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Dataset results
126 results for “no2”
GlobalNO2_AIT: 0.1° Annual Resolution Global Ground-level NO2 Dataset
<p>The GlobalNO2_AIT dataset provides a comprehensive annual resolution of ground-level nitrogen dioxide (NO2) concentrations at a spatial resolution of 0.1° across global land areas. This dataset is generated using advanced machine learning techniques, integrating various satellite and ground-based observations to enhance accuracy and coverage. It facilitates the study of air quality, atmospheric chemistry, and the impacts of NO2 on human health and the environment. The dataset is essential for researchers, policymakers, and environmental organizations aiming to analyze trends, evaluate pollution mitigation strategies, and model air quality impacts.</p>
MAX-DOAS tropospheric NO2 column measurements in Islamabad, Pakistan (33°N, 73°E) from 2015 to 2019 and comparisons with OMI and TROPOMI satellite data
<p>This data presents an intercomparison of NO<sub>2</sub> retreival settings using Differential Optical Absorption Spectroscopy (DOAS) and those based on literature published over last 20 years. Moreover, it presents comparison of NO<sub>2</sub> Vertical Column Densities(VCD) obtained from ground based MAX-DOAS in Islamabad, Pakistan with satellite data from 2015-2019. MAX-DOAS has retrieved data at seven elevation angles i.e., 2, 4, 5, 10, 15, 30, 45. On the other hand, VCDs are in molecules per cm<sup>2</sup>. However, in order to collect NO2 dataset, DOASIS was used was used to obtain data from MAX-DOAS and further analyzed using QDOAS. Then geometric approximation was applied to obtain VCDs that are presented in this data set.</p>
Dataset - Impact of 3D radiative transfer on airborne NO2 imaging remote sensing over cities with buildings
<p>This dataset was created by Marc Schwaerzel (marc.schwaerzel@empa.ch) and is intended to get along with the Schwaerzel et al. (2021) AMT publication (amt-2020-146) . The data and the data structure is described in the<em> <strong>readme.md </strong></em>text file.</p> <p>The dataset contains:</p> <p>- libRadtran output (radiances and AMFs)</p> <p>- Synthetic SCDs</p>
Data-set of the partial pressure of CO2, dissolved concentrations of CH4, N2O, NO3-, NO2- and NH4+, specific conductivity and water temperature in the rivers and streams of the Napo River basin in Ecuador (2018, 2019, 2020, 2021)
<p>Data-set consists of two files:</p> <p>- data_ghgs.xlsx : Time-stamped and georeferenced data-set of the partial pressure of CO2 (pCO2 in ppm), dissolved CH4 concentration (CH4 in nmol/L), dissolved N2O concentration (N2O in nmol/L), specific (Sp.) conductivity (in µS/cm), water temperature (in °C), dissolved nitrate concentration (NO3- in µmol/L), dissolved nitrite concentration (NO2- in µmol/L), and dissolved ammonia concentration (NH4+ in µmol/L) in the rivers and streams of the Napo River basin in Ecuador (October 2018 and 2019, January 2019 and 2020, April 2019 and 2021, July 2019 and 2020). Gas measurements were made by headspace equilibration directly in the field with a infra-red gas analyser for CO2 and in the lab with a gas chromatograph for CH4 and N2O. NO3-, NO2- and NH4+ were measured with standard colometric procedures. Sampling and analytical protocols are provided here <a href="https://doi.org/10.5194/bg-16-3801-2019">https://doi.org/10.5194/bg-16-3801-2019</a></p> <p>- RiverATLAS.xlsx: hydro-environmental data for the sampled streams extracted from RiverATLAS (https://www.nature.com/articles/s41597-019-0300-6). Data codes and units are available here: https://data.hydrosheds.org/file/technical-documentation/HydroATLAS_TechDoc_v10_1.pdf</p> <p>First column of each of the two files provides station ID allowing to merge both data-sets.</p>
A tetrathiafulvalene salt of the nitrite (NO2−) anion: investigations of the spin-Peierls phase
<p>SQUID and EPR data of the publication</p> <p>Contain Data, metadata, source code and figures</p>
Spatiotemporal Estimation of TROPOMI NO2 Column with Depthwise Partial Convolutional Neural Network
<p>Public Repository of the model outputs of TROPOMI NO2 datasets for 2019 and 2020.</p> <p>Comprises:</p> <p>Saved Partial Convolution Neural Network models (PCNN, PCNN-ST, and DW-PCNN) and code to load the models.</p> <p>Datasets (in Netcdf4 format) from PCNN model outputs, Inverse Distance Weighting, Inverse Distance Weighting with Kriging, spatial coordinates, time, target NO2 for imputation, and masks.</p> <p> </p>
Relation entre l'Indice d'Influence Humaine (IIH) et la variation relative du NO2 troposphérique (ΔNO2) pendant avril 2020 dans les départements du Grand Est (Fig. 4a)
<p>L'IIH est une mesure territoriale de l'impact humain sur les terres, estimée à partir de plusieurs indicateurs anthropiques globaux dont : la pression démographique (densité de population), l'utilisation des terres et les infrastructures (zones bâties, éclairage nocturne, utilisation des terres/couverture des terres) et l'accessibilité (littoral, routes, voies ferrées, rivières navigables). Un modèle de régression a indiqué la forte dépendance de la baisse du NO<sub>2</sub> par rapport à l'IIH lors des mesures de confinement en avril 2020 (R<sup>2</sup> = 0,91) dans les départements du Grand Est.</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>
Dataset parent CARB 19RD004: Daily pollutant concentrations of NO2, PM2.5 and O3 of 100 m resolution for California 2012-2019
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Berkeley High Resolution (BEHR) OMI NO2 - Gridded pixels, daily profiles
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Berkeley High Resolution (BEHR) OMI NO2 - Native pixels, monthly profiles
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Berkeley High Resolution (BEHR) OMI NO2 - Gridded pixels, monthly profiles
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Berkeley High Resolution (BEHR) OMI NO2 - Native pixels, daily profiles
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The Spring Festival Effect: the change of NO2 column concentration in China caused by the migration of human activities
<p>The Spring Festival is the most important holiday in China, human activity and population mobility may contribute greatly to air quality, especially in the megacities. According to the satellite-based tropospheric nitrogen dioxide (NO<sub>2</sub>) column and ground-based observational concentration of NO<sub>2</sub> in the megacities from 2013 to 2018 around the Spring Festival, we found that NO<sub>2</sub> concentration decreases obviously during the Spring Festival and rebounds after the Spring Festival in China, particularly in the megacities. The tropospheric NO<sub>2</sub> columns density around Beijing-Tianjin-Hebei region decreases about 40% than the period before the festival, and it in Beijing decreases by 41.6% and rebounds by 22.3%. While under the Coronavirus disease 2019 (COVID-19) pandemic progresses, the tropospheric NO<sub>2</sub> columns density in Beijing decreases by 56.2% and rebounds only by 6.8% in 2020.</p>
A dataset of ground-based vertical profile observations of aerosol, NO2 and HCHO from the hyperspectral vertical remote sensing network in China (2019-2023)
<p>Vertical <span>profile </span>observations of atmospheric composition are crucial for understanding the generation, evolution, and transport of regional air pollution. However, existing technological limitations and costs have resulted in a scarcity of vertical profil<span>e</span> data. This study <span>introduces </span>a high-<span>time-</span>resolution (approximately 15 minutes) dataset of vertical <span>profile </span>observations of atmospheric composition (aerosols, NO2, and HCHO) conducted using passive remote sensing technology across 32 sites in seven major regions of China from 2019 to 2023. The study meticulously documents the vertical distribution, seasonal <span>variations and </span>diurnal <span>pattern</span> of these pollutants, revealing long-term trends in atmospheric composition across various regions of China. This dataset provides essential scientific evidence for regional environmental management and policy-making. Its sharing <span>would </span>facilitate the scientific community <span>in </span>explor<span>ing</span> of source-receptor relationships, investigating the impacts of atmospheric composition on regional and global climate <span>and </span>feedback mechanisms.</p>
Land use information and NO2 observations of environmental monitoring stations in China
<p>The land use information for environmental monitoring stations in China is stored in the "<i>site_lu.csv" </i>file. The monthly NO2 observations from 2015 to 2017 are stored in the "<i>NO2_ground.csv</i>" file.</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>
Global anthropogenic NOx emissions from 2019 to 2022 based on satellite NO2 observations and GEOS-Chem model
<p><a href="../api/records/10947114/draft/files/emissions.nc/content" target="_blank" rel="noopener noreferrer">emissions.nc</a><a href="10052904"> includes the monthly data of global anthropogenic nox emissions from 2019 to 2022, using the GEOS-Chem model combined with TROPOMI satellite observation data</a></p>
RBE-DS-NO2: A fine-scale NO2 dataset during 2005-2020 in China
<p><span> </span><span>RBE-DS-NO2 is a long-term, high-resolution NO<sub>2</sub> dataset for China. This dataset was generated by employing the robust back-extrapolation via a data augmentation approach (RBE-DA) to ensure the predictive accuracy in back-extrapolation before 2013, and by utilizing an improved spatial downscaling technique (DS) to refine the spatial resolution from 10 km to 1 km. Back-extrapolation validation based on 2005-2012 observations from sites in Taiwan province yielded an<em> R<sup>2</sup></em> of 0.72 and RMSE of 10.7 μg/m<sup>3</sup>, while cross-validation across China during 2013-2020 showed an <em>R<sup>2</sup></em> of 0.73 and RMSE of 9.6 μg/m<sup>3</sup>. </span></p>
HSTCM-NO2
<p><strong>A Global Daily High Spatial-temporal Coverage Merged Tropospheric NO2 dataset (HSTCM-NO2) from 2007 to 2022 based on OMI and GOME-2</strong></p> <p>This dataset is the end product of a reconstruction of global NO2.</p> <p>The product was made using OMI (OMNO2), GOME-2 and other data. The method includes machine learning (XGBoost) and gap filling (DINEOF). The new reconstructed product (HSTCM-NO2) is compared with MAX-DOAS and TROPOMI to establish both concentration validatity, and spatial-temporal coherence.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.