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21 results for “ultrafine particles”
Ultrafine particle number concentration observations perpendicular to a main street in Berlin, Germany, in summer 2017
<p>The data set was recorded in summer 2017 in Berlin, Germany. It was the basis of the paper:</p> <p>Fritz, S., Schubert, S., and Schneider, C. (2021). Measurements of spatial variability of sub-micron particle number concentrations perpendicular to a main road in a built-up area. metz. 30, 315–331. doi:10.1127/metz/2021/1058</p> <p>The measurement setup, study site, and devices used are described more detailed in the paper.</p> <p>The data sets uploaded include observations of particle number concentrations, wind direction and wind speed at ground level as well as traffic count data. The time stamp is in local time for Berlin/Germany.</p> <p>The data sets:</p> <p>coordinates.csv contains the coordinates of the measurement points used in file obs_pnc_wind. The column ‘distance’ links these two files. The values in the column ‘distance’ are the distance from the curb of the main road. The distance ‘-10’ represents the measurement point on the central strip of the main road. The two columns ‘latitude’ and ‘longitude’ provide the coordinates of the measurement points.</p> <p>obs_pnc_wind contains the observation data for particle number concentration (PNC), wind speed, and wind direction. PNC was recorded in #/cm³ with a TSI 3007 CPC at a 1-second resolution. Wind speed `ws’ [m/s] was recorded with a KESTREL 5000 with a 2-second resolution. Wind direction ‘wdir’ [°] was manually recorded as the most prominent wind direction within the 3-minute measurements per measurement point in angles of 10°. Wind direction was changed to NA (wdir_comment > 0), if it could not be determined either due to too calm wind (wdir_comment = 1) or with continuously changing wind direction (wdir_comment = 2). Along the measurement route, sometimes additional sources occurred and were recorded in the variable ‘source_type’ at the time they passed by the measurement device. Variable ‘source_comment’ provides additional information about ‘source_type’ where necessary.</p> <p>traffic_count contains the vehicles counted on the main street (Straße des 17. Juni) for each of the 72 runs. It was calculated as [vehicles/hour] based on 5-minute traffic counts. During the traffic counts, we differentiated between smaller (light duty) vehicles (<3.5 t, cars, vans, motorcycles, scooters) and bigger (heavy duty) vehicles (> 3.5 t, trucks and busses).</p>
Dataset for 'Inland ship emissions and their contribution to NOx and ultrafine particle concentrations at the Rhine'
<p>Dataset for ACP manuscript 'Inland ship emissions and their contribution to NOx and ultrafine particle concentrations at the Rhine'. Further explanations on the datasets can be found in the 'README.txt' file. In case of any questions please contact: Philipp Eger (eger@bafg.de), Federal Institute of Hydrology.</p> <p>1_Timeseries_Worms</p> <p>Description: Time series of measured gaseous and particulate species for station "BRI" in Worms on 10 December 2021. </p> <p><br> 2_All_ship_peaks_Worms</p> <p>Description: All analyzed ship peaks fulfilling the quality check criteria. </p> <p><br> 3_Ship_contribution_Worms</p> <p>Description: Mean monthly contribution from shipping based on analyzed peaks. </p> <p><br> 4_Examples_Worms</p> <p>Description: Particle size distribution of five exemplary peaks (A-E).</p>
Ultrafine Particle Dataset Collected by the OpenSense Zurich Mobile Sensor Network
<p><strong>Ultrafine Particle Dataset Collected by the OpenSense Zurich Mobile Sensor Network</strong></p> <p>This dataset contains over 2 and a half years (04/2012-12/2014, >36 Mio samples) worth of ultra-fine particle (UFP) concentration measurements collected by a mobile senor network. The sensors are mounted on top of 10 streetcars in the city of Zurich, Switzerland.</p> <p><strong>Hardware:</strong></p> <ul> <li><strong>Ultrafine particle sensor</strong>: MiniDiSC (see also: Martin Fierz et al. Design, Calibration, and Field Performance of a Miniature Diffusion Size Classifier. Aerosol Science and Technology, Volume 45, 2011.)</li> <li><strong>GPS receiver</strong>: u-blox EVK-6p<br> </li> </ul> <p><strong>Sensor Data<br> ------------------</strong><br> <strong>ufp_data</strong><strong>*.csv column format:</strong></p> <ol> <li>Time of day: yyyy.mm.dd HH:MM</li> <li>Latitude WGS84</li> <li>Longitude WGS84</li> <li>HDOP: horizontal dilution of precision, uncertainty of the GPS position</li> <li>Tram ID</li> <li>Number of particles [#/ccm]</li> <li>Average particle diameter [nm]</li> <li>LDSA: lung deposited surface area [um2 /cm3]</li> </ol> <p><strong>Data quality:</strong><br> The data has been post-processed by performing a periodic null-offset calibration and filtering samples during malfunction.</p> <p><strong>High-Resolution Maps<br> --------------------------------</strong></p> <p>The data has been used to create high-resolution ultrafine particle concentration maps. Four maps, which show the seasonal average particle concentration over seasonal periods, can be found in ufp_seasonal_maps_201204_201304.csv.</p> <p><strong>ufp_map*.csv column format:</strong></p> <ol> <li>Latitude WGS84</li> <li>Longitude WGS84</li> <li>Estimated number of particles [#/ccm]</li> </ol> <p><strong>Map quality</strong></p> <p>Please have a look at the papers in References 1. and 2. (Hasenfratz et al. 2014 and 2015) for a detailed evaluation of the maps.</p> <p><strong>References<br> ----------------</strong><br> The dataset has been used and is described in more detail in the following publications:</p> <ol> <li>David Hasenfratz et al.<em> Pushing the Spatio-Temporal Resolution Limit of Urban Air Pollution Maps.</em> IEEE International Conference on Pervasive Computing and Communications (PerCom). Budapest, Hungary, March 2014. Best Paper Award. </li> <li>David Hasenfratz et al. <em>Deriving High-Resolution Urban Air Pollution Maps Using Mobile Sensor Nodes. </em>Pervasive and Mobile Computing. Elsevier, 2015. </li> <li>David Hasenfratz et al. <em>Demo Abstract: Health-Optimal Routing in Urban Areas.</em> ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN). Seattle, USA, April 2015.</li> <li>Michael Müller et al. <em>Statistical modelling of particle number concentration in Zurich at high spatio-temporal resolution utilizing data from a mobile sensor network. </em>Atmospheric Environment. Elsevier, 2016.</li> </ol> <p>For further information, visit: <a href="http://www.opensense.ethz.ch">http://www.opensense.ethz.ch</a></p>
Data from: Seasonal investigation of ultrafine particle organic composition in an eastern Amazonian rainforest
Open the record for dataset details and reuse information.
Data from: Organic composition of ultrafine particles formed from automotive braking
Open the record for dataset details and reuse information.
Indirect measurements of the composition of ultrafine particles in the Arctic late-winter
Open the record for dataset details and reuse information.
Intercomparison and Evaluation of the TSI NanoScan SMPS and the GRIMM Mini WRAS Ultrafine Particle Size Spectrometers
<p><span>An inter-comparison workshop was held at the World Calibration Center for Aerosol Physics (WCCAP) in Leipzig, Germany, from January 27-31, 2020. Manufacturers and users were invited to have their portable instruments tested and compared against reference instrumentation for particle number size distribution (PNSD) and total particle number concentration (PNC). In particular, the performance and uncertainties of the NanoScan SMPS (Scanning Mobility Particle Sizer) Model 3910 (TSI Inc.) and the Mini Wide Range Aerosol Spectrometer (WRAS) Model 1371 (Grimm Aerosol Technik) were investigated against the WCCAP Mobility Particle Size Spectrometers (MPSS) and Condensation Particle Counters (CPC). A total of 11 TSI NanoScan SMPS and 4 GRIMM Mini WRAS instruments were characterized for ambient aerosols as well as lab-generated aerosols. </span></p> <p><span>The data set represents the respective data for each ambient inter-comparison measurement and performance evaluation using lab generated aerosols such as PSL, NaCl etc.</span></p>
Ultrafine particles and size distributions from airborne experiments
<p>Dataset of ultrafine particles measured during aircraft campaigns with microloght D-MIFU and Dimona VH-OBS and VH-EOS for particle budget studies.</p>
Data from: Nucleation of jet engine oil vapours is a large source of aviation-related ultrafine particles
<p>Data of Figures 1-3</p>
Exposure of Taxi Drivers to Ultrafine Particles and Black Carbon Within Their Vehicles
ClinicalTrials.gov study NCT03839537. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Study to Explore the Effects of Two Hour Inhalation of Ultrafine Carbon Black Particles on Airway Inflammation in Asthmatics
ClinicalTrials.gov study NCT00527462. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Ultrafine Particles and Fetal Contamination
ClinicalTrials.gov study NCT04068389. IPD Sharing: NO. Countries: 0. Publications: 0.
Gene expression profiles in human keratinocytes exposure to ultrafine, fine, and submicron TiO2 particles
GEO Series GSE16425. Homo sapiens. 12 samples. Type: Expression profiling by array.
Impact of sequential exposures to allergens and ultrafine particles on human bronchial epithelial BEAS-2B cells at the air liquid interface
GEO Series GSE216533. Homo sapiens. 18 samples. Type: Expression profiling by array.
Gene expression profiles in rat lung after inhalation exposure to C60 fullerene or ultrafine NiO particles
GEO Series GSE13249. Rattus norvegicus. 6 samples. Type: Expression profiling by array.
Acute Health Effects Due to Ultrafine Particles From Candles and Cooking
ClinicalTrials.gov study NCT04315740. IPD Sharing: NO. Countries: 1. Publications: 0.
Physiological Changes in Adults With Metabolic Syndrome Exposed to Ultrafine Air Particles
ClinicalTrials.gov study NCT01475968. IPD Sharing: Not stated. Countries: 1. Publications: 0.
MDM from COPD patients and healthy subjects after treatment with LPS or fine and ultrafine particles
GEO Series GSE8608. Homo sapiens. 6 samples. Type: Expression profiling by array.
Primary human epithelial cells exposed to coarse, fine and ultrafine particles
GEO Series GSE7010. Homo sapiens. 12 samples. Type: Expression profiling by array.
Endothelial cell culture with Chapel Hill Ultrafine particle
GEO Series GSE4567. Homo sapiens. 8 samples. Type: Expression profiling by array.
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OpenNeuro
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