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16 results for “particle growth”
Sub-10 nm size-distribution data for "What controls the observed size-dependency of the growth rates of sub-10 nm atmospheric particles?"
<pre>Size-Distribution data from the CERN CLOUD experiment (Kirkby et al., 2011) measured with a DMA-train (Stolzenburg et al., 2017) Data acquired during the CLOUD10 (Fall 2015) and CLOUD12 (Fall 2017) campaigns. Data associated with the publication Kontkane et al. (2022). File name indicates the Experiment number as specified in Table 3, Kontkanen et al. (2022) and the internal CLOUD run numbers as given in Table S1, Kontaknen et al. (2022). Concentration of precursor gases are also given in these two Tables. Exp. 8 only used data from NAIS and is not included in this repository. Header indicates the diameter at which the size-distribution is measured. First column is time column with areadable timestamp in the format %Y-%m-%d %H:%M:%S. Data is dN/dlog_10 dp in unit cm^(-3). Full size-distribution (up to 400 nm) can be obtained from the author upon request. References: Kontkanen et al. (2022), What controls the observed size-dependency of the growth rates of sub-10 nm atmospheric particles?, Environ. Sci.: Atmos., accepted. Kirkby et al. (2011), Role of sulphuric acid, ammonia and galactic cosmic rays in atmospheric aerosol nucleation, Nature, 476, 429-433, http://dx.doi.org/10.1038/nature10343 Stolzenburg et al. (2017), A DMA-train for precision measurement of sub-10nm aerosol dynamics, Atmos. Meas. Tech., 10, 1639-1651, http://www.atmos-meas-tech.net/10/1639/2017/ </pre>
Particle trajectories - Freilich et al. "Diversity of growth rates maximizes phytoplankton productivity in an eddying ocean"
<p>The files provided here are the offline particle trajectories analyzed in Freilich, Flierl, and Mahadevan “Diversity of growth rates maximizes phytoplankton productivity in an eddying ocean”</p> <p>Both files are sqlite databases containing information about the same particle trajectories which are identified by the variable “iD”</p> <p> </p> <p>ini_day135_z115_forward_biology.db contains nutrient concentration on particle trajectories. A different biological rate lambda is used for each variable denoted NX where X is 0-13. The rates are: 0.015,0.075,0.15,0.3,0.75,1.5,3,10,15,20,50,75,100,120</p> <p>The variable DOY is the model day. </p> <p> </p> <p>ini_day135_z115_physical_forward.db contains the physical variables on particle trajectories. The variables are:</p> <p>x - east-west position</p> <p>y - north-south position</p> <p>z - vertical position</p> <p>u - east-west velocity</p> <p>v - north-south velocity</p> <p>w - vertical velocity</p> <p>vorticity - vertical component of relative vorticity</p>
Simulation data on the growth of atmospheric molecular clusters and particles
<p>This data set contains output data from cluster population simulations performed with Atmospheric Cluster Dynamics Code (ACDC) model, which simulates the formation of clusters from atmospheric vapors and the growth of these clusters by further molecular and cluster-cluster collisions. The data can be used for investigating the formation and growth of atmospheric particles from inorganic and organic vapors.</p> <p>The data is output of a computational process model, and hence does not represent a specific time period or location. Simulation sets are calculated for a one or two-component system containing a quasi-unary inorganic compound representing a mixture of sulfuric acid and ammonia (SA) and/or oxidized organic vapors corresponding to a low volatility organic compound (LVOC) and an extremely-low volatility organic compound (ELVOC). The external conditions in the simulations correspond to those in the CLOUD (Cosmics Leaving Outdoor Droplets) chamber at temperature of 5 C°.</p> <p>Data are provided for 14 simulations.</p> <p><strong>References</strong></p> <p>Kontkanen J, Stolzenburg D, Olenius T, Yan C, Dada L, Ahonen L, Simon M, Lehtipalo K, Riipinen I (2022) What controls the observed size-dependency of the growth rates of sub-10 nm atmospheric particles?. Environ. sci. Atmos. <a href="https://doi:10.1039/d1ea00103e">https://doi:10.1039/d1ea00103e</a> </p> <p>Olenius T, Riipinen I (2017) Molecular-resolution simulations of new particle formation: Evaluation of common assumptions made in describing nucleation in aerosol dynamics models. Aerosol Sci. Tech. 51:397 – 408. <a href="https://doi.org/10.1080/02786826.2016.1262530">https://doi.org/10.1080/02786826.2016.1262530</a></p> <p>Olenius T, Atmospheric Cluster Dynamics Code. <a href="https://github.com/tolenius/ACDC">https://github.com/tolenius/ACDC</a> </p> <p>McGrath MJ et al. (2012) Atmospheric Cluster Dynamics Code: a flexible method for solution of the birth-death equations. Atmos. Chem. Phys. 12:2345 – 2355. <a href="https://doi.org/10.5194/acp-12-2345-2012">https://doi.org/10.5194/acp-12-2345-2012</a></p> <p><strong>Data description</strong></p> <p>The data is in the form of text files. The provided data files (total compressed size ~10GB) correspond to simulation output from the ACDC model. Simulation sets are shown in the table below and further described in Kontkanen et al. (2022). For the interpretation of the model output, the interested user is referred to the manual of ACDC model (<a href="https://github.com/tolenius/ACDC">https://github.com/tolenius/ACDC</a>). </p> <table align="left"> <tbody> <tr> <td> <p>Simulation set</p> </td> <td> <p>Model compounds</p> </td> <td> <p>Vapor concentrations (cm<sup>-3</sup>)</p> </td> <td> <p>Method to retrieve evaporation rates</p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>SA</p> </td> <td> <p><em>C</em><sub>SA </sub>= 8.0*10<sup>6</sup>, 2.0*10<sup>7</sup>, 4.7*10<sup>7</sup>, 1.1*10<sup>8</sup></p> </td> <td> <p>Kelvin eq.<br> <em>(classical evaporation rates)</em></p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>SA</p> </td> <td> <p><em>C</em><sub>SA </sub>= 2.0*10<sup>7</sup>, 4.7*10<sup>7</sup>, 1.1*10<sup>8</sup></p> </td> <td> <p>QC data and Kelvin eq.<br> <em>(non-classical evaporation rates)</em></p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>LVOC</p> </td> <td> <p><em>C</em><sub>LVOC </sub>= 5.0*10<sup>7</sup>, 1*10<sup>8</sup></p> </td> <td> <p>Kelvin eq.<br> <em>(classical evaporation rates)</em></p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>LVOC,<br> ELVOC</p> </td> <td> <p><em>C</em><sub>LVOC </sub>= 5.0*10<sup>7</sup>, 1*10<sup>8</sup><br> <em>C</em><sub>ELVOC </sub>= 1.0 *10<sup>7</sup></p> </td> <td> <p>Kelvin eq.<br> <em>(classical evaporation rates)</em></p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p>LVOC,<br> SA</p> </td> <td> <p><em>C</em><sub>LVOC </sub>= 2.0*10<sup>7</sup>, 5.0*10<sup>7</sup>, 1*10<sup>8</sup><br> <em>C</em><sub>SA </sub>= 8.0*10<sup>6</sup></p> </td> <td> <p>Kelvin eq.<br> <em>(classical evaporation rates)</em></p> </td> </tr> </tbody> </table> <p> </p>
Dataset for "On the potential of the Cluster Ion Counter (CIC) to observe local new particle formation, condensation sink and growth rate of newly formed particles"
<p>Data for Kulmala et al. (2024 )"On the potential of the Cluster Ion Counter (CIC) to observe local new particle formation, condensation sink and growth rate of newly formed particles" (https://doi.org/10.5194/ar-2024-14).</p> <p>Included in the file are number concentrations of sub-2 nm ions and 2-2.3 nm ions measured with Cluster Ion Counter (CIC) and Neutral cluster and Air Ion Spectrometer (NAIS) at SMEAR II station in Hyytiälä, Finland. Concentrations of 1-2 nm ions measured with the NAIS are also included. Sub-2 nm (2-2.3 nm) ion concentrations measured with CIC are refered as Channel 1 (Channel 2-Channel 3) in the .csv file.</p> <p>Contact Santeri Tuovinen (santeri.tuovinen@helsinki.fi) for more details.</p>
Data for the figures in "In-cloud characteristics observed in US Northeast and Midwest non-orographic winter storms with implications for ice particle mass growth and residence time"
<div> <div>This repository contains data shown in the figures in Allen, L. R., Yuter, S. E., Crowe, D. M., Miller, M. A., and Thornhill, K. L.: "In-cloud characteristics observed in US Northeast and Midwest non-orographic winter storms with implications for ice particle mass growth and residence time," to be submitted to Atmospheric Chemistry and Physics.</div> <div> </div> <div> <div> <div>Description of the original data sources: All of the NASA IMPACTS data are archived by GHRC at https://ghrc.nsstc.nasa.gov/uso/ds_details/collections/impactsC.html (McMurdie et al., 2019). The NSF PLOWS 1-second flight-level data are archived by the UCAR Earth Observing Laboratory at https://data.eol.ucar.edu/dataset/113.063 (UCAR/NCAR - Earth Observing Laboratory, 2011). ERA5 hourly data on pressure levels are available from the Copernicus Climate Data Store at https://cds.climate.copernicus.eu/datasets/reanalysis-era5-pressure-levels?tab=overview (Hersbach et al., 2023b). ERA5 hourly single-level data are available from the Copernicus Climate Data Store at https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=overview (Hersbach et al., 2023a).</div> </div> </div> </div>
Dataset for CFD4NRS-9 conference paper "Simulation of noble metal particle growth and removal in the molten salt fast reactor"
<p>Dataset used for the results presented in the CFD4NRS-9 conference paper "Simulation of noble metal particle growth and removal in the molten salt fast reactor". Also the code used to generate results is included (it is also publicly available at https://github.com/edofrederix/flotationFoam), and contains a README with instructions. The plots can be generated with their respective Python files.</p>
Rapid growth of new atmospheric particles by nitric acid and ammonia condensation: data resources
<p>Data presented in the manuscript "Rapid growth of new atmospheric particles by nitric acid and ammonia condensation" currently in review.</p> <p> </p> <p>The manuscript associated with this data was written using results from the CLOUD experiment at CERN, and the author list is a subset of the CLOUD collaboration.</p> <p> </p>
Dataset for "Observations of new particle formation, modal growth rates, and direct emissions of sub-10 nm particles in an urban environment"
<p>Dataset for "Observations of new particle formation, modal growth rates, and direct emissions of sub-10 nm particles in an urban environment". Each directory contains a README.md including descriptions in Markdown format.</p>
Measurement report: The variation properties of aerosol hygroscopic growth related to chemical composition during new particle formation days in a coastal city of southeast China
<p>The aerosol scattering hygroscopic growth factor (f(RH)), along with conventional air pollutants and meteorological parameters were simultaneously observed from February to April 2022 in Xiamen, a coastal city of southeast China.</p>
Dataset for "Rapid initial growth of new atmospheric particles by large nanoparticle concentration gradient"
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Low hygroscopicity of newly-formed particles on the North China Plain and its implications for nanoparticle growth
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Distinct New Particle Formation and Growth Events under Heatwave Weather of Summer 2022 at Urban Chongqing, Southwest China
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Data from: Plastic particles adsorb to the roots of freshwater vascular plant Spirodela polyrhiza but do not impair growth
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Ridge Augmentation Using Allograft Particles Hydrated With or Without Recombinant Human Platelet-Derived Growth Factor
ClinicalTrials.gov study NCT04954664. IPD Sharing: NO. Countries: 1. Publications: 0.
Rapid growth of new atmospheric particles by nitric acid and ammonia condensation: data resources
<p>Data presented in the manuscript "Rapid growth of new atmospheric particles by nitric acid and ammonia condensation" currently in review.</p> <p>The manuscript associated with this data was written using results from the CLOUD experiment at CERN, and the author list is a subset of the CLOUD collaboration</p>
Growth of ice particle mass and projected area during riming
<p>There is a long-standing challenge in cloud and climate models to simulate the process of ice particle riming realistically, partly due to the unrealistic parameterization of the growth of ice particle mass (<em>m</em>) and projected area (<em>A</em>) during riming. This study addresses this problem, utilizing ground-based measurements of <em>m</em> and ice particle maximum dimension (<em>D</em>) as well as theory to formulate simple expressions describing the dependence of <em>m</em> and <em>A</em> on riming. It was observed that <em>β</em> in the <em>m</em> − <em>D</em> power law <em>m</em>  =&thinsp;<em>α</em> <em>D</em><sup><em>β</em></sup> appears independent of riming during the phase 1 (before the formation of graupel), with <em>α</em> accounting for the ice particle mass increase due to riming. This semi-empirical approach accounts for the degree of riming and renders a gradual and smooth ice particle growth process from unrimed ice particles to graupel, and thus avoids discontinuities in <em>m</em> and <em>A</em> during accretional growth. Once the graupel with quasi-spherical shape forms, <em>D</em> increases with an increase in <em>m</em> and <em>A</em> (phase 2 of riming). The treatment for riming is explicit, and includes the parameterization of the ice crystal–cloud droplet collision efficiency (<em>E</em><sub>c</sub>) for hexagonal columns and plates using hydrodynamic theory. In particular, <em>E</em><sub>c</sub> for cloud droplet diameters less than 10 µm are estimated, and under some conditions observed in mixed-phase clouds, these droplets can account for roughly half of the mass growth rate from riming. These physically meaningful yet simple methods can be used in models to improve the riming process.</p>
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