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126 results for “no2”
Outputs of NO2 from GEOS-Chem v13.4.0
<p><br>These ncfiles contain ground and total column NO2 data simulated from GEOS-Chem v13.4.0 in China. They are utilized in the preprint "Observational operator for fair model calibration with ground NO2 measurements" (https://doi.org/10.5194/gmd-2023-216) to ensure reproducibility.</p>
MuAP Spatial distribution of various air pollutants in China at 1 km(NO2 2021-01-01:2023-12-31) (Version1.1)
<p>MuAP Spatial distribution of various air pollutants in China at 1 km(NO2)</p> <p>Multiple air pollutions dataset (MuAP) </p> <p>Time frame: 2021-2023<br>Area: Most of China<br>Resolution: about 1km<br>File storage format: .xz and GeoTIFF<br>Spatial projection: WGS84<br>Daily file name: year_doy.tif (Daily MuAP data volume exceeds Zenodo platform limits. Please contact the author at fjcyfeng@qq.com.)</p> <p>Monthly file name: year_month.tif</p> <p>Yearly file name: year_month.tif</p> <p>Unit: Please divide by 10 when using. (ug/m3)</p> <p>When you download and use our data, please cite:</p> <ol> <li>Chi, Y., Zhan, Y., Wang, K., and Ye, H.: Sequential spatiotemporal distribution of PM<sub>2.5</sub>, SO<sub>2</sub> and Ozone in China from 2015 to 2020, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-76, in review, 2023.</li> <li>Chi, Y., Zhan, Y., Wang, K., & Ye, H. (2023). Spatial Distribution of Multiple Atmospheric Pollutants in China from 2015 to 2020. Remote Sensing, 15(24). doi:10.3390/rs15245705</li> </ol> <p>Note: The MuAP for 2015-2020 can be obtained by:</p> <p>1.</p> <ul> <li>PM2.5:https://zenodo.org/records/8093749</li> <li>O3:https://zenodo.org/records/8180923</li> <li>SO2:https://zenodo.org/records/8093749</li> <li>NO2:Please contact the author at fjcyfeng@qq.com.</li> </ul> <p> </p> <p>2. Daily MuAP data volume exceeds Zenodo platform limits. Please contact the author at fjcyfeng@qq.com.</p> <p> </p>
AirGAM 2022r1 NO2 results for all stations 2005-2019
<p>Contains all trend, cross-validation and evaluation results for NO2.</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>
Whittier_CH2O_NO2_MAXDOAS_20200326
<div>INSTRUMENT: Airyx Skyspec Compact 150</div> <div>PLATFORM: Whittier College, Science and Learning Center</div> <div>LOCATION: Latitude: 33.97680° North</div> <div> Longitude: 118.03040° West</div> <div> Elevation: 130.0 Meters</div> <div>AUTHORS: Peter Peterson</div> <div> Department of Chemistry</div> <div> Whittier College</div> <div> 13406 E. Philadelphia St. Whittier, CA 90602, USA</div> <div> email: ppeterso@whittier.edu</div> <div> phone: +1 (562) 907-5149</div> <div> </div> <div>The files are comma-delimited text files with explanatory column headers defined below. </div> <div> </div> <div>We welcome collaborations and are excited about working with others to make discoveries regarding atmospheric chemistry in eastern Los Angeles county. These data are ground-based NO2 and HCHO remote sensing measurements collected by the Peterson group at Whittier College. If the data are downloaded for the purpose of scientific publication, we should be informed of the possible use of the data so we work with this group and ensure the data are being used appropriately. If the work leads to publishable findings, we will work with collaborating scientists to determine if co-authorship or acknowledgement is appropriate. </div> <div> </div> <div>All date and time stamps are in UTC and are the starting time of the retrieval period which utilizes spectra collected over 30 min; hence the times are the midpoint of the measurement. </div> <div> </div> <div>These data files contain NO2 and HCHO retrievals from spectra collected using a commercial MAX-DOAS instrument. This instrument and DOAS fitting procedure are detailed in:</div> <div> </div> <div>The retrieval of vertical profiles from spectral data is done using HEIPRO which is described in: </div> <div>Yilmaz, S. (2012). Retrieval of Atmospheric Aerosol and Trace Gas Vertical Profiles using Multi-Axis Differential Optical Absorption Spectroscopy. University of Heidelberg. Retrieved from http://archiv.ub.uni-heidelberg.de/volltextserver/id/eprint/13128</div> <div> </div> <div> <p>The file columns and units are described below: </p> <p><br>== Column descriptions for metadata ==<br>Date: MM/DD/YYYY HH:MM (UTC)<br>(NO2 or HCHO)_VCD,molec cm^-2, Vertical Column Density<br>Err_(NO2 or HCHO)_VCD,molec cm^-2,UncertaintyData,Vertical Column Density<br>surf_vmr,ppbv,NO2 or HCHO,Nitrogen Dioxide or Formaldehyde Volume Mixing Ratio<br>vmr_m_err,ppbv,UncertaintyData,Volume Mixing Ratio<br>DOFS, integer, Degrees of Freedom<br>f_two, dimensionless, fraction of retrieved column in lowest 200 m<br>prof, ppmv, vertical profile of average vmr in 40 100 m layers</p> </div>
Dataset for " Direct or Indirect Sonication in Ecofriendly MoS2 Dispersion for NO2 and NH3 Gas-Sensing Applications
<p>This zip file contains the opj data of the published paper entitled: Direct or Indirect Sonication in Ecofriendly MoS2 Dispersion for NO2 and NH3 Gas-Sensing Applications published in ACS Omega 2024, 9, 23, 2597-25308</p> <p>DOI: <a title="DOI URL" href="https://doi.org/10.1021/acsomega.4c03166">10.1021/acsomega.4c03166</a></p> <p> </p>
Inferring Surface NO2 over Western Europe: A Machine Learning Approach with Uncertainty Quantification
<p>The data that serves to substantiate the analysis presented in the article.</p>
Rare Nature of Crystals of the Novel Multimeric Haloalkane Dehalogenase DpaA from Paraglaciecola agarilytica NO2.
<p>Manuscript reports on novel haloalkane dehalogenase DpaA isolated from a psychrophilic and halophilic bacterium <em>Paraglaciecola agarilytica</em> NO2 that was crystallized and two independent crystallization and data collection experiments resulted in several data sets with a resolution ranging from 2.2 to 3.7 Å. X-ray diffraction data analysis hinted at oligomeric nature of DpaA leading to certain issues with the solution of structure. Our results unveil a rare example of an oligomeric dehalogenase belonging the subfamily I as well as highlight new findings in the solution of crystallographic data with unusual crystal packing. The crystal structures of HLD-I subfamily already deposited into the PDB database are monomeric. There are only a few examples of oligomeric haloalkane dehalogenases from the other subfamilies HLD-II and HLD-III that can form dimers and tetramers. The crystal structures of enzymes from the HLD-III subfamily showing oligomeric properties in gel-filtration experiments have not been solved to date. DpaA’s propensity to oligomerization likely causes the problems with solution of the structure, such as pseudo-translation, in a wide range of crystallization conditions and across various crystal lattices. Our results contribute to explaining new findings in solving the structures of oligomeric proteins that exhibit unusual crystal packing and mark the DpaA enzyme as an uncommon oligomeric example of the HLD-I subfamily.</p>
Dataset for "Spaceborne observations of lightning NO2 in the Arctic"
<p>Core data used in <a href="https://github.com/zxdawn/S5P-LNO2-Notebook">S5P-LNO2-Notebook</a> repository for <a href="https://doi.org/10.1021/acs.est.2c07988">Zhang et al. (2023)</a>.</p> <p>For the full S5P LNO2 product (June-August, 2019-2021), please check the Arctic lightning NO2 product (<a href="https://doi.org/10.5281/zenodo.7547817">2019</a>, <a href="https://doi.org/10.5281/zenodo.7547819">2020</a>, <a href="https://doi.org/10.5281/zenodo.7547825">2021</a>).</p> <p><strong>Reference</strong></p> <p>Zhang et al., <strong>Spaceborne observations of lightning NO<sub>2</sub> in the Arctic</strong>, <em>Environ. Sci. Technol</em>.</p> <p><strong>Date files</strong></p> <ul> <li> <p>CAMS</p> <ul> <li> <p>CAMS anthropogenic, ship, and soil NOx emissions (2018)</p> </li> </ul> </li> <li> <p>clean lightning</p> <ul> <li> <p>Two clean lightning cases of S5P LNO2 product.</p> <p>Because it is ~1G per file, I just uploaded two files.</p> </li> </ul> </li> <li> <p>era5</p> <ul> <li> <p>Monthly CAPE (2019-2021 summer)</p> </li> </ul> </li> <li> <p>GFAS</p> <ul> <li> <p>GFAS wildfire NOx emission (2018-2021)</p> </li> </ul> </li> <li> <p>gld360</p> <ul> <li> <p>See <a href="https://doi.org/10.5281/zenodo.7528016">10.5281/zenodo.7528016</a> (need request)</p> </li> </ul> </li> <li> <p>lno2</p> <ul> <li> <p>Lightning NO2 emission product</p> <ul> <li> <p>LNO2_emiss.nc</p> </li> <li> <p>LNO2_emiss_area.nc</p> </li> </ul> </li> <li> <p>LNOx profile of Luo et al. 2016</p> </li> <li> <p>Gridded 0.1 x 0.1 LNO2 product</p> <ul> <li> <p>S5P_LNO2_grid.nc</p> <p>vars: lno2, lno2_max, lno2_sum, lightning, and lightning_500hpa</p> </li> </ul> <ul> <li> <p>S5P_LNO2_grid_product.nc</p> <p>vars: no2, lno2, lightning_counts, and cloud_pressure_crb</p> </li> </ul> </li> <li> <p>LNO2 lifetime products</p> <ul> <li> <p>S5P_LNO2_lifetime.csv</p> </li> <li> <p>S5P_LNO2_lifetime.nc (without lightning data, need request for original data)</p> </li> </ul> </li> <li> <p>LNO2 production products</p> <ul> <li> <p>S5P_LNO2_production.csv</p> </li> <li> <p>S5P_LNO2_production.nc (without lightning data, need request for original data)</p> </li> <li> <p>S5P_LNO2_production_**.csv (sensitivity tests)</p> </li> </ul> </li> <li> <p>Lightning within TROPOMI swath</p> <ul> <li> <p>swath_lightning_**.csv (need request)</p> </li> </ul> </li> </ul> </li> <li> <p>merra2</p> <ul> <li> <p>MERRA2 AOD netcdf files</p> </li> </ul> </li> <li> <p>otd</p> <ul> <li> <p>OTD low resolution and high resolution monthly data</p> </li> </ul> </li> <li> <p>tropomi</p> <ul> <li> <p>Web scrapied S5P-PAL TROPOMI NO2 L2 swath shapes</p> </li> </ul> </li> <li> <p>tropomi_regrid_combine</p> <ul> <li> <p>Regridded summertime TROPOMI NO2 L2 data</p> </li> </ul> </li> <li> <p>viirs</p> <ul> <li> <p>VIIRS fire archive csv data</p> </li> </ul> </li> </ul>
Surface hourly measurement data of O3, NO2 and PM2.5 for "Large modeling uncertainty in projecting decadal surface ozone changes over urban and industrial regions of China"
<p>Surface hourly measurement data of O3, NO2 and PM2.5 during summer of 2017.</p> <p>In the .csv files, the first column contains the ID for each measurement site. "lon", "lat" are longitude and latitude, respectively.</p> <p>Date format is "YYYYMMDD_hour".</p>
Supporting data for "Evaluation of version 3.0B of the BEHR OMI NO2 product"
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BErkeley High Resolution (BEHR) OMI NO2 Prototype High Temporal Resolution Product
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OSIRIS NO2 v6.0.3
<p>A nitrogen dioxide (NO2) profile dataset based on spectrograph data from the Canadian OSIRIS instrument on-board the Swedish Odin satellite.</p> <p>The algorithm relies on spectral fitting to obtain slant column densities of NO<sub>2</sub>, followed by inversion using an algebraic reconstruction technique and the SASKTRAN spherical radiative transfer model (RTM) to obtain vertical profiles of local number density.</p>
Performance of Fe-N/C oxygen reduction electrocatalysts towards NO2-, NO, and NH2OH electroreduction – from fundamental insights into the active centre to a new method for environmental nitrite destruction
<p>This Excel data file contains the data used to produce the figures in the paper:</p> <p>Malko, D., Kucernak, A and Lopes, T, " Performance of Fe-N/C oxygen reduction electrocatalysts towards NO2-, NO, and NH2OH electroreduction – from fundamental insights into the active centre to a new method for environmental nitrite destruction"</p> <p>Journal of the American Chemical Society, 2016, DOI:10.1021/jacs.6b09622</p> <p>Please cite the above reference if you wish to use this data</p>
Pict warrior No2
A model of pict warrior. Leather helmet, green tunik, leather belt. Holding spear, shield and battle-axe. In an attacking pose. The model is suitable for example for battle simulation. Source: Objaverse 1.0 / Sketchfab
Ground-based vertical observations of NO2 and HCHO in Guangzhou
<p>The NO2 and HCHO vertical profiles supporting the paper entitled Diagnosis of ozone formation sensitivities in different height layers via MAX-DOAS observations in Guangzhou.</p>
Global surface O3, NO2, HCHO, and PM2.5 concentrations estimated from deep learning from 2019 to 2023
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Gas cooking impact on indoor NO2 pollution
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Mobile MAX-DOAS measurements for emission quantifications and source analysis of NO2, HCHO and HONO during Chengdu 2021 FISU World University Games
<p>The shared data includes the spatial distribution of NO2, HCHO and HONO measured by moving MAX-DOAS during the Chengdu 2021 FISU World University Games</p>
NO2 gas sensing properties of hydrothermally synthesized WO3.nH2O nanostructures
<p><span>Nitrogen dioxide (NO<sub>2</sub>) has been identified as a serious air pollutant that threats to our environment, human life and world ecosystems. Therefore, detection of this air pollutant is crucial. Metal oxide semiconductor (MOS) is one of the best approaches frequently used to detect NO2 at relatively low temperatures. Hydrated tungsten trioxide (</span><span>WO<sub>3</sub>•nH<sub>2</sub>O</span><span>), an n-type semiconductor, is regarded to be a promising material for fabricating gas sensors, which are widely utilized in environmental and safety monitoring. In this work, </span><span>WO<sub>3</sub>•nH<sub>2</sub>O</span><span> nanoparticles have been synthesized using a polyfunctional surfactant-mediated hydrothermal approach in the addition of H<sub>2</sub>C<sub>2</sub>O<sub>4</sub> and K<sub>2</sub>SO<sub>4</sub> at a molar ratio of 1:1. This paper has also reported the effect of reaction temperature (120°C to 200</span><span>°</span><span>C) on morphological changes and gas sensing performance. The characterization of these synthesized nanostructures was carried out by UV–Vis absorption spectroscopy (UV-Vis), X-ray diffraction (XRD) and field emission scanning electron microscopy (FESEM). The UV absorption peak was obtained around 300 nm. FESEM analysis showed sheet-like structures come together to form flower-type morphology. The synthesized WO<sub>3</sub>•nH<sub>2</sub>O flower-like structures was then used for </span><span>NO<sub>2</sub></span><span> gas sensing application. The prepared sensors showed considerably better sensor response (R<sub>g</sub>/R<sub>a</sub>=17.48) at 185°C for 25 ppm </span><span>NO<sub>2</sub></span><span>. </span></p>
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