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10 results for “Surface NO2”
Dataset for: Importance of satellite observations for high-resolution mapping of near-surface NO2 by machine learning
<p>Dataset for: Importance of satellite observations for high-resolution mapping of near-surface NO<sub>2 </sub>by machine learning</p> <p>This dataset is uploaded as a part of the article by Kim et al. (2021). The dataset is the hourly maps of near-surface nitrogen dioxide (NO<sub>2</sub>) concentrations at 100 m resolution for an Alpine domain (Switzerland and northern Italy, 6-12 °E, 42-48 °N). The dataset is provided per day (24 hours) in a netcdf (*.nc ~550MB). In this work, we have generated NO<sub>2 </sub>hourly maps for Feb. 2019 to May 2020 and, here, we upload for March 2019 only (~16 GB). If you need data for another period of time, please contact Gerrit Kuhlmann (gerrit.kuhlmann@empa.ch) or Minsu Kim (minsu.kim@empa.ch). </p>
Estimated surface NO2 over Europe for the year 2019-2021
<p>These datasets consist of estimated daily surface NO<sub>2</sub> concentrations over Europe at ~1km spatial resolution in tiff file format generated using S-MESH model and is part of the research article https://doi.org/10.1016/j.rse.2024.114321. Files are zipped into 3 folders each corresponding to a year and can be unzipped from command line using "tar -xvzf filename.tar.gz". Each file represents surface NO<sub>2</sub> during the Sentinel-5P satellite overpass time and the file is named based on the date of measurement. Each tiff file is a single band image with an extent of 25°W-42.5°E & 29.9-74.28°N in EPSG:4326 - WGS 84 projection. The surface NO<sub>2</sub> concentrations are estimated using Sentinel-5P TROPOMI tropospheric column density and a XGBoost machine learning model. The overall median absolute error of the model predictions across Europe is 4.43μg/m<sup>3 </sup></p> <p><strong>Summary</strong></p> <ul> <li>Data: NO<sub>2</sub> concentrations over Europe at ~1km spatial resolution</li> <li>Time Period: 2019-2021</li> <li>Methodology: Using Sentinel-5P TROPOMI NO2 and XGBoost </li> </ul> <ul> <li><span>More information in the article </span><span><a href="https://www.sciencedirect.com/science/article/pii/S0034425724003390#s0155" target="_blank" rel="noopener"><span>https://authors.elsevier.com/sd/article/S0034-4257(24)00339-0</span></a></span><span> or </span><span><a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.rse.2024.114321" target="_blank" rel="noopener"><span>https://doi.org/10.1016/j.rse.2024.114321 </span></a></span></li> </ul>
Global Surface Reactive Nitrogen Concentration (NO2 and NH3)
<p>This datesets include global surface reactive nitrogen concentration (NO2 and NH3) using the OMI NO2 (2005-2016) and IASI NH3 (2008-2016). </p>
China's fossil fuel CO2 emissions estimated using surface observations of co-emitted NO2
<p>We employed an EnKF-based Regional Multi-Air Pollutant Assimilation System (RAPAS) to assimilate <em>in-situ</em> NO<sub>2</sub> observations, allowing us to combine observation-constrained NO<em><sub>x</sub></em> emissions co-emitted with FFCO<sub>2</sub> and grid-specific CO<sub>2</sub>-to-NO<em><sub>x</sub></em> emission ratios for inferring daily <strong>FFCO<sub>2</sub> emissions</strong> over China.</p> <p><strong>cnemc_obs.nc </strong>includes assimilated and verified observations.</p> <p><strong>emission.tar.gz</strong> includes inferred daily posterior NO<em><sub>x</sub></em> and FFCO<sub>2</sub> emissions for the year 2016.</p>
Surface Ozone, NO2, and PM2.5 Concentrations Estimated by the Deep Learning model (Air Transformer) based on Satellite data.
<p>Surface ozone, NO2, and PM2.5 concentrations Estimated by the deep learning model (Air Transformer) based on massive ground-level monitoring, satellite observations, meteorological conditions, dynamic industrial emissions, and other ancillary data from May 2018 to June 2021.</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>
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>
Global surface O3, NO2, HCHO, and PM2.5 concentrations estimated from deep learning from 2019 to 2023
Open the record for dataset details and reuse information.
NOx Emission Changes over China during the COVID-19 Epidemic Inferred from Surface NO2 Observations
<p><strong>obs_assimilation.csv </strong>and<strong> obs_verification.csv </strong>include assimilated and verified observations from 7 January ~ 29 February 2020, respectively.</p> <p><strong>emission.tar.gz</strong> includes inferred daily posterior NO<sub>x</sub> emissions from 10 January ~ 29 February 2020.</p>
TROPESS Chemical Reanalysis Surface NO2 2-Hourly 2-dimensional Product V1 (TRPSCRNO22H2D) at GES DISC
The TROPESS Chemical Reanalysis NO2 2-Hourly 2-dimensional Product contains surface concentrations of nitrogen dioxide. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.The data files are written in the netCDF version 4 file format, and each file contains a year of data at 2-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.
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