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14 results for “Sinking particle”
Dataset for Influence of Aerosol Chemical Composition on Condensation Sink Efficiency and New Particle Formation in Beijing
<p>This dataset includes one year long measurements of particle number size distributions, chemical composition of PM2.5, gaseous precursors, and meteorological parameters in urban Beijing, China, from March 1, 2018, to March 1, 2019. It is the supplementary data for "Influence of Aerosol Chemical Composition on Condensation Sink Efficiency and New Particle Formation in Beijing", which is published by Environmental Science & Technology Letter. Please cite: Wei Du, Jing Cai, Feixue Zheng, Chao Yan, Ying Zhou, Yishuo Guo, Biwu Chu, Lei Yao, Liine M. Heikkinen, Xiaolong Fan, Yonghong Wang, Runlong Cai, Simo Hakala, Tommy Chan, Jenni Kontkanen, Santeri Tuovinen, Tuukka Petäjä, Juha Kangasluoma, Federico Bianchi, Pauli Paasonen, Yele Sun, Veli-Matti Kerminen, Yongchun Liu, Kaspar R. Daellenbach, Lubna Dada, and Markku Kulmala Environmental Science & Technology Letters Article ASAP DOI: 10.1021/acs.estlett.2c00159</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>
FESOM model data and particle tracking data used in publication 'Cross-shelf transport of Barents Sea dense water as a sink for CO2 in the Arctic Ocean'
<p>FESOM model data and particle tracking data used in the paper 'Cross-shelf transport of Barents Sea dense water as a sink for CO2 in the Arctic Ocean" by Andreas Rogge at al.</p> <p>1) FESOM velocity fields averaged over the top 200 m water depth, averaged over the time period 2015-2018.</p> <p>2) FESOM transect at 95°E in the Arctic Ocean (temperature, salinity and velocity), averaged over the time period 2015-2018.</p> <p>3a) Particle back-tracking data based on daily FESOM velocity fields in netcdf format. Particles were released at 95°E every 14 days during the year 2018 and tracked until they reached the surface. Three different constant sinking velocities were used, representative for small and large non-ballasted particles and small ballasted particles.</p> <p>3b) Distribution of particles at the surface for the experiments with three different sinking velocities as mat files. </p>
Data for The effectiveness of coagulation sink of 3-10 nm atmospheric particles
<p>Data for figures 2-7 in the main manuscript; raw time series and metadata</p>
Data for: Fungal parasitism on diatoms alters formation and bio–physical properties of sinking aggregates: Particle analyses
<p>Phytoplankton forms the base of aquatic food webs and element cycling in diverse aquatic systems. The fate of phytoplankton-derived organic matter, however, often remains unresolved as it is controlled by complex, interlinked remineralization and sedimentation processes. We here investigate a rarely considered control mechanism on sinking organic matter fluxes: fungal parasites infecting phytoplankton. We demonstrate that bacterial colonization was promoted 3.5-fold on fungal-infected phytoplankton cells in comparison to non-infected cells in a cultured model pathosystem (diatom <em>Synedra</em>, fungal microparasite <em>Zygophlyctis</em>, and co-growing bacteria), and even ≥17-fold in field-sampled populations (<em>Planktothrix</em>, <em>Synedra</em>, and <em>Fragilaria</em>). The <em>Synedra</em>–<em>Zygophlyctis</em> model system further revealed that fungal infections reduced the formation of aggregates. Moreover, carbon respiration was 2-fold higher and settling velocities 11–48% lower for similar-sized fungal-infected <em>vs</em> non-infected aggregates. Our data imply that parasites can effectively control the fate of phytoplankton-derived organic matter on a single-cell to single-aggregate scale, potentially enhancing remineralization and reducing sedimentation in freshwater and coastal systems.</p>
Data for: Fungal parasitism on diatoms alters formation and bio–physical properties of sinking aggregates: Particle analyses
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Compiled sinking POC flux data collected by particle traps used to evaluate the MSPACMAM
<p>A compilation of 22 vertical profiles of sinking particulate organic carbon (POC) fluxes measured with either free-drifting sediment traps (i.e., surface tethered and neutrally buoyant sediment traps) or Marine Snow Catchers. The compiled raw data were processed as follows: (1) all POC flux observations were binned to the model grid and (2) individual POC flux profiles were normalized by their shallowest flux.</p>
Grouped star formation: converting sink particles to stars in hydrodynamical simulations
<p>This repository contains the essential raw data and codes to recreate the plots in this paper (10.1093/mnras/stab3617; <a href="https://ui.adsabs.harvard.edu/abs/2022MNRAS.510.2657L/abstract">Liow et al. 2022</a>), which contains the following directories and codes:</p> <p><strong>B12/</strong></p> <ul> <li>This directory contains the initial condition (or the snapshot right before star formation) and the snapshot at 0.19 Myr for all the sub-parsec-scale isolated cluster models (see Sections 2.3.1 and 3.1). The only exception is the B12/B12/ subdirectory, which contains the sink particles at 0.19 Myr from the original simulation (Bate 2012). </li> <li>The gas, sink and star files were produced using AMUSE v2021.6.0 (Portegies Zwart et al. 2021; <a href="https://zenodo.org/record/4946130">https://zenodo.org/record/4946130</a>), while the random state and setting files were created using Ekster v1.01 (Rieder and Liow 2021; <a href="https://zenodo.org/record/5520944">https://zenodo.org/record/5520944</a>). See Ekster for more information on how to run the code.</li> </ul> <p><strong>L20/ </strong></p> <ul> <li>This directory contains the initial condition (or the snapshot right before star formation) and the snapshot at 1.75 Myr for all the parsec-scale cloud-cloud collision models (see Sections 2.3.2 and 3.2). The only exception is the L20/L20/ subdirectory, which contains the sink particles at 1.75 Myr from the original simulation (Liow and Dobbs 2020). </li> </ul> <p><strong>J+/</strong></p> <ul> <li>This directory contains the stars at 5 Myr, and stars and sinks at 20 Myr for all the parsec-scale isolated cluster models (see Sections 2.3.3 and 3.3). The only exception is the J+/J+/ subdirectory, which contains the sink particles at 5 Myr and at 20 Myr from the original simulation (Jaffa et al. 2022).</li> </ul> <p><strong>R+/</strong></p> <ul> <li>This directory contains the stars at 1.80 Myr and at 2.40 Myr for the kilo-parsec-scale spiral arm model (see Sections 2.3.4 and 3.4).</li> </ul> <p><strong>L20_Extra/</strong></p> <ul> <li>This directory contains the initial condition (or the snapshot right before star formation) and the snapshot at 1.75 Myr for all the additional parsec-scale cloud-cloud collision models to study the effect of changing random seeds on cluster properties (see Section 4.1).</li> </ul> <p><strong>B12_VG/</strong></p> <ul> <li>This directory contains the initial condition (or the snapshot right before star formation) and the snapshot at 0.19 Myr for all the additional sub-parsec-scale isolated cluster models to study the effect of varying upper star mass limits (see Section 4.2).</li> </ul> <p><strong>L20_VG/</strong></p> <ul> <li>This directory contains the initial condition (or the snapshot right before star formation) and the snapshot at 1.75 Myr for all the additional parsec-scale cloud-cloud collision models to study the effect of varying upper star mass limits (see Section 4.2).</li> </ul> <p><strong>misc/</strong></p> <ul> <li>This directory contains the miscellaneous files to produce some of the plots in the paper. </li> </ul> <p><strong>load_data.py</strong></p> <ul> <li>This python code loads the necessary sink and star files for the code 'plot.py'. </li> </ul> <p><strong>plot.py</strong></p> <ul> <li>This python code recreates the plots in the paper. See individual functions for more information.</li> </ul> <p>Please contact the authors for more information.</p>
Nitrogen isotope ratios of nitrate, ammonium, and amino acids in sinking particles in the Northwestern North Pacific
<p>The database includes sample information and nitrogen isotope measurements of nitrate, ammonium, and amino acids in sinking particles in the Northwestern North Pacific. The sinking particles were collected in sediment traps moored at depths of 200, 500, and 4,810 m at station K2 (47°N, 160°E; water depth, ~5,200 m). The seawater was sampled with a plastic bucket for the surface and Niskin-X sampling bottles for water below the surface at stations K2 and KNOT (44°N, 155°E). The model package of a 1D nitrogen isotope model includes model source code written in Fortran 90, forcing, and initial condition at station K2.</p>
Data From: Active sinking particles: Flow and transport dynamics due to sessile suspension feeders on sinking aggregates
<p>Sinking or sedimentation of biological aggregates plays a critical role in carbon sequestration in the ocean and in vertical material fluxes in waste-water treatment plants. In both these contexts, the sinking aggregates are "active," since they are biological hot-spots and are densely colonized by microorganisms including bacteria and sessile protists, some of which generate feeding currents. However, the effect of these feeding currents on the sinking rates, trajectories, and mass transfer to these "active sinking particles," has not previously been studied. Here we use a novel scale-free vertical-tracking microscope (a.k.a. Gravity Machine, Krishnamurthy et al. "Scale-free vertical tracking microscopy." Nature Methods (2020)) to follow model sinking aggregates (agar spheres) with attached protists (<em>Vorticella convallaria</em>), sinking over long distances while simultaneously measuring local flows. We find that activity due to attached \vortc causes significant changes to the flow around aggregates in a dynamic manner and reshapes mass transport boundary layers. Furthermore, we find that activity-mediated local flows along with sinking modify the encounter and plume cross-sections of the aggregate and induce sustained aggregate rotations. Overall our work shows the important role of biological activity in shaping the near-field flows around aggregates with potentially important effects on aggregate fate and material fluxes.</p>
Data From: Active sinking particles: Flow and transport dynamics due to sessile suspension feeders on sinking aggregates
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Nitrogen isotope ratios of nitrate, ammonium, and amino acids in sinking particles in the Northwestern North Pacific
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Data and code to calculate the encounter frequency between plumes from sinking particles and heterotrophic bacteria in the ocean
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Carbon:234Thorium ratios of sinking particles collected by sediment trap and large particles collected by in situ pump on CCE-LTER Process Cruises. 2006 - 2017 (ongoing).
The ratio of carbon to 234Thorium on particles can be used to convert water column 234Th measurements to sinking carbon flux estimates and to investigate particle scavenging in the ocean. Carbon:234Th ratios of two types of particles are measured: true sinking particles collected by sediment trap and large (>50-µm) particles collected using a McLane in situ Pump. Sinking particles are collected in VERTEX-style particle interceptor traps (PIT) with an 8:1 aspect ratio, 70-mm diameter, and a baffle on top comprised of 13 smaller beveled tubes with a similar 8:1 aspect ratio. Tubes are deployed with a formalin-brine for a duration of 2-5 days. After recovery, samples were gravity filtered through a 200-µm filter. The 200-µm filtered was then immediately examined under a stereomicroscope and mesozooplankton swimmers were removed from the sample. On P0704, P0810, and P1106 cruises the remaining (non-swimmer) portion of the material on the >200-µm filter was then re-combined with the <200-µm portion of the sample. Samples were then filtered through a pre-combusted quartz (QMA) filter. On P1208, P1604, and P1706 cruises the >200-µm and <200-µm fractions were filtered separately onto QMA filters to determine C:234Th ratios of different size classes of sinking particles. Typically, triplicate whole PIT tubes were filtered for C:234Th ratios. However, on some cycles with very high flux, samples were first split on a Folsom splitter. On some cycles, the mesozooplankton ‘swimmers’ were saved and filtered onto a separate QMA filter to quantify the C:234Th ratio of mesozooplankton. QMA filters were all dried and mounted in RISO sample holders. >50-µm particles were also collected using McLane in situ pumps (typically deployed for a period of ~1 hour at the same depth of the sediment traps). In situ pump samples were rinsed off of the 147-mm nitex mesh filters used with the pump onto a pre-combusted QMA filter, which was similarly dried and mounted in a RISO sample holder. Samples
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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