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Dataset results
6 results for “aerosol sensor”
Single aerosol measurements from a wideband integrated bioaerosol sensor, collected during the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>This data set contains the time series of single particle data, measured by the wideband integrated bioaerosol sensor (WIBS-4, University of Hertfordshire, Hatfield, UK), during the Antarctic Circumnavigation Expedition (ACE), which was conducted between 20th of December 2016 and 19th of March 2017. WIBS provides aerosol optical diameter (5 μm - 14 μm), asymmetry factor and fluorescent signals on three different channels. WIBS measures single aerosol particles at a sampling rate of 125 Hz. More technical details about WIBS could be found in Kaye et al. (2005).</p> <p><strong>Dataset contents</strong></p> <ul> <li>part_1_Cape_Town_Kerguelen.csv, data file, comma-separated values</li> <li>part_2_Kerguelen_Hobart.csv, data file, comma-separated values</li> <li>part_3_Hobart_Mertz.csv, data file, comma-separated values</li> <li>part_4_Mertz_Punta Arenas.csv, data file, comma-separated values</li> <li>part_5_Punta_Arenas_Cape_Town.csv, data file, comma-separated value</li> <li>part_6_Cape_Town_Bremerhaven.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This aerosol measurement dataset collected using a WIBS during ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
MAJA look-up tables for Sentinel-2 A&B sensors, for a continental aerosol model
<p>These are the Look-up tables used by MAJA atmospheric correction software, used to process Sentinel-2 A&B sensors.</p> <p>These look-up tables correspond to a continental model.</p>
MAJA look-up tables for Sentinel-2 A&B sensors, for Copernicus Atmosphere Monitoring Service aerosol types
<p>The archive contains the Look-up tables used by MAJA atmospheric correction software, used to process Sentinel-2 A&B sensors. These look-up tables correspond to the aerosol types used by Copernicus Atmosphere Monitoring Service (CAMS). However, the default continental model is also provided.</p> <p>Version 1.1 has new LUT for water vapour estimates, which corrects for a bias observed for large water vapour contents (above 2.5 g/cm2)</p> <p>Version 1.2 just changed the Folder name for a better integration with Start_maja.</p> <p>Version 1.3 added the Header files</p>
Digitized Raw Output Signals of a Low-cost Aerosol PM Sensor Sharp GP2Y
<p>The data contains the digitized traces of low-cost PM sensor Sharp GP2Y1010AU0F pulse outputs during calibration and environmental measurements. The work was done within the Aeromet EMPIR project 19ENV08.</p> <p>Further details are given in the article:</p> <p>Bučar, K.; Malet, J.; Stabile, L.; Pražnikar, J.; Seeger, S.; Žitnik, M. Statistics of a Sharp GP2Y Low-Cost Aerosol PM Sensor Output Signals. Sensors 2020, 20, 6707. <a href="https://doi.org/10.3390/s20236707">https://doi.org/10.3390/s20236707</a></p> <p>Also see the included README.pdf file.</p> <p> </p>
Data related to manuscript: Comparison of size distribution and electrical particle sensor measurement methods for particle lung deposited surface area (LDSAal) in ambient measurements with varying conditions (accepted for publication in Aerosol Research)
<p>These files include data which was utilised to calculate the results in the manuscript (https://doi.org/10.5194/ar-2024-13). </p>
KORUS-AQ B200 Remotely Sensed Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) Data
KORUSAQ_AircraftRemoteSensing_GeoTASO_B200_Data are remotely sensed data collected by the Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) instrument onboard the B200 aircraft during the KORUS-AQ field campaign. NO2 and HCHO trace gas slant column data are featured in this collection. Data collection for this product is complete.The KORUS-AQ field study was conducted in South Korea during May-June, 2016. The study was jointly sponsored by NASA and Korea’s National Institute of Environmental Research (NIER). The primary objectives were to investigate the factors controlling air quality in Korea (e.g., local emissions, chemical processes, and transboundary transport) and to assess future air quality observing strategies incorporating geostationary satellite observations. To achieve these science objectives, KORUS-AQ adopted a highly coordinated sampling strategy involved surface and airborne measurements including both in-situ and remote sensing instruments.Surface observations provided details on ground-level air quality conditions while airborne sampling provided an assessment of conditions aloft relevant to satellite observations and necessary to understand the role of emissions, chemistry, and dynamics in determining air quality outcomes. The sampling region covers the South Korean peninsula and surrounding waters with a primary focus on the Seoul Metropolitan Area. Airborne sampling was primarily conducted from near surface to about 8 km with extensive profiling to characterize the vertical distribution of pollutants and their precursors. The airborne observational data were collected from three aircraft platforms: the NASA DC-8, NASA B-200, and Hanseo King Air. Surface measurements were conducted from 16 ground sites and 2 ships: R/V Onnuri and R/V Jang Mok.The major data products collected from both the ground and air include in-situ measurements of trace gases (e.g., ozone, reactive nitrogen species, carbon monoxide and dioxide, methane, non-methane and oxygenated hydrocarbon species), aerosols (e.g., microphysical and optical properties and chemical composition), active remote sensing of ozone and aerosols, and passive remote sensing of NO2, CH2O, and O3 column densities. These data products support research focused on examining the impact of photochemistry and transport on ozone and aerosols, evaluating emissions inventories, and assessing the potential use of satellite observations in air quality studies.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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OpenNeuro
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