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EU MarcoPolo project | SO2 emission inventory over China
<p>The aposteriori SO<sub>2</sub> emissions for year 2014, in the domain from 102°E to 132°E and from 15°N to 55°N, in a 0.25°x0.25° spatial resolution and monthly temporal resolution, have been provided to the MarcoPolo project and can be found at <a href="http://users.auth.gr/mariliza/MarcoPolo/SO2_EmissionInventory/">http://users.auth.gr/mariliza/MarcoPolo/SO2_EmissionInventory/</a>. For details on the creation of the inventory refer to <a href="http://users.auth.gr/mariliza/MarcoPolo/D3.4_SO2_emission_estimates.pdf">http://users.auth.gr/mariliza/MarcoPolo/D3.4_SO2_emission_estimates.pdf</a> and for the inclusion of the SO2 emission inventory to the MarcoPolo Emission Database refer to: <a href="http://users.auth.gr/mariliza/MarcoPolo/D4.2_DescriptionMarcoPoloInventory.pdf">http://users.auth.gr/mariliza/MarcoPolo/D4.2_DescriptionMarcoPoloInventory.pdf</a> as well as <a href="http://users.auth.gr/mariliza/MarcoPolo/D4.3_assessment_impact_updated_emission_inventories_v2.0.pdf">http://users.auth.gr/mariliza/MarcoPolo/D4.3_assessment_impact_updated_emission_inventories_v2.0.pdf</a> .</p> <p>The main reference to this dataset is found here:</p> <p>Koukouli, M. E., Theys, N., Ding, J., Zyrichidou, I., Mijling, B., Balis, D., and van der A, R. J.: Updated SO<sub>2</sub> emission estimates over China using OMI/Aura observations, Atmos. Meas. Tech., 11, 1817–1832, https://doi.org/10.5194/amt-11-1817-2018, 2018.</p> <p>The netcdf data files contain the following structure:</p> <ul> <li>Dimensions <ul> <li>lat = 129</li> <li>lon = 121</li> </ul> </li> <li>Attributes <ul> <li>author = "MariLiza Koukouli"</li> <li>contact information = "mariliza@auth.gr"</li> <li>institution = "Laboratory of Atmospheric Physics, Aristotle University of Thessaloniki"</li> <li>time frame = "2014"</li> <li>sector classification = "total emissions"</li> <li>emis_cat_name = "sulphur dioxide emissions"</li> <li>source_type_name = "sulphur dioxide emissions"</li> <li>pollutant_description = "updated sulphur dioxide emissions based on the CHIMERE model running the MEIC emissions and the OMI/Aura observations"</li> <li>unit_emissions = "Mg/month"</li> <li>nodata_value = "-9999.0"</li> </ul> </li> <li>Variables <ul> <li>float emissions(lon, lat)</li> </ul> </li> </ul>
Data for: Impact of SO2 injection profiles on simulated volcanic forcing for the Sarychev 2009 eruptions - investigating the importance of using high vertical resolution methods when compiling SO2 data
<p>The files are data assosicated with the study High-resolution stratospheric volcanic SO2 injections in WACCM. The files are associated with four differnt simulaions described in the paper: M16, S21-1D, S21-3D and No-Volc. The files with "input" in the name are the SO2 input files used in the WACCM (Whole Atmosphere Community Climate Model) simulations in the paper. The files with "monthly_averages" in the filenames are monthly averages of model output data the variables used in the paper. </p> <p>The CALIOP_monthly_averages.nc file is monthly average of the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) satellite data used in the study to evaluate the WACCM simulations. </p> <p> </p>
Seasonal and longitudinal variability in Io's SO2 atmosphere from 22 years of IRTF/TEXES observations
<p>This dataset contains the reduced Io spectra used in the paper "Seasonal and longitudinal variability in Io's SO2 atmosphere from 22 years of IRTF/TEXES observations" (doi: 10.1016/j.icarus.2024.116151). There are 150 spectra, spanning from 2001 to 2023. These spectra are described in Table 1 of the paper.</p> <p>The spectra in the data file are listed in date order. For each spectrum, we first provide the date (YYMMDD format) and the mean Io central longitude at the time of the observation. This is then followed by the spectrum. Column 1 is the wavelength, in units of microns. Column 2 is the Io spectrum, which has been divided by a Callisto spectrum, flattened in order to correct for any residual continuum slope, and then normalized such that the continuum level is 1. </p>
Sulphur Dioxide (SO2) emitted by Mount Etna on 4 March 2021
<p>Tracking volcanic Sulphur Dioxide (SO2) emitted by Mount Etna on 4 March 2021 that reached China with Sentinel-5P/TROPOMI data (<a href="https://www.volcanodiscovery.com/kunlun/news/124081/SO2-cloud-detected-in-the-area-of-Kunlun-volcano-Tibet-could-it-be-from-a-volcanic-eruption.html">Volcano Discovery article</a>).</p> <p> </p>
CEDS_GBD-MAPS: Global Anthropogenic Emission Inventory of NOx, SO2, CO, NH3, NMVOCs, BC, and OC from 1970-2017
<p><strong>CEDS_GBD-MAPS: Global Anthropogenic Emission Inventory of NO<sub>x</sub>, SO<sub>2</sub>, CO, NH<sub>3</sub>, NMVOCs, BC, and OC from 1970-2017</strong></p> <p><strong>version tag: 2020_v1.0 (April 2020)</strong></p> <p>Annual anthropogenic emissions of 7 key atmospheric pollutants from 1970 - 2017, produced using the <a href="http://www.globalchange.umd.edu/ceds/">Community Emissions Data System</a>, updated for the Global Burden of Disease - Major Air Pollution Sources project (<a href="https://github.com/emcduffie/CEDS/tree/CEDS_GBD-MAPS">CEDS_GBD-MAPS</a>).</p> <p>Emissions are provided for NO<sub>x</sub>, SO<sub>2</sub>, CO, NH<sub>3</sub>, NMVOCs, Black Carbon (BC), and Organic Carbon (OC) from 11 anthropogenic sectors and four fuel categories as both annual country totals and global gridded emission fluxes (0.5 x 0.5 degree resolution).<br> Note: The CEDS_GBD-MAPS inventory does not include emissions from open fires or aircraft.<br> <strong>Sectors: </strong><br> 1. Agriculture (non-combustion sources only, excludes open fires)<br> 2. Energy (transformation and extraction)<br> 3. Industry (combustion and non-combustion processes)<br> 4. On-Road Transportation<br> 5. Off-Road/Non-Road Transportation (rail, domestic navigation, other)<br> 6. Residential Combustion<br> 7. Commercial Combustion<br> 8. Other Combustion<br> 9. Solvents<br> 10. Waste (disposal and handling)<br> 11. International Shipping<br> <strong>Fuel Categories:</strong><br> 1. Total Coal Combustion (hard coal + brown coal + coal coke)<br> 2. Solid Biofuel Combustion<br> 3. Liquid Fuel (light oil + heavy oil + diesel oil) plus Natural Gas Combustion<br> 4. CEDS Process Source Categories (see McDuffie, et al., (ESSD) 2020) for further details.<br> Note: Total anthropogenic emissions = the sum of fuel categories 1-4</p> <p><strong>Zip File Details:</strong><br> The CEDS_GBD-MAPS inventory is available in three different formats:<br> <br> 1. <em>CEDS_GBD-MAPS_annual_country_total_emissions_by_sector_fuel_1970-2017.zip</em></p> <ul> <li>Zip file contains 7 .csv files that each contain a complete times series (1970-2017) of total annual anthropogenic emissions of each compound from each country, as a function of 11 anthropogenic sectors and 4 fuel categories.</li> <li>Emissions are in units of kt yr<sup>-1</sup> and include NO<sub>x</sub> (as NO<sub>2</sub>), CO, SO<sub>2</sub>, NH<sub>3</sub>, total NMVOCs, BC, and OC</li> </ul> <p>2. <em>CEDS_GBD-MAPS_gridded_emissions_by_sector_fuel_[year].zip</em></p> <ul> <li>Each .zip file contains 145 netCDF files of annual anthropogenic global gridded emission fluxes, reported as a function of 11 anthropogenic sectors and 5 fuel categories (1 file per compound per fuel category, plus 1 file for the sum of all fuel categories)</li> <li>Emission fluxes are in units of kg m<sup>-2</sup> s<sup>-1</sup> and include NO<sub>x</sub> (as NO), CO, SO<sub>2</sub>, NH<sub>3</sub>, 25 speciated VOCs, BC, and OC</li> <li>Emission fluxes are provided as monthly averages and have been formatted for use in the GEOS-Chem model (<a href="http://acmg.seas.harvard.edu/geos/">http://acmg.seas.harvard.edu/geos/</a>).</li> <li>Example: ALD2-em-liquid-fuel-plus-natural-gas_CEDS_1970.nc inside the CEDS_GBD-MAPS_gridded_emissions_by_sector_fuel_1970.zip file provides monthly emission fluxes in 1970 for the subVOC ALD2 that result from the combustion of liquid fuel and natural gas in each of the 11 source sectors.</li> </ul> <p>3. <em>CEDS_GBD-MAPS_[compound]_gridded_total_anthro_emissions_by_sector_input4CMIP_1970-2017.zip</em></p> <ul> <li><em>compound = [BC_OC], [CO_NOx_SO2_NH3], [speciated_NMVOCs_01-04], [speciated_NMVOCs_05-08], [speciated_NMVOCs_09-14], [speciated_NMVOCs_15-18], [speciated_NMVOCs_19-22], or [speciated_NMVOCs_23-25]</em></li> <li>Each .zip file contains between 2 - 4 netCDF files (1 per compound) of anthropogenic global gridded emission fluxes from 1970-2017, as a function of 11 anthropogenic sectors only (no disaggregation of fuel categories)</li> <li>netCDF files follow the CEDS CMIP6 gridded emissions format. More information available at: <br> <a href="http://www.globalchange.umd.edu/ceds/ceds-cmip6-data/">http://www.globalchange.umd.edu/ceds/ceds-cmip6-data/</a></li> <li>Emission fluxes are in units of kg m<sup>-2</sup> s<sup>-1</sup> and include NO<sub>x</sub> (as NO<sub>2</sub>), CO, SO<sub>2</sub>, NH<sub>3</sub>, 25 speciated VOCs, BC, and OC</li> <li>Emission fluxes are provides as monthly averages</li> <li>Note: Zip files are group by compound only as a means to reduce the zipped file sizes. The file format for each compound is the same. </li> </ul> <p> </p> <p><strong>*Additional data details are provided in the README.txt file*</strong></p> <p> </p> <p>*Version 2020_v1.0 of this dataset was produced to accompany the following manuscript:<br> McDuffie, E. E., S. J. Smith, P. O'Rourke, K. Tibrewal, C. Venkataraman, E. A. Marais, B. Zheng, M. Crippa, M. Brauer, R. V. Martin, <strong>A global anthropogenic emission inventory of atmospheric pollutants from sector- and fuel- specific sources (1970- 2017): An application of the Community Emissions Data System (CEDS)</strong>, <em>Earth System Science Data, Submitted</em></p>
Europa spectral images of HCN, SO2, H2CO and CH3OH observed using ALMA
<p>This dataset contains observations of Europa carried out using the Atacama Large Millimeter/submillimeter Array (ALMA) on four dates in 2021, using the Band 7 receiver. The targeted spectral lines included HCN ($J=4-3$; 354.505 GHz), H$_2$CO ($J_{K_a,K_c}=5_{1,5}-4_{1,4}$; 351.769 GHz), SO$_2$ ($J_{K_a,K_c}=5_{3,3}-4_{2,2}$; 351.257 GHz), observed at 122~kHz resolution, and CH$_3$OH ($J_K = 4_0 - 3_{-1}\ E$; 350.688 GHz), observed at 61 kHz resolution. The interferometric data were cleaned and imaged using CASA, with a pixel size of 0.02'' and a clean mask of 1.1''. The resulting spatial resolution is ~0.15 arcseconds.</p> <p>The data includes FITS image cubes and PDF spectral maps for each molecule, on each observing date. See FITS headers for further details. For each PDF, the ALMA beam FWHM is indicated lower left (dot-dashed ellipse); upper right axes show physical distances in the plane of the sky, while lower left axes show the spectral units for each sub-panel.</p>
RAL IMS retrieval of SO2 and sulphates (January to April 2022)
<p><strong>RAL IMS retrieval of SO2 and sulphates</strong></p> <p><strong>Description of the product</strong></p> <p>The RAL (Rutherford Appleton Laboratory) Infrared/Microwave Sounder (IMS) retrieval core scheme (Siddans, 2019) uses an optimal estimation spectral fitting procedure to retrieve atmospheric and surface parameters jointly from co-located measurements by IASI (Infrared Atmospheric Sounding Interferometer), AMSU (Advanced Microwave Sounding Unit) and MHS(Microwave Humidity Sounder) on MetOp-B spacecraft, using RTTOV 12 (Radiative Transfer for TOVS)(Saunders et al., 2017) as the forward radiative transfer model. The use of RTTOV 12 enables the quantitative retrieval of volcanic-specific aerosols (sulphate aerosol) and trace gases (SO2). The present dataset includes IMS SO2 and sulphate aerosols retrievals from its near-real time implementation. The IMS scheme retrieves the SO2 in the sensitive region around 1100-1200 cm<sup>−1</sup>, in ppbv assuming a uniform vertical mixing ratio. It retrieves sulphate-specific AOD (Aerosol Optical Depth) at 1170 cm<sup>−1</sup> (i.e. the peak of the mid-infrared extinction cross section (Sellitto and Legras, 2016)), assuming a Gaussian extinction coefficient profile shape peaking at 20 km altitude, with 2 km full-width half-maximum. The bulk of the spectroscopic information on SO2 and sulphate aerosols, in the IMS scheme, thus comes from the IASI Fourier transform spectrometer (Clerbaux et al., 2009).</p> <p>We refer to the two retrieved products as IMS SO2 and IMS SA OD.</p> <p><strong>References</strong></p> <p>Clerbaux, C., Boynard, A., Clarisse, L., George, M., Hadji-Lazaro, J., Herbin, H., Hurtmans, D., Pommier, M., Razavi, A., Turquety, S., Wespes, C., and Coheur, P.-F.: Monitoring of atmospheric composition using the thermal infrared IASI/MetOp sounder, Atmospheric Chemistry and Physics, 9, 6041–6054, https://doi.org/10.5194/acp-9-6041-2009, 2009.</p> <p>Saunders, R., Hocking, J., Rundle, D., Rayer, P., Hayemann, S., Matricardi, A., Lupu, C., Brunel, P., and Vidot, J.: RTTOV-12 SCIENCE AND VALIDATION REPORT; Version : 1.0, Doc ID : NWPSAF-MO-TV-41, https://nwp-saf.eumetsat.int/site/download/documentation/rtm/docs_rttov12/rttov12_svr.pdf, 2017.</p> <p>Sellitto, P. and Legras, B.: Sensitivity of thermal infrared nadir instruments to the chemical and microphysical properties of UTLS secondary sulfate aerosols, Atmospheric Measurement Techniques, 9, 115–132, https://doi.org/10.5194/amt-9-115-2016, 2016.</p> <p>Siddans, R.: Water Vapour Climate Change Initiative (WV-CCI) - Phase One, Deliverable 2.2; Version 1.0, https://climate.esa.int/documents/1337/Water_Vapour_CCI_D2.2_ATBD_Part2-IMS_L2_product_v1.0.pdf, 2019.</p> <p><strong>Description of the data</strong></p> <p>The archive IMS-2022.tgz contains level 3 daily gridded files for the two retrieved products IMS SO2 and IMS SA OD in the period 13 January to 30 April 2022. A few days are missing between 9 March and 13 March. The first 8 letters of the name of each file contain the date. There are 4 files per day as the two products are in separate files and there is a file collecting day-time orbits and another one for night-time orbits every day.</p> <p>For the 28 April 2022, the four files are</p> <p>20220428_ims_metopb_tir_qnrt_aot0_day_global_g0.5_qc0.nc SA OD day-time orbits</p> <p>20220428_ims_metopb_tir_qnrt_aot0_night_global_g0.5_qc0.nc SA OD night-time orbits</p> <p>20220428_ims_metopb_tir_qnrt_so2_day_global_g0.5_qc0.nc SO2 day-time orbits</p> <p>20220428_ims_metopb_tir_qnrt_so2_night_global_g0.5_qc0.nc SO2 night-time orbits</p> <p>The names for other dates can be derived by changing the first 8 letters.</p> <p>The format is netdcf4 that is readable with many programming languages and graphics packages.</p> <p>The data are on a [-90,90] x [-180,180] lat x lon grid with resolution 0.25°, that is a 720 x 1440 array of centered values.</p> <p>For both SO2 and SA OD, the values are in the ‘data’ variable. The variable ‘qa_value’ is a quality control value used to screen values for plotting; 0 means do not plot; -1 means mask is not defined so the mask is not used (data will be plotted).</p> <p>SO2 units are ppbv (assuming a uniform mixing ratio vertical profile ). SA OD is an optical depth with no unit.</p> <p><strong>Reading software</strong></p> <p>A python package to read and process the data is available at https://github.com/bernard-legras/ASTuS/tree/master/IMS and in the IMS-reader.tgz archive</p>
Regional SO2 removal simulations with HadGEM3-GA4
<p>This dataset contains time-averaged model output from simulations performed with the UK Met Office HadGEM3-GA4 (UM Version 8.2) climate model, investigating the climate response to regional reductions in anthropogenic sulfur dioxide (SO<sub>2</sub>) emissions.</p> <p>The model configuration and experimental setup are described in: Kasoar, M., Shawki D. & Voulgarakis, A. (2018), Similar spatial patterns of global climate response to aerosols from different regions, <em>npj Climate and Atmospheric Science</em>, 1(1), doi:<a href="http://dx.doi.org/10.1038/s41612-018-0022-z">10.1038/s41612-018-0022-z</a><em>.</em> An overview of the experimental setup, experiment identifiers and file naming convention for this dataset, is given below.</p> <p>Simulations were performed as 200-year long coupled atmosphere-ocean simulations. The model was run with repeating year-2000 values for aerosol emissions and greenhouse gas concentrations throughout the simulation. The first 50 years were discarded as spin-up, and 150-year annual and seasonal averages calculated over the remaining simulation period. Data is provided as NetCDF files, containing the 150-year means of all the diagnostics that were output from these model runs.</p> <p>Additionally, 26-year long atmosphere-only simulations with prescribed year-2000 sea surface temperatures (SST) and sea ice concentrations were also performed. For these fixed-SST simulations, the first year was discarded as spin-up and the remaining 25 years averaged over.</p> <p>Six 200-year atmosphere-ocean control simulations were carried out to assess internal variability, with different initial atmospheric states but otherwise identical inputs. Only one 26-year fixed-SST control simulation was performed. For both atmosphere-ocean and fixed-SST setups, a series of perturbation experiments were then carried out in which SO<sub>2</sub> emissions were set up zero over a particular region of the world, while being kept the same as in the control runs elsewhere. Each SO<sub>2</sub> removal experiment was run with one simulation for atmosphere-ocean and fixed-SST setups.</p> <p>File naming convention: [JOBID]_[number of years averaged][season]_avg.nc</p> <p>JOBID is a five-letter run identification number (see below). Number of years averaged over is either 150 (atmosphere-ocean simulations) or 25 (fixed-SST simulations). Season is either 'year' for annual means, or 'djf', 'mam', 'jja', 'son', for December-January-February, March-April-May, June-July-August, or September-October-November seasonal averages, respectively.</p> <p>Experiment identifiers and descriptions are as follows:</p> <p><strong>Atmosphere-ocean simulations:</strong></p> <p>xizko: Base control run (year-2000 aerosol emissions and greenhouse gas concentrations everywhere)</p> <p>xizka: Alternative control simulation with different initial atmospheric state (i.e. perturbed initial conditions). Emissions/GHG concentrations unchanged.</p> <p>xizkp: Alternative control simulation with different initial atmospheric state</p> <p>xkhqb: Alternative control simulation with different initial atmospheric state</p> <p>xkhqc: Alternative control simulation with different initial atmospheric state</p> <p>xkhqd: Alternative control simulation with different initial atmospheric state</p> <p>xizkm: No SO<sub>2</sub> emissions from the northern hemisphere mid-latitudes (30°N-60°N). Outside this region, emissions kept the same as in the control simulations.</p> <p>xizks: No SO<sub>2</sub> emissions from East Asia (105°E-145°E, 20°N-45°N)</p> <p>xizkv: SO<sub>2</sub> emissions removed from North America (235°E-290°E, 30°N-50°N). *For North America, SO<sub>2</sub> emissions were not completely reduced to zero as in the other regions. Instead, emissions were set to zero from the following major emission sectors: energy production, industry, transport, domestic use, and waste. In practice though, this corresponds to a 97% removal of SO<sub>2</sub> emissions over this domain.</p> <p>xizkw: No SO<sub>2</sub> emissions from Europe. For Europe, instead of a latitude-longitude box, the domain was defined following the HTAP Phase II European region definition (described at: http://iek8wikis.iek.fz-juelich.de/HTAPWiki/WP2.1) which follows country borders.</p> <p>xkhqa: No SO<sub>2</sub> emissions from South Asia (70°E-90°E, 10°N-30°N)</p> <p><strong>Fixed-SST simulations:</strong></p> <p>xjnda: Atmosphere-only control run (year-2000 aerosol emissions and greenhouse gas concentrations everywhere, prescribed year-2000 sea surface temperatures and sea-ice concentrations)</p> <p>xjnde: Atmosphere-only; no SO<sub>2</sub> emissions from the northern hemisphere mid-latitudes (30°N-60°N)</p> <p>xjndh: Atmosphere-only; no SO<sub>2</sub> emissions from East Asia (105°E-145°E, 20°N-45°N)</p> <p>xjndp: Atmosphere-only; SO<sub>2</sub> emissions removed from North America (235°E-290°E, 30°N-50°N). *For North America, SO<sub>2</sub> emissions were not completely reduced to zero as in the other regions. Instead, emissions were set to zero from the following major emission sectors: energy production, industry, transport, domestic use, and waste. In practice though, this corresponds to a 97% removal of SO<sub>2</sub> emissions over this domain.</p> <p>xjndq: Atmosphere-only; no SO<sub>2</sub> emissions from Europe. For Europe, instead of a latitude-longitude box, the domain was defined following the HTAP Phase II European region definition (described at: http://iek8wikis.iek.fz-juelich.de/HTAPWiki/WP2.1) which follows country borders.</p> <p>xjndr: Atmosphere-only; no SO<sub>2</sub> emissions from South Asia (70°E-90°E, 10°N-30°N)</p> <p> </p>
ESA SEOM-IAS – Measurement and ACS database SO2 UV region
<p>The database contains measurements and absorption cross sections generated within the framework of the ESA project SEOM-IAS (Scientific Exploitation of Operational Missions - Improved Atmospheric Spectroscopy Databases), ESA/AO/1-7566/13/I-BG. Details on the project can be found at http://www.wdc.dlr.de/seom-ias/.</p> <p>The measurements were recorded at the German Aersopace Center (DLR) to provide a new absorption cross section database for SO2 according to the needs of the TROPOMI instrument aboard the Sentinel 5-P satellite. The data are compiled in two zip files, one for the measurements (SO2_UV_region_measurement_database_20112018.zip), one for the absorption cross sections (SO2_UV_region_absorption_cross_section_database_20112018.zip) and a readme file (ESA_SEOM_IAS_spectra_SO2UVRegion_readme.docx).</p>
SO2 and copper tolerance exhibit an evolutionary trade-off in Saccharomyces cerevisiae
<p>Copper tolerance and sulfite tolerance are two well-studied phenotypic traits of <em>Saccharomyces cerevisiae</em>. The genetic bases of these traits are derived from allelic expansion at the CUP1 locus and reciprocal translocation at the SSU1 locus, respectively. Previous work identified a negative association between sulfite and copper tolerance in <em>S. cerevisiae</em> wine yeasts. Here we probe the relationship between sulfite and copper tolerance and show that an increase in <em>CUP1</em> copy number does not impart copper tolerance in all <em>S. cerevisiae</em> wine yeast. Bulk-segregant QTL analysis was used to identify variance at <em>SSU1</em> as a causative factor in copper sensitivity, which was verified by reciprocal hemizygosity analysis in a strain carrying 20 copies of <em>CUP1</em>. Transcriptional and proteomic analysis demonstrated that <em>SSU1</em> over-expression did not suppress <em>CUP1</em> transcription or constrain protein production but suggested that <em>SSU1</em> overexpression induced sulfur limitation during exposure to copper. Finally, an <em>SSU1</em> over-expressing strain exhibited increased sensitivity to moderately elevated copper concentrations in sulfur-limited medium, demonstrating that <em>SSU1</em> over-expression burdens the sulfate assimilation pathway. Over-expression of MET 3/14/16, genes upstream of H<sub>2</sub>S production in the sulfate assimilation pathway increased the production of SO<sub>2</sub> and H<sub>2</sub>S but did not improve copper sensitivity in an <em>SSU1</em> overexpressing background. We conclude that copper and sulfite tolerance are conditional traits in <em>S. cerevisiae</em> and provide evidence of the metabolic basis for their mutual exclusivity. These findings suggest an evolutionary basis for the extreme amplification of <em>CUP1</em> observed in some yeasts.</p>
O2-O2, SO2, BrO, and IO differential slant column densities (dSCDs) measured by the University of Colorado Multi-AXis Differential Optical Absorption Spectroscopy (CU MAX-DOAS) instrument at Maido Observatory during April 29, 2018 and May 4, 2018
<p>Description: O<sub>2</sub>-O<sub>2</sub>, SO<sub>2</sub>, BrO, and IO differential slant column densities (dSCDs) measured by the University of Colorado Multi-AXis Differential Optical Absorption Spectroscopy (CU MAX-DOAS) instrument at Maido Observatory during April 29, 2018 and May 4, 2018.</p> <p>Instrument: University of Colorado Multi-AXis Differential Optical Absorption Spectroscopy (CU MAX-DOAS)<br> Instrument reference: Coburn et al. (2011); doi:10.5194/amt-4-2421-2011<br> Instrument contact: Christopher F. Lee (christopher.f.lee@colorado.edu)<br> Instrument PI: Rainer Volkamer (rainer.volkamer@colorado.edu)<br> <br> Measurement site: Maido Observatory, Reunion Island<br> Longitude: 55.384 degrees East<br> Latitude: 21.080 degrees South<br> Altitude: 2160 meters above sea level<br> Azimuth angle: Approximately 100 degrees clockwise from north<br> <br> The detection limit is defined as (2*Measured RMS) / (Maximum differential absorption cross section), where RMS = root-mean-square noise of spectral signal not accounted for by DOAS fit parameters [optical density units]. The maximum differential absorption cross sections used are 7.0e-21 [cm<sup>2</sup>] for SO<sub>2</sub>, 2.6e-17 [cm<sup>2</sup>] for BrO, and 3.5e-17 [cm<sup>2</sup>] for IO. Detection limits for SO<sub>2</sub> dSCDs, BrO dSCDs, and IO dSCDs are only reported during periods of significant SO<sub>2</sub> detection. BrO to SO<sub>2</sub> ratios are only reported during periods when both BrO dSCDs and SO<sub>2</sub> dSCDs are above the detection limit.</p> <p>Local time (RET) is UTC+4.<br> <br> Column 1: UTC start datetime (yyyy-mm-dd HH:MM:SS)<br> Column 2: UTC center datetime (yyyy-mm-dd HH:MM:SS)<br> Column 3: UTC stop datetime (yyyy-mm-dd HH:MM:SS)<br> Column 4: Elevation angle above the horizon (degrees)<br> Column 5: O<sub>2</sub>-O<sub>2</sub> dSCD [molec<sup>2</sup> cm<sup>-5</sup>]<br> Column 6: Spectral fit error for O<sub>2</sub>-O<sub>2</sub> dSCD [molec<sup>-2</sup> cm<sup>-5</sup>]<br> Column 7: SO<sub>2</sub> dSCD [molec cm<sup>-2</sup>]<br> Column 8: Spectral fit error for SO<sub>2</sub> dSCD [molec cm<sup>-2</sup>]<br> Column 9: Detection limit for SO<sub>2</sub> dSCD [molec cm<sup>-2</sup>]<br> Column 10: BrO dSCD [molec cm<sup>-2</sup>]<br> Column 11: Spectral fit error for BrO dSCD [molec cm<sup>-2</sup>]<br> Column 12: Detection limit for BrO dSCD [molec cm<sup>-2</sup>]<br> Column 13: IO dSCD [molec cm<sup>-2</sup>]<br> Column 14: Spectral fit error for IO dSCD [molec cm<sup>-2</sup>]<br> Column 15: Detection limit for IO dSCD [molec cm<sup>-2</sup>]<br> Column 16: Ratio of BrO dSCDs to SO<sub>2</sub> dSCDs<br> Column 17: Error in ratio of BrO dSCDs to SO<sub>2</sub> dSCDs</p>
SO2 and copper tolerance exhibit an evolutionary trade-off in Saccharomyces cerevisiae
Open the record for dataset details and reuse information.
Elucidating the reaction mechanism of SO2 with Cu-CHA catalysts for NH3-SCR by X-ray absorption spectroscopy
<p>Dataset related to the article with the same title and authors:</p><p>https://pubs.rsc.org/en/content/articlelanding/2023/SC/D3SC03924B#fn1</p><p>dat files corresponding to the spectra reported in the article. See the article for the description of the procedures and of high and load loading catalysts</p><p> </p><p> </p>
IMS/IASI SO2 total column and SA OD monthly means gridded data
<p>Datasets in support of the manuscript: https://essopenarchive.org/doi/full/10.22541/essoar.169091894.48592907 (in revision for GRL).</p><p>Climatology 2008-2019: MM_ims_metopa_tir_qnrt_{so2,aot0}_day_global_g0.5_qc0.nc</p><p>2022: 2022MM_ims_metopb_tir_qnrt_{so2,aot0}_day_global_g0.5_qc0.nc</p><p>(MM=month; {so2,aod0}=SO2 total columns or SA OD)</p><p> </p><p> </p>
Mechanism for SO2 poisoning of Cu-CHA during low temperature NH3-SCR
<p>Dataset related to the article with the same title and authors:</p> <p><a href="https://www.sciencedirect.com/science/article/pii/S0021951722005255">Mechanism for SO2 poisoning of Cu-CHA during low temperature NH3-SCR</a></p> <p> </p> <p>The zip file contain selected structures and a README.txt file with additional information.</p> <p> </p>
Sentinel-5p/TROPOMI SO2 Layer Height dataset covering the Raikoke volcanic eruption 2019
<p>Sentinel-5p/TROPOMI SO2 Layer Height product generated by DLR as part of the INPULS project using the retrieval algorithm developed in the framework of the ESA Sentinel-5p Innovations: SO2 LH (S5P+I: SO2LH) project</p> <p>The dataset contains SO2LH results for the timeframe 2019-06-22 until 2019-07-30 covering the eruptive period of the Raikoke volcanic eruption. This dataset was used as input for the paper of Inness et al. "The CAMS volcanic forecasting system utilizing near-real time data assimilation of S5P/TROPOMI SO2 retrievals" (2021, submitted to GMD)</p> <p>The dataset contains modified Copernicus Sentinel data processed by DLR</p>
Enhanced oxidation of SO2 by H2O2 during haze events: constraints from sulfur isotopes
<p>Sulfate aerosols play a critical role in atmospheric chemistry and climate change. However, the mechanism of sulfate formation during haze events remains highly uncertain. In this work, sulfur isotopic compositions of SO<sub>2</sub> and sulfate in PM<sub>2.5</sub> samples collected in Tianjin, China as well as a Rayleigh distillation model are used to constrain SO<sub>2</sub> oxidation pathways. Here, we show the sulfur isotopic compositions of SO<sub>2</sub> and sulfate as well as concentrations of major ions in PM2.5. Our results show that a large sulfur isotope fractionation between SO<sub>2</sub> and sulfate observed in the first stage of haze events may be attributed to an enhanced oxidation of SO<sub>2</sub> by H<sub>2</sub>O<sub>2</sub>. This study highlights the important role of SO<sub>2</sub> oxidation by H<sub>2</sub>O<sub>2</sub> during the haze events. In addition, it is particularly important to note that the sulfur isotopic fractionation factors during haze events dispaly a distinct trend to other periods, indicating that this isotopic approach is useful in understanding the sulfate formation during haze events.</p>
PM10, SO2, and NO2 Ambient Air Quality Monitoring Data from India's National Ambient Monitoring Program (NAMP) 2011-2015
<p>India's Central Pollution Control Board (CPCB) operates and maintains the National Ambient Monitoring Program (<a href="https://cpcb.nic.in/about-namp/">NAMP</a>) which includes both continuous and manual ambient monitoring stations. This dataset is a collation of manual monitoring data by day for years 2011, 2012, 2013, 2014, and 2015 for PM10, SO2, and NO2. These stations collect for a maximum of 104 days in a year. This cleaned dataset was utilized for understanding trends and conducting comparisons with modeled concentrations under the APnA city program, published <a href="https://doi.org/10.1016/j.uclim.2018.11.005">here</a> (<a href="https://doi.org/10.1016/j.uclim.2018.11.005">Urban Climate, 2019</a>).<br> <br> Data format - year, month, day, SO2, NO2, PM10, Stn Code, State, City<br> All units - micro-gm/m3 (ug/m3)</p> <p>Official annual summary reports (PDFs) are available <a href="https://cpcb.nic.in/namp-data/">here</a>.</p> <p>For guidelines for ambient and emissions monitoring, summaries of available data, and other resources on monitoring in India, visit <a href="https://urbanemissions.info/resources-energy-emissions-analysis-in-india/#monitoring">https://urbanemissions.info/resources-energy-emissions-analysis-in-india</a></p>
CEDS Gridded SO2 Emissions v_2021_4_21 with Point Sources 0.5 Degrees
<p>Preliminary release of gridded SO2 emissions from 2000-2019 based on the 2021_04_21 CEDS release with direct inclusion of point sources as time series. This release contains global grids at 0.5 degree resolution.</p>
CEDS Gridded SO2 Emissions v_2021_4_21 with Point Sources 0.1 Degrees
<p>Preliminary release of gridded SO2 emissions from 2000-2019 based on the 2021_04_21 CEDS release with direct inclusion of point sources as time series. This release contains global grids at 0.1 degree resolution.</p>
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