Vice-versa for high-risk (e.g., 0.83).</p> </td> </tr> <tr> <td>NegEmis</td> <td>Quantiles of 2100 GHG emissions among AR6 scenarios with a similar temperature target</td> <td> <p>17%, 33%, 50%, 67%, 83%</p> </td> <td> <p>50%</p> </td> <td> <p>Even though negative emissions (predominantly in the second-half of the century) are not very relevant for achieving a certain peak temperature, they do alter the second half of global emissions pathways.</p> </td> </tr> <tr> <td>NonCO2red</td> <td>Quantiles of non-CO2 reductions in 2040 with respect to 2020 among AR6 scenarios with a similar temperature target</td> <td> <p>17%, 33%, 50%, 67%, 83%</p> </td> <td>50%</td> <td>Non-CO2 reduction varies greatly among mitigation scenarios, but at the same time has a large effect on the remaining carbon budget. Hence, we vary this factor.</td> </tr> <tr> <td>Timing</td> <td>-</td> <td> <p>Immediate or Delayed</p> </td> <td>Immediate</td> <td>The timing of mitigation action up to 2030. Either this starts immediately (2020) or only after 2030. This factor distinguishes mitigation scenarios from which the functional form of the global emissions pathways are constructed.</td> </tr> <tr> <td><strong>Parameters in allocation rules</strong></td> <td> </td> <td> <p> </p> </td> <td> </td> <td> </td> </tr> <tr> <td>Scenario</td> <td>SSP</td> <td> <p>SSP1-5</p> </td> <td>SSP2</td> <td>Shared-Socioeconomic pathway, defining population and GDP data based on a scenario of how to perceive the future world.</td> </tr> <tr> <td>Convergence_year</td> <td>Year</td> <td> <p>2040, 2050, 2080, 2100</p> </td> <td>2050</td> <td>Year of convergence for the per capita convergence and equal-cumulative per capita rules.</td> </tr> <tr> <td>Discount_factor</td> <td>% per year</td> <td> <p>0%, 1.6%, 2%, 2.8%</p> </td> <td>0%</td> <td>Discount factor of historical emissions, counting from the startyear 2021.</td> </tr> <tr> <td>Historical_startyear</td> <td>Year</td> <td> <p>1850, 1950, 1990</p> </td> <td>1990</td> <td>Year from which and on historical emissions are accounted for in the computation of the responsibility of countries.</td> </tr> <tr> <td>Capability_threshold</td> <td>-</td> <td> <p>No, PrTh, Th</p> </td> <td>Th</td> <td>Implicates whether an additional development threshold should be implemented for the computation of the capability of a country to contribute to mitigation. This is used in the calculations of the Greenhouse Development Rights rule. Entries are (1) no development threshold (No), (2) a threshold of \$7500 (Th) or (3) the \$7500 threshold plus additional progressivity factors. For more information, see <a href="https://joss.theoj.org/papers/10.21105/joss.01273">Holz et al. (2019)</a>.</td> </tr> <tr> <td>RCI_weight</td> <td>-</td> <td> <p>Cap, Half, Resp</p> </td> <td>Half</td> <td>Distinguishes how the Responsibility-Capability Index in the Greenhouse Development Rights rule should weight capability (fully = Cap) or responsibility (fully = Resp). 'Half' indicates that both factors should weigh equally.</td> </tr> </tbody> </table> <h1>Input data sources</h1> <p>For most important data sources, aggregated regions (e.g., G20 and the Umbrella group) are not reported in the original data sources below. We did that aggregation ourselves.</p> <ul> <li>Historic population: UN population data</li> <li>Future population: <a href="https://data.ece.iiasa.ac.at/ssp/#/login">SSP database</a></li> <li>Future GDP: <a href="https://data.ece.iiasa.ac.at/ssp/#/login">SSP database</a></li> <li>Historical emissions: <a href="https://www.nature.com/articles/s41597-023-02041-1">Jones et al. (2023)</a></li> <li>Emissions pathways (shapes): <a href="../records/7197970">Byers, E. et al. AR6 Scenarios Database. (2022)</a></li> <li>NDC data: <a href="https://themasites.pbl.nl/o/climate-ndc-policies-tool/">PBL NDC tool</a></li> <li>Carbon budgets: <a href="https://essd.copernicus.org/articles/15/2295/2023/">Forster et al. (2023)</a></li> <li>Impact of non-CO2 on carbon budgets: <a href="https://www.nature.com/articles/s43247-023-01168-8">Rogelj et al. (2024)</a></li> </ul> <h1>Changelog</h1> <ul> <li>Version 0.4.2: <ul> <li>Fixed export error that resulted in incomplete PCB_lin data.</li> </ul> </li> <li>Version 0.4.1: <ul> <li>Fixed export error that mixed up the columns in DefaultReductions and DefaultAllocation files.</li> </ul> </li> <li>Version 0.4: <ul> <li>Equal-cumulative per capita is significantly revised in terms of temporal allocation. This has large consequences for short-term allocations in most countries, depending on the convergence year. See description above under 'Allocation rules'.</li> <li>Data is now also available for different analysis starting years, gases and including or excluding LULUCF. This is upon request because this would make the repository too large.</li> <li>In the same spirit, a selection has been made on what to include in these datafiles for completeness and clarity, and what to omit to limit file size and computation problems. If you need any specific parameter combination that you cannot find here, feel free to contact us.</li> <li>Improved data on historical population data and baseline emissions</li> <li>Added units in CSV datafiles</li> </ul> </li> <li>Version 0.3.1: <ul> <li>Added CO2 budgets for additional combinations of global targets (no changes in allocation values)</li> </ul> </li> <li>Version 0.3: <ul> <li>Fixed small error in regional aggregation</li> <li>Cumulative CO2 budgets for PC, AP and ECPC are added in a new file</li> <li>Updated global baseline emissions, which affects AP and ECPC</li> </ul> </li> <li>Version 0.2: <ul> <li>Significant update on LULUCF emissions data and historical emissions data by changing to a more up-to-date data source</li> <li>NDC data update (now from the PBL NDC tool)</li> <li>Added the per-capita via budget rule</li> <li>All the above affect emissions allocations, which are therefore also updated</li> </ul> </li> <li>Version 0.1: <ul> <li>First version of the data</li> <li>Published on the Carbon Budget Explorer</li> </ul> </li> </ul> <h1>Contact</h1> <p>We are very open to suggestions of all kinds. Feel free to contact Mark Dekker at <a href="mailto:mark.dekker@pbl.nl?subject=Effort%20Sharing%20Data">this email address </a>or at the contact form on <a href="https://www.pbl.nl/en/about-pbl/employees/mark-dekker">this website</a>.</p>
Data set associated to the manuscript entitled Carbon emissions from inland waters may be underestimated: evidence from European river networks fragmented by drying by López-Rojo et. al
<p>CO2 and CH4 emissions and several associated environmental variables were taken in 6 European drying river networks, in 20 river reaches per river network. The field work was carried across 3 sampling campaigns in 2021, coinciding with 3 hydrological seasons (pre-dry, dry and post-rewetting) to encompass most of the hydrological variability. Each time, measures were taken in the habitats available (flowing water, dry riverbeds, isolated pools).</p>
Carbon Monitor - Global Daily CO2 Emissions in Near-Real-Time
<p><strong><em>Carbon Monitor: A near-real-time global daily CO2 emission dataset</em></strong></p> <p>Carbon dioxide (CO<sub>2</sub>) emissions from the use of fossil fuels and the production of cement are the main driving force of climate change. Carbon Monitor is an international initiative providing for the first time regularly updated, science-based estimates of daily CO<sub>2</sub> emissions.</p> <ul> <li>Website:</li> </ul> <p><a href="https://carbonmonitor.org">https://carbonmonitor.org</a></p> <ul> <li>Citation:</li> </ul> <p>Liu, Z., Ciais, P., Deng, Z. <em>et al.</em> Near-real-time monitoring of global CO<sub>2</sub> emissions reveals the effects of the COVID-19 pandemic. <em>Nat Commun</em> <strong>11, </strong>5172 (2020). https://doi.org/10.1038/s41467-020-18922-7</p> <ul> <li>Data file description:</li> </ul> <table> <thead> <tr> <th scope="col">Field</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>country</td> <td>Brail, China, EU27 & UK, France, Germany, India, Italy, Japan, ROW, Russia, Spain, UK, US, WORLD *</td> </tr> <tr> <td>co2</td> <td>CO2 emissions from fuel combustion and cement production process (unit: kt CO2)</td> </tr> <tr> <td>sector</td> <td>Power, Industry, Residential, Ground Transport, Domestic Aviation, International Aviation, International Shipping, Total **<sup>,</sup>***</td> </tr> <tr> <td>date</td> <td>From 2019/1/1, every day</td> </tr> </tbody> </table> <p>* WORLD = China + US + EU27 & UK + India + Russia + Japan + Brazil + ROW + International Aviation (WORLD) + International Shipping (WORLD)</p> <p>** Total (country level) = Power + Industry + Residential + Ground Transport + Domestic Aviation</p> <p>** Total (WORLD) = Power + Industry + Residential + Ground Transport + Domestic Aviation + International Aviation + International Shipping</p>
The detection of radio emission from known X-ray flaring star EXO 040830−7134.7
<p>This is the radio light curve of known X-ray flaring star EXO 040830−7134.7 observed by MeerKAT as part of ThunderKAT. These data are part of a publication in the Monthly Notice of the Royal Astronomical Society (Driessen et al., Accepted 2021 November 25. Received 2021 November 25; in original form 2021 August 25).</p> <p>The light curve is from the full-time-integration, full-frequency-integration images of VW Hyi, as processed by the LOFAR Transients Pipeline (<a href="https://tkp.readthedocs.io/en/latest/introduction.html">TraP</a>).</p> <p>The columns in the file are:</p> <ul> <li>mjd: the modified Julian Date (MJD) of the observation. The MJD is given by MJD=JD-2400000.5 where JD is the Julian Date</li> <li>f_int_Jy: the integrated flux density of the source in Jansky (Jy) determined by the LOFAR TraP</li> <li>f_int_err_Jy: the uncertainty on f_int_Jy in Jansky determined by the LOFAR TraP</li> <li>freq_eff_Hz: the effect frequency in Hertz (Hz) as determined by the LOFAR TraP</li> <li>taustart_ts: the ISO 8601 time of the observation in Coordinated Universal Time (UTC)</li> </ul> <p>The files were made using the Pandas package, so we recommend Python users load them using</p> <pre><code>import pandas as pd pd.read_csv(filename, comment='#')</code></pre> <p>If you use the data shared here please ensure that you cite the MNRAS paper (Driessen at al. 2021) and the Zenodo DOI: 10.5281/zenodo.5084298.</p> <p>The MeerKAT telescope is operated by the South African Radio Astronomy Observatory, which is a facility of the National Research Foundation, an agency of the Department of Science and Innovation.<br> LND acknowledges support from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 694745).</p>
Emissions from building materials - concentration of micropollutants and heavy metals in stormwater runoff of two new development areas in Berlin (Germany)
<p>This dataset includes concentrations of micropollutants (27) and heavy metals (7) for stormwater runoff from different sampling points at two test sites (A and B) in Berlin, Germany. Both sites are new development areas of similar size that were both constructed in 2017 (1 – 1.5 years prior to the start of the monitoring campaign). Composite samples of individual rain events were taken at three sampling points of each test site: façade runoff, roof runoff and corresponding stormwater runoff from the catchment area. Samples were taken as part of the research project BaSaR (<a href="http://www.kompetenz-wasser.de/en/forschung/projekte/basar/">www.kompetenz-wasser.de/en/forschung/projekte/basar/</a>) of Kompetenzzentrum Wasser Berlin, Ostschweizer Fachhochschule and Berliner Wasserbetriebe. More information including sampling and analytical methods are detailed in the corresponding journal paper "Emissions from building materials – a thread for the environment?", submitted to the MDPI-journal <em>Water</em>.</p> <p><strong>Description of fields:</strong></p> <ul> <li><strong>SiteID</strong>: site identifier <ul> <li>A: new development site with typical architecture for multi-storey apartment buildings with plastered and painted facades in northern part of Berlin (124 apartments)</li> <li>B: new development site with typical architecture for multi-storey apartment buildings with plastered and painted facades in southeastern part of Berlin (122 appartments)</li> </ul> </li> <li><strong>SamplingPoint</strong> <ul> <li>facade runoff: runoff from plastered facade collected with gutters during individual rain events</li> <li>roof runoff: roof runoff collected from one downpipe during individual rain events</li> <li>storm sewer: stormwater runoff sampled during individual rain events in a manhole receiving runoff from the entire catchment (A or B)</li> </ul> </li> <li><strong>LocalDateTime_StartRain</strong>: start time of sampled rain event (CET / CEST)</li> <li><strong>LocalDateTime_EndRain</strong>: end time of sampled rain event (CET / CEST)</li> <li><strong>CardinalDirection</strong>: only relevant for facade runoff <ul> <li>N: runoff from facade oriented to the north</li> <li>W: runoff from facade oriented to the west</li> </ul> </li> <li><strong>VariableName</strong>: name of analysed substance/parameter</li> <li><strong>CensorCode</strong>: either "lt" (less than) for concentration below detection limit (value is detection limit) or "nc" (not censored) for concentration above detection limit</li> <li><strong>UnitsAbbreviation</strong>: either "ug/L" (microgram per litre) or "mg/L" (milligram per litre)</li> <li><strong>DataValue</strong>: measured value (if censor code is lt, value indicates detection limit)</li> </ul> <p>One data file is provided in comma separated format:<br> "BaSaR_data.csv" contains concentrations of all samples.</p>
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