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535 results for “DUST”
Model Datafiles for Observed seasonal changes in Martian hydrogen chloride consistent with heterogeneous chemistry on atmospheric dust and ice
<p>Datafiles produced by the 1-D photochemistry model used to create the publication "Observed seasonal changes in Martian hydrogen chloride consistent with heterogeneous chemistry on atmospheric dust and ice" - Taysum et al. 2024.</p> <p> </p> <p>matching_orbits_v2.txt : Lists the orbital parameters and water vapour / aerosol file names corresponding to each of the 77 ACS MIR HCl observations that we study.</p> <p> </p> <p><strong>MCD_GlobaMaps.zip</strong> : .nc files containing model output where the model is ran at Longitude 0 degrees, across Ls 0 --> 360 in intervals of 30 degrees, and latitudes 60 S to 60 N in intervals of 15 degrees.</p> <ul> <li>LowTau : model runs with the chlorine heterogeneous chemistry scheme active. <ul> <li>MY34/ : Runs driven with MY34 climatology from the MCDv6.1</li> <li>StandardClim/ : Runs driven with Standard Climatology from the MCDv6.</li> </ul> </li> <li>No_Chlorine : model runs with no chlorine tracers in the model chemistry. <ul> <li>MY34/ : Runs driven with MY34 climatology from the MCDv6.1</li> <li>StandardClim/ : Runs driven with Standard Climatology from the MCDv6.1</li> <li> </li> </ul> </li> </ul> <p><strong>ACS_Comparisons.zip </strong>: .nc files containing the model output for specific ACS MIR HCl observations in Mars Year 34.</p> <ul> <li>LowTau : model runs with the chlorine heterogeneous chemistry scheme active. <ul> <li>MY34/ : Runs driven with MY34 climatology from the MCDv6.1</li> <li>StandardClim/ : Runs driven with Standard Climatology from the MCDv6.1</li> <li>TGO/ : Runs driven with MY34 climatology, and TGO measured water ice, dust, and H2O vapor profiles, driven at the position of the ACS-TIRVIM aerosol retrievals corresponding to the approximately colocated ACS MIR HCl observation</li> </ul> </li> <li>No_Chlorine : model runs with no chlorine tracers in the model chemistry. <ul> <li>MY34/ : Runs driven with MY34 climatology from the MCDv6.1</li> <li>StandardClim/ : Runs driven with Standard Climatology from the MCDv6.1</li> <li>TGO/ : Runs driven with MY34 climatology, driven at the position of the ACS-TIRVIM aerosol retrievals corresponding to the approximately colocated ACS MIR HCl observation</li> </ul> </li> </ul>
Supporting datasets used in the paper entitled "Aircraft-based observation of mineral dust particles over the western North Pacific in summer using a complex amplitude sensor"
<p>This archive contains datasets used in the paper entitled "Aircraft-based observation of mineral dust particles over the western North Pacific in summer using a complex amplitude sensor."</p>
Optical properties of mineral dust aerosols with non-absorptive coating: a numerical investigation
<p>Model data for optical calculations of non-absorptive coated mineral dust aerosol</p> <p>This repository provides data used for plots in a manuscript submitted to Optics Express. The folder contains optical data from calculations performed with the T-matrix code. The subdirectory aspect ratio contains results for eleven aspect ratios with the coating ratio equal to 0.5 stored in respective directories. The directory coating ratio contains results for eleven coating ratios with an aspect ratio of 1.37. The directory naming of folders within the subdirectories derives from the parameter choice as indicated in the manuscript.</p>
Polarization lidar data for detecting dust orientation
<p>Polarization lidar data used in publication:</p> <p>Tsekeri, A., Amiridis, V., Louridas, A., Georgoussis, G., Freudenthaler, V., Metallinos, S., Doxastakis, G., Gasteiger, J., Siomos, N., Paschou, P., Georgiou, T., Tsaknakis, G., Evangelatos, C., and Binietoglou, I.: Polarization lidar for detecting dust orientation: System design and calibration, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2021-30, in review, 2021.</p>
The complex refraction index of mineral dust
<p>The complex refraction index of mineral dust used in the work "Opposite effects of mineral dust sizes and nonsphericity on snow albedo reduction".</p>
Data from 90-day quantitative inhalation toxicology study evaluating the dose-response and fate in the lung and pleura of chrysotile-containing brake dust
<p>The final results from this multi-dose, 90-day inhalation toxicology study in the rat with life-time post-exposure observation have shown a significant fundamental difference in pathological response and tumorgenicity between brake dust generated from brake pads manufactured with chrysotile or from chrysotile alone in comparison to the amphiboles, crocidolite and amosite asbestos.</p> <p>The groups exposed to brake dust showed no significant pathological or tumorigenic response in the respiratory track compared to the air control group at exposure concentrations and deposited doses well above those at which humans have been exposed. Slight alveolar/interstitial macrophage accumulation of particles was noted. Wagner grades were 1–2 (1 = control group), similar to the TiO2 particle control group. Chrysotile was not biopersistent, exhibiting in the lung a deterioration of its matrix which results in breakage into particles and short fibers which can be cleared by alveolar macrophages and which can continue to dissolve. Particle-laden macrophage accumulation was observed, leading to a very-slight interstitial inflammatory response (Wagner grade 1–3). There was no peribronchiolar inflammation, occasional very-slight interstitial fibrosis (Wagner grade 4), and no exposure-related tumorigenic response.</p> <p>The pathological response of crocidolite and amosite compared to the brake dust and chrysotile was clearly differentiated by the histopathology and the confocal analysis. Crocidolite and amosite induced persistent inflammation, microgranulomas, persistent fibrosis (Wagner grades 4), and a dose-related lung tumor response. Confocal microscopy quantified extensive inflammatory response and collagen development in the lung, visceral and parietal pleura as well as pleural adhesions.</p> <p>These results provide a clear foundation for differentiating the innocuous effects of brake dust exposure from the adverse effects following amphibole asbestos exposure.</p>
Ocean-atmosphere changes in the midlatitude North Pacific over the last 330 ka: Dust, biogenic sediment and authigenic uranium accumulation at Shatsky Rise — Dataset
<p><strong>Title</strong>: Ocean-atmosphere changes in the midlatitude North Pacific over the last 330 ka: Dust, biogenic sediment and authigenic uranium accumulation at Shatsky Rise — Dataset</p> <p><strong>Version</strong>: 1.0</p> <p><strong>Date of Release</strong>: July 03, 2022</p> <p><strong>Last Update</strong>: July 03, 2022</p> <p><strong>Identifier</strong>: 10.5281/zenodo.6791726</p> <p><strong>Permalink</strong>: <a href="https://doi.org/10.5281/zenodo.6791725">https://doi.org/10.5281/zenodo.6791725</a></p> <p><strong>Associated publication</strong>: </p> <p><strong>Link to publication preprint</strong>: </p> <p><strong>Suggested citation</strong>: Please reference the associated publication above when using any datasets or materials in this repository.</p> <p><strong>Contact information</strong>: Christopher W. Kinsley, ckinsley@mit.edu OR cwkinsley@gmail.com</p> <p><strong>Dates of data collection and generation</strong>: </p> <p>---------------</p> <p><strong>DESCRIPTION OF DATA</strong></p> <p>This data repository contains the following dataset. We refer the user to the original manuscript (see above) and the text of the Supporting Information published alongside this manuscript for additional general information regarding the collection and generation of these data.</p> <p>DATA TABLES FOR ALL CORE SITES</p> <ul> <li> <p><strong>Kinsley et al. (2022) P&P - Data Tables for ODP 198-1208A</strong><strong> core - v1</strong>: This Excel workbook contains all data used in the study for the ODP 198-1208A core site, taken by the R/V JOIDES Resolution close to the center of the Central High of Shatsky Rise in the western North Pacific Ocean during Ocean Drilling Program Leg 198. This includes the age control and age model, biogenic %s, U-Th isotopic measurements, and <sup>230</sup>Th-normalized flux data. All previously published data is noted as such and referenced.</p> </li> </ul>
Repository data for Characterisation of deposited dust particles on Mars InSight lander ICC lens
<p>This public data repository contains the images, analyses, results and data outputs for generating the figures of the manuscript titled characterisation of deposited dust particles on Mars InSight lander ICC lens.</p>
Surface dust coverages on rock targets in Gale crater: Influence of seasonal wind variability, elevation and proximity to aeolian sand fields.
<p>The following dataset accompanies the paper submission to AGU - JGR: Planets for the paper titled: "</p> <p><span>Surface dust coverages on rock targets in Gale crater: Influence of seasonal wind variability, elevation and proximity to aeolian sand fields."</span></p>
Dataset related to the manuscript Wagner and Schepanski (submitted to JAMES, 2024): "Quantifying fire-driven dust emissions using a global aerosol model"
<p>This dataset belongs to the manuscript of Wagner and Schepanski (2024) entitled "Quantifying fire-driven dust emissions using a global aerosol model" submitted to the "Journal of Advances in Modeling Earth Systems (JAMES)".</p> <p>It contains the for the 10 year simulation period 2004-2013 the monthly, seasonal, or yearly averaged fields of the variables (variable name in brackets) that were used to prepare the plots and statements made in the manuscript. These are in detail:</p> <ol> <li>GFAS input data of FRP (frp)</li> <li>simulated AOD (tau_2d_550nm) and dust AOD (tau_comp_du_550nm)</li> <li>simulated wind-driven (emi_du_dust) and fire-driven (emi_du_fdust) dust emission fluxes</li> <li>simulated atmospheric dust concentration (du_all) including the soluble/insoluble coarse (du_ci, du_cs) and accumulation (du_ai, du_as) mode together with vertical atmospheric pressure levels (pfull)</li> </ol> <p>The simulated results are provided for both simulations, the <strong>control run</strong> without the additional fire-dust emissions and the actual <strong>firedust simulation</strong> with the new fire-dust emission parameterization.</p>
Data presented in Dupont et al. 2024 "Impact of dust source patchiness on the existence of a constant dust flux layer during aeolian erosion events"
<p>Meteorological and dust data used in Dupont et al. 2024. Data are based on measurements recorded above two erosive surfaces from two very different desert regions, one at a high-latitude location in Iceland and the other at a low-latitude location in Jordan. The Iceland campaign was co-organized by two projects: FRAGMENT (FRontiers in dust minerAloGical coMposition and its Effects upoN climaTe) and HiLDA (Iceland as a model for high-latitude dust sources – a combined experimental and <br>modeling approach for characterization of dust emission and transport processes); and the Jordan campaign (J-WADI: Jordan Wind erosion And Dust Investigation) was co-organized by the FRAGMENT and Helmholtz Young Investigator Group ``A big unknown in the climate impact of atmospheric aerosol: Mineral soil dust'' projects.</p>
Unveiling the Milky Way dust extinction curve in 3D
<p>We measure the extinction curves of 220 million stars with Gaia XP spectra and near-infrared photometry from 2MASS and WISE. We use a data-driven model that is developed from <a href="https://ui.adsabs.harvard.edu/abs/2023MNRAS.524.1855Z/abstract">Zhang et al. 2023</a>, with variable extinction curves, to determine stellar parameters and extinction curves simultaneously. For full details of our method, see our corresponding paper*.</p> <p>Here, we provide the resulting catalog of stellar parameters, our trained stellar model, and a few Python scripts that demonstrate how to interact with the catalog and model.</p> <p>See the <code>README.md</code> for more information.</p> <p>* Link of the paper will be posted here once available.</p> <p>Update on 05.04.2024: Uploaded the reference table <code>xi_Rv_ext_curve.h5</code>, which was missing. Updated the script <code>plot_ext_curve.py</code> for a larger range of R(V) .</p> <p>Update on 17.05.2024: Uploaded preliminary maps in <code>Rv_map_new.h5</code>; Uploaded the script <code>example_query.py</code> for examples of querying the map, as well as code for generating sky-view and birds'-eye view pictures. </p>
All-sky three-dimensional dust density and extinction Maps of the Milky Way out to 2.8 kpc
<p>Three-dimensional dust density maps are crucial for understanding the structure of the interstellar medium of the Milky Way and the processes that shape it. However, constructing these maps requires large datasets and the methods used to analyse them are computationally expensive and difficult to scale up. As a result it is has only recently become possible to map kiloparsec-scale regions of our Galaxy at parsec-scale grid sampling. We present all-sky three-dimensional dust density and extinction maps of the Milky Way out to 2.8~kpc in distance from the Sun using the fast and scalable Gaussian Process algorithm Dustribution. The sampling of the three-dimensional map is $l,b,d = 1^{\circ} \times1^{\circ} \times 1.7$~pc. The input extinction and distance catalogue contains 120 million stars with photometry and astrometry from Gaia DR2, 2MASS and AllWISE. This combines the strengths of optical and infrared data to probe deeper into the dusty regions of the Milky Way. We compare our maps with other published 3D dust maps. All maps quantitatively agree at the $0.001$~mag~pc$^{-1}$ scale with many qualitatively similar features, although each map also has its own features. We recover Galactic features previously identified in the literature. Moreover, we also see a large under-density that may correspond to an inter-arm or -spur gap towards the Galactic Centre.</p> <p> </p> <p>Here we provide the full dataset generated from Dustribution covering the full sky out to 2.8 kpc. We provide the 3D dust extinction and density files as well the l,b,d coordinate grid that the data is presented in. </p> <p> </p> <p>The data in each file is as follows:</p> <ul> <li>lbdGrid_l_bounds.pkl.npy: The boundaries of the grid cells of the merged map in l (degrees). Numpy array of size 881.</li> <li>lbdGrid_b_bounds.pkl.npy: The boundaries of the grid cells of the merged map in b (degrees). Numpy array of size 273.</li> <li>lbdGrid_d_bounds.pkl.npy: The boundaries of the grid cells of the merged map in d (pc). Numpy array of size 1644.</li> <li>Density_Median_lbd.pkl.npy: The median of 100 density samples of the merged map (mag pc^-1). Treat this as the estimate of the density. Numpy array of size (880, 272, 1644)</li> <li>Density_16P_lbd.pkl.npy: The 16th percentile of the density of the merged map (mag pc^-1). Treat median-16p as the estimate of the lower uncertainty on the density. Numpy array of size (880, 272, 1644)</li> <li>Density_84P_lbd.pkl.npy: The 84th percentile of the density of the merged map (mag pc^-1). Treat 84p-median as the estimate of the upper uncertainty on the density. Numpy array of size (880, 272, 1644) </li> <li>Ext_Median_lbd.pkl.npy: The median of the integral of 100 sample maps at each cell in the merged map (mag). Treat this as the estimate of the extinction in each cell. Numpy array of size (880, 272, 1644). </li> <li>Ext_16P_lbd.pkl.npy: The 16th percentile of the integral of 100 sample maps at each cell in the merged map (mag). Treat median-16p as the estimate of the lower uncertainty on the extinction in each cell. Numpy array of size (880, 272, 1644)</li> <li>Ext_84P_lbd.pkl.npy: The 84th percentile of the integral of 100 sample maps at each cell in the merged map (mag). Treat 84p-median as the estimate of the upper uncertainty on the extinction in each cell. Numpy array of size (880, 272, 1644)</li> <li>read_and_plot.ipynb: a short jupyter notebook demonstrating how to read in the data files, with some quick examples of plotting them.</li> </ul>
Data for "Unusually Low Dust Activity in North Africa in June 2023: Causes, Impacts and Future Projections"
<p>Datasets for manuscript entitled "Unusually Low Dust Activity in North Africa in June 2023: Causes, Impacts and Future Projections"</p>
Dumps and Data for 'Compaction during fragmentation and bouncing produces realistic dust grain porosities in protoplanetary discs'
<div>This dataset contains the files required to recreate the Phantom simulations run in the publication Michoulier et al. 2024, A&A 688, A31.</div> <div>We have also included the dumps required to re-create Figures 10, 11, 14, 15, 16, D.1 and E.1.</div>
DirtyGrid: 3D dust radiative transfer modeling of spectral energy distributions of dusty stellar populations
<p>Output global SEDs of a large grid of 3D stellar+dust radiative transfer models spanning the range of star formation and dust contents of regions of galaxies.</p> <p>Paper describing the DirtyGrid is Law, Gordo, & Misset (2018, ApJ, submitted)</p> <p>Code to make to access this data at: https://github.com/karllark/pydirtygrid</p>
Dust formation and mass loss around intermediate-mass AGB stars with initial metallicity Zini ≤ 10-4 in the early Universe - I. Effect of surface opacity on stellar evolution and the dust-driven wind
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2017MNRAS.466.1709T">Dust formation and mass loss around intermediate-mass AGB stars with initial metallicity Zini ≤ 10-4 in the early Universe - I. Effect of surface opacity on stellar evolution and the dust-driven wind</a></p>
Influence of Dust Particles on Spatial Distributions of Particles and Fluxes in Positive Column of Glow Discharge.
<p><strong>Figures obtained from numerical simulation and discribe the influence of dust particles on spatial distributions of particles and fluxes in positive column of glow discharge. Table contain a list of reactions used in model. Dataset will be published in the article "Influence of Dust Particles on Spatial Distributions of Particles and Fluxes in Positive Column of Glow Discharge" </strong>C. Yuan, R. Tian, D. V. Bogdanov, E. A. Bogdanov, A. A. Kudryavtsev and Z. Zhou</p>
Dust Devils on Titan
<p>Conditions on Saturn's moon Titan suggest dust devils, which are convective, dust-laden plumes, may be active. Although the exact nature of dust on Titan is unclear, previous observations confirm an active aeolian cycle, and dust devils may play an important role in Titan's aeolian cycle, possibly contributing to regional transport of dust and even production of sand grains. The Dragonfly mission to Titan will document dust devil activity and thereby provide a new window into these features, and our analysis shows that dust devil vortex winds are likely to be modest and pose no hazard to the mission.</p>
Impact of a Bimodal Dust Distribution on the 2018 Martian Global Dust Storm with the NASA/Ames Mars Global Climate Model
<p>This dataset is simulation data from the NASA Ames Mars GCM, produced for the research article titled "Impact of a Bimodal Dust Distribution on the 2018 Martian Global Dust Storm with the NASA/Ames Mars Global Climate Model." It is presented in NetCDF format.</p>
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