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74 results for “Aerosol particles”
Data for: Automotive braking is a source of highly charged aerosol particles
<p>Although the last several decades have seen a dramatic reduction in emissions from vehicular exhaust, non-exhaust emissions (e.g., brake and tire wear) represent an increasingly significant class of traffic-related particulate pollution. Aerosol particles emitted from the wear of automotive brake pads contribute roughly half of the particle mass attributed to non-exhaust sources, while their relative contribution to urban air pollution overall will almost certainly grow, coinciding with vehicle fleet electrification and the transition to alternative fuels. To better understand the implications of this growing prominence, a more thorough understanding of the physicochemical properties of brake wear particles (BWPs) is needed. Here we investigate the electrical properties of BWPs as emitted from ceramic and semi-metallic brake pads. We show that up to 80% of BWPs emitted are electrically charged, and demonstrate a dependence of charge state on particle size and charge polarity. We find that brake wear produces both positive and negative charged particles that can hold in excess of 30 elementary charges, and show evidence that more negative charges are produced than positive. Our results will provide insights into the currently limited understanding of how BWPs behave in the atmosphere, including future investigations into their atmospheric lifetimes and potential climatic relevance. In addition, our study will inform future efforts to remove BWP emissions before entering the atmosphere by taking advantage of their electric charge.</p>
Complementary aerosol mass spectrometry elucidates sources of wintertime sub-micron particle pollution in Fairbanks, Alaska during ALPACA 2022
Open the record for dataset details and reuse information.
Particle number size distribution of fluorescent, hyper-fluorescent, and total aerosol particles measured during the Antarctic Circumnavigation Expedition.
<p><strong>Dataset abstract</strong></p> <p>This dataset consists of a 5-minute time series of particle number concentration of fluorescent and total aerosol particles that were measured by wideband integrated by aerosol sensor during the Antarctic Circumnavigation Expedition during the austral summer of 2016/2017. For this dataset, aerosol particles within the optical diameter range of 0.5 μm to 14.5 μm were considered. In this dataset, the time periods when samples are likely contaminated by the ship’s exhaust were removed from the time series.</p> <p><strong>Dataset contents</strong></p> <ul> <li>PSD_fluorescent.csv , data file, comma-separated values</li> <li>PSD_hyper_fluorescent.csv , data file, comma-separated values</li> <li>PSD_hyper_tot.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 particle number size distribution of fluorescent, hyper-fluorescent, and total aerosol particles dataset from 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>
Accurate observation of black and brown carbon in atmospheric fine particles via a versatile aerosol concentration enrichment system (VACES)
<p>The processed data used to prepare the paper.</p>
Individual aerosol particle data from Zeppelin Observatory
<p>The excel sheets include individual aerosol particle composition data measured using a scanning transmission electron microscope with an energy-dispersive X-ray spectrometer (STEM-EDS). The samples were collected from Zeppelin Observatory, Svalbard. The data are used in a paper by Adachi et al. submitted to Atmospheric Chemistry and Physics (Composition and mixing state of Arctic aerosol and cloud residual particles from long-term single-particle observations at Zeppelin Observatory, Svalbard).</p>
Modeling the hydrological cycle in the atmosphere of Mars: Influence of a bimodal size distribution of aerosol nucleation particles
<p>Data from figures.</p>
Dataset for "Aerosol mixing state, new particle formation, and cloud droplet number concentration in an urban environment "
<p>Inverted HTDMA data, parcel model input, and parcel model output associated with the manuscript. HTDMA data are from the TRACER campaign and inverted using the LSQ1 or LSQ2 method described in Petters (2021). Model input is a 4 lognormal aerosol mode representation of the aerosol size distribution during TRACER, obtained by fitting measured size distributions obtained from mobility-based and optical-based sensors. Hygroscopicity is assigned for each mode based on observed size-resolved hygroscopicity from the HTDMA measurements. Model output refers to parcel model simulations using the pyrcel model (Rothenberg and Iompar).</p> <p>Petters, M. D.: Revisiting matrix-based inversion of scanning mobility particle sizer (SMPS) and humidified tandem differential mobility analyzer (HTDMA) data, Atmos. Meas. Tech., 14, 7909–7928, https://doi.org/10.5194/amt-14-7909-2021, 2021. </p> <p>Rothenberg, D. and lompar: darothen/pyrcel: Pyrcel v1.3.2, , https://doi.org/10.5281/zenodo.8378595, 2023.563</p>
Visualization of particle-resolved simulations of aerosols on the regional scale
<p><span><span>In this study, we visualize the evolution of aerosol particles over Northern California using simulations from the WRF-PartMC model. We present two days of results, centering the numerical grid on the Bay Area with a horizontal resolution of 4-by-4-kilometers. Each grid cell contains approximately 5,000 computational particles to represent the aerosol.</span></span></p> <p><span><span>Much like snowflakes, aerosol particles exhibit immense diversity in size and chemical composition, continuously evolving as they travel through the atmosphere. Our particle-resolved simulation captures these processes with unprecedented detail, including the aerosol mixing state. This study is pioneering in its use of particle-resolved methods to simulate aerosols on a regional scale.</span></span></p> <div></div> <div> <div> <div></div> </div> </div> <p> </p>
Predominance of Secondary Organic Aerosol to Particle-bound Reactive Oxygen Species Activity in Fine Ambient Aerosol
<p>Reactive oxygen species (ROS) are believed to contribute to the adverse health effects of aerosols. This may happen by inhaled particle-bound (exogenic) ROS (PB-ROS) or by ROS formed within the respiratory tract by certain aerosol components (endogenic ROS). We investigated the chemical composition of aerosols and their exogenic ROS content at the two contrasting locations Beijing (China) and Bern (Switzerland). We apportioned the ambient organic aerosol to different sources and attributed the observed water-soluble PB-ROS to them. The oxygenated organic aerosol (OOA, a proxy for secondary organic aerosol, SOA) explained the highest fraction of the exogenic ROS concentration variance at both locations. We also characterized primary and secondary aerosol emissions generated from different biogenic and anthropogenic sources in smog chamber experiments. The exogenic PB-ROS content in the OOA from these emission sources was comparable to that in the ambient measurements. Our results imply that SOA from gaseous precursors of different anthropogenic emission sources is a crucial source of water-soluble PB-ROS and should be additionally considered in toxicological and epidemiological studies in an adequate way besides primary emissions. The importance of PB-ROS may be connected to the seasonal trends in health effects of PM reported by epidemiological studies, with elevated incidences of adverse effects in warmer seasons, which are accompanied by more intense atmospheric oxidation processes.</p>
Data of Fluorescent Aerosol Particles during the Pallas Cloud Experiment 2022
<p>Overview: This data set contains number concentrations and number size distributions of total aerosol particles (TAPs), fluorescent aerosol particles (FAPs) and highly fluorescent aerosol particles (HFAPs) of diameter 0.5 to 30 µm (optical equivalent diameter) obtained by a Wideband Integrated Bioaerosol Sensor 5/NEO (Droplet Measurement Technologies) during the Pallas Cloud Experiment 2022 (PaCE22) campaign in the sub Arctic region of Finish Lapland. The instrument was operated between 17.09.2022 17:00 UTC and 13.12.2022 21:00 UTC at Sammaltunturi Station (67.9733°N 24.1157°E) at 564 m above sea level in the Pallas Atmosphere-Ecosystem Supersite and was connected to a heated total suspended particles (TSP) inlet.</p> <p>Instrument description: The WIBS samples air with a sample flow rate of 0.3 L/min and uses a 635 nm diode laser to measure the size of single particles between 0.5 and 30 µm and to trigger two xenon lamps with excitation wavelengths of 280 nm and 370 nm. Fluorescence emission intensity is measured in two emission windows of 310 - 400 nm and 420 - 650 nm resulting in three main fluorescence channels: FL1 (excitation 280 nm and emission 310 – 400 nm), FL2 (excitation 280 nm and emission 420 - 650 nm) and FL3 (excitation 370 nm and emission 420 - 650 nm). A particle is considered as fluorescent, if it exceeds the three sigma fluorescence threshold (forced trigger intensity plus three standard deviations) in any channel (FAP). A particle is considered highly fluorescent if it exceeds the nine sigma fluorescence threshold (forced trigger intensity plus nine standard deviations) in any channel (HFAP). Both FAPs and HFAPs are split into 10 types of fluorescent particles each, using the Perring nomenclature (Perring et al., 2015): (H)FAP_ A (FL1 only), (H)FAP_ B (FL2 only), (H)FAP_ C (FL3 only), (H)FAP_ AB (FL1 and FL2 only), (H)FAP_ AC (FL1 and FL3 only), (H)FAP_ BC(FL2 and FL3 only) and (H)FAP_ ABC (FL1 and FL2 and FL3), (H)FAP_ FL1 (A+AB+AC+ABC), (H)FAP_ FL2(B+AB+BC+BC) and (H)FAP_ FL3 (C+AC+BC+ABC). </p> <p>The files are separated by date (in UTC) and are named the following: TUW.WIBS_N.b1.yyyyMMdd.hhmm.csv. The data file processing level is b1. Quality control checks were applied and missing data points or those with bad values were set to -9999.9.</p> <p>References: Perring et al., “Airborne observations of regional variation in fluorescent aerosol across the United States,” J Geophys Res-Atmos, vol. 120, Art. no. 3, 2015.</p>
Data for publication "The surface tension and CCN activation of sea spray aerosol particles"
<p>This repository contains the data of the publication:</p> <p>Title: "The surface tension and CCN activation of sea spray aerosol<br>particles"<br>Authors: Judith Kleinheins, Nadia Shardt, Ulrike Lohmann, and Claudia Marcolli<br>Date: 2024</p> <p>Version v2: updated data (Scrit_results.json) after first round of paper review.</p>
Data of individual aerosol particle compositions using STEM-EDS
<p>Data set of aerosol particle compositions measured using scanning transmission electron microscopy with energy-dispersive X-ray spectroscopy (STEM-EDS) used in a paper by Adachi et al. TEM samples were collected during the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ), the Biomass Burning Observation Project (BBOP), and the Megacity Initiative: Local and Global Research Observations (MILAGRO) campaigns. Here I show all EDS data and the sample average data measured using STEM-EDS.</p>
The Relationship of Aerosols and Ice Nucleating Particles in Beijing - Data
<p>The data of INP concentrations from the CFDC, aerosol size distributions from the APS and mass concentrations of PM10, PM2.5 and PM1 are displayed. The data in episodes are also displayed.</p>
Datasets-Effects of the test aerosol on the performance of periodic technical inspection particle counters
<p>Datasets related to the peer-reviewed publication "Effects of the test aerosol on the performance of periodic technical<br> inspection particle counters" (<a href="https://doi.org/10.1016/j.jaerosci.2023.106182">https://doi.org/10.1016/j.jaerosci.2023.106182</a>)</p>
Individual aerosol particle data from Villum research station and Zeppelin Observatory
<p>The Excel sheets include individual aerosol particle composition data measured using a scanning transmission electron microscope with an energy-dispersive X-ray spectrometer (STEM-EDS). The samples were collected from Villum research station (VRS), Greenland, and Zeppelin Observatory (ZO), Svalbard. The data are used in a paper by Adachi et al. submitted to Atmospheric Environment (Composition and mixing state of individual aerosol particles from northeast Greenland and Svalbard in the Arctic during spring 2018).</p>
A 1D Model for Nucleation of Ice from Aerosol Particles: An Application to a Mixed-Phase Arctic Stratus Cloud Layer
<p>A 1D Model for Nucleation of Ice from Aerosol Particles:</p> <p>An Application to a Mixed-Phase Arctic Stratus Cloud Layer</p> <p>Daniel A. Knopf<sup>1</sup>*, Israel Silber<sup>2</sup>, Nicole Riemer<sup>3</sup>, Ann M. Fridlind<sup>4</sup>, Andrew S. Ackerman<sup>4</sup></p> <p><sup>1</sup>School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, USA</p> <p><sup>2</sup>Department of Meteorology and Atmospheric Science, Pennsylvania State University, University Park, PA, USA</p> <p><sup>3</sup>Department of Atmospheric Sciences, University of Illinois at Urbana–Champaign, Urbana, IL, USA</p> <p><sup>4</sup>NASA Goddard Institute for Space Studies, New York, NY, USA</p> <p> </p> <p>Corresponding author: Daniel Knopf (daniel.knopf@stonybrook.edu)</p> <p> </p> <p><strong>This repository contains all model output data in netCDF format to reproduce simulation results and corresponding figures given in above listed publication.</strong></p> <p>File name description:</p> <p>Type of parameterization: <em>INN, INAS, ABIFM</em></p> <p>INP treatment, diagnostic or prognostic: <em>diag, prog</em></p> <p>Aerosol size distribution:</p> <p><em>05mu</em>: monodisperse 0.5 μm diameter</p> <p><em>15mu</em>: monodisperse 0.5 μm diameter</p> <p><em>polydisperse</em>: polydisperse particle size distribution</p> <p>Model initialization and thermodynamic data: <em>model_data</em></p> <p>Aerosol/INP data: <em>aerosol_data</em>, <em>polydisperse_data</em></p> <p>Change in ice nucleation efficiency: <em>10X, 100X</em></p>
Enrichment of calcium in sea spray aerosol: Insights from bulk measurements and individual particle analysis during the R/V Xuelong cruise in the summertime Ross Sea, Antarctica
<p>This dataset is alout a paper that entiled "<strong>Enrichment of calcium in sea spray aerosol: Insights from bulk measurements and individual particle analysis during the R/V <em>Xuelong</em> cr</strong><strong>uise </strong><strong>in the summertime Ross Sea, </strong><strong>Antarctica</strong>".</p>
The Effects of Filter During CPET on WOB and Aerosol Particle Concentrations
ClinicalTrials.gov study NCT04526925. IPD Sharing: NO. Countries: 1. Publications: 4.
Aerosol Particle Concentrations Among Different Oxygen Devices for Spontaneous Breathing Patients With Tracheostomy
ClinicalTrials.gov study NCT04654754. IPD Sharing: NO. Countries: 1. Publications: 8.
Data from: Efficacy of masks and face coverings in controlling aerosol particle emission from expiratory activities
Open the record for dataset details and reuse information.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.