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74 results for “organic aerosols”
Mass spectrometry-based aerosolomics: a new approach to resolve sources, composition, and partitioning of secondary organic aerosol
<p>Dataset of Thoma et al. published at Atmospheric Measurements and Techniques:</p> <p>Mass spectrometry-based aerosolomics: a new approach to resolve sources, composition, and partitioning of secondary organic aerosol</p>
Organic aerosol sources in Krakow, Poland, before implementation of a solid fuel residential heating ban
<p>Krakow is a pollution hot-spot in Europe which is thought to be caused mainly by a high use of coal combustion (power plants, residential heating). Here, we quantify the impact of coal burning on air quality in the city of Krakow before the use of solid fuels for residential heating was banned within the city of Krakow. The particulate matter (PM) was collected on 126 24-hour filter samples (January to September, both PM<sub>1</sub> and PM<sub>10</sub>, i.e., with an aerodynamic diameter smaller than 1 µm and 10 µm, respectively) and analyzed with an aerosol mass spectrometer and the sources of the organic aerosol (OA) quantified. Secondary OA (SOA) likely from residential heating was the main contributor to winter-time OA (78% in PM<sub>1</sub>, 57% in PM<sub>10</sub>) and was composed of equal parts of fossil and non-fossil emissions. Additionally, fresh solid fuel combustion emissions from residential heating contribute to OA during winter (coal combustion OA (CCOA): 12%, biomass burning OA (BBOA): 3%). While BBOA contributed substantially to water-soluble OA, COOA was found to be water-insoluble and thus not identified as part of water-soluble OA. Together with the fairly low water-solubility of WOOA (29%), this leads to a low overall water-solubility of organic carbon during winter (35%). In contrast, spring and summer were characterized by more soluble organic carbon (71% in PM<sub>1</sub>, 55% in PM­<sub>10</sub>) which was dominated by biogenic sources (non-fossil), i.e., fine biogenic secondary oxygenated OA (summer oxygenated OA (SOOA): 35% in PM<sub>1</sub>) and coarse primary biological OA (PBOA: 54% in PM<sub>10</sub>). Overall, here we provide information on OA’s sources needed to evaluate the success of mentioned efforts to improve air quality in Krakow in future studies.</p>
Scenario Configurations for Simulating Organic Aerosol in Delhi using WRF-Chem and a VBS Approach
<p>Parameter configuration files for a study into the sensitivity of model predictions (in this case WRF-Chem) of organic aerosol mass loadings, and composition, to organic aerosol production processes.</p> <p>The production processes for both anthropogenic (ANTH) and biomass burning (BB) generated organic aerosols are investigated. 5 production processes are perturbed for each, making a total of 10 parameters for the whole study.</p> <p>The production processes are:</p> <ol> <li>VBS aging rate (VBS_AGERATE): the reaction rate of VBS compounds with OH. Expressed as a reaction rate in cm<sup>3</sup> molec.<sup>-1</sup> s<sup>-1</sup>.</li> <li>SVOC volatility distribution (SVOC_VOLDIST): expressed in terms of an equivalent age (dimensionless between 0-1). This is translated using a simple aging model into a volatility distribution for the emitted VBS compounds.</li> <li>SVOC oxidation rate (SVOC_OXRATE): the degree of oxidation that occurs with, or is induced by, each reaction with an OH molecule. Range is 0.075 (one extra oxygen atom) to 0.45 (six extra oxygen atoms).</li> <li>IVOC scaling (IVOC_SC): scaling factor for emissions of IVOC's alongside the SVOC's. Initial IVOC emitted amount is taken to be x1.5 the non-volatile OA mass in the emission inventory. This scaling factor, ranging from 0 to 3, modifies that initial emitted amount, to give the final IVOC fraction to add.</li> <li>SVOC scaling (SVOC_SC): scaling factor for emissions of SVOCs. This applied to the volatility distribution generated from the SVOC volatility distribution. For anthropogenic emissions this ranges from 0.1 to 4. For biomass burning emissions this ranges from 0.5 to 4.</li> </ol> <p>SVOC_VOLDIST, IVOC_SC, and SVOC_SC combine to give the VBS_FRAC_[X] fractional volatility distributions. These volatility bins start at Ci*=-2 , and increase decadally to Ci*=6.</p> <p>The template namelist into which these parameters are inserted is included too. This is for a modified version of WRF-Chem 3.8.1 - it will not work with the standard WRF-Chem model.</p> <p> </p>
Initial application of the noise-sorted scanning clustering algorithm to the analysis of composition-dependent organic aerosol thermal desorption measurements
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Influence of Relative Humidity on the Heterogeneous Oxidation of Secondary Organic Aerosol
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Biogenic secondary organic aerosol participates in plant interactions and herbivory defense
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Non-target screening of a Siberian ice core reveals changes in the pre-industrial to industrial organic aerosol composition
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Effects of the VACES particle concentrator on secondary organic aerosol and ambient particle composition
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Molecular Formulas of Organic Aerosols at Two Sites of East China
<p>In this dataset we present molecular composition of organic aerosols collected at Xiangshan gulf (29°29′ N, 121°46′ E) and an inland urban site (32°20′ N, 118°71′ E) of east China. Compound formula, mass-to-charge ratio, mass error between measured and theoritical values, intensity and adduct form are measured using a FT-ICR-MS with a high size resolution.</p>
Development of a versatile source apportionment analysis based on positive matrix factorization: a case study of the seasonal variation of organic aerosol sources in Estonia
<p>This work presents an advanced source apportionment approach that estimates the uncertainties of the positive matrix factorization model. The selection of the environmentally meaningful solutions is challenging in such models and a systematic approach to identify and sort the factors is important. We applied this method to a novel data set covering one yearly cycle at three sites in Estonia. The main results showed that biomass burning dominates in winter, while biogenic sources prevail in summer.</p>
Data files for the publication "Surfactants Regulate the Mixing State of Organic-Inorganic Mixed Aerosols Undergoing Liquid-Liquid Phase Separation"
<p>Data files for the publication “Surfactants Regulate the Mixing State of Organic-Inorganic Mixed Aerosols Undergoing Liquid-Liquid Phase Separation”</p>
Measurement report: Secondary organic aerosols at a forested mountain site in southeastern China
<p>Supporting dataset for Measurement report: Secondary organic aerosols at a forested mountain site in southeastern China</p>
Model data for "A model study on investigating the sensitivity of aerosol forcing on the volatilities of semi-volatile organic compounds" by Irfan et al
<p><span>Abstract: </span>This dataset contains simulation results from global aerosol-climate model ECHAM-SALSA. These simulations were performed to study the sensitivity of simulated SOA mass, CCN and radiative forcings to the assumed volatility distributions of biogenic SOA precursor species. The study employed volatility basis set (VBS) approach to represent and simulate SOA in the atmosphere. The study involved a comparative analysis between finely resolved 9-bin VBS setup with a simplified 3-bin VBS setup. It also included how the SOA mass, CCN and radiative forcing are sensitive to the volatitility of individual VBS bins.</p> <p><span>Methods: </span>Global scale aerosol-climate model simulations were performed using ECHAM-SALSA. We performed three diferent simulations each using 9-bin and 3-bin VBS setups with the volatilities increased (VBSx10) and decreased (VBSx0.1) by one-order of magnitude with respect to the original volatility (VBSx1). Another set of six different simuations were performed by increasing and decreasing the volatilities of one VBS bin at a time while keeping the original volatilities of other bins.</p> <p><span>TechnicalInfo: </span>In this study, all the ECHAM-SALSA simulations used T63 spectral truncation and 47 hybrid sigma pressure levels in horizontal and vertical resolution respectively. Simulations were performed for the year 2010 with half a year spin-up. The data was simulated with 3-hourly output for the simulation period. We then calculated monthly means of the summer months from 3-hourly data for all the model values except CDNC. We used the the 3-hourly data to analyse CDNC from grids with cloud fraction ≥ 0.95. A detailed description of model simulations is given in the setup file of each simulation.</p> <p><span>TechnicalInfo: </span>The external URL leads to a bucket containing setup files, the complete dataset (post-processed NETCDF files) presented in the manuscript, and the python scripts used for data analysis for each of the simulations.</p>
Equal abundance of summertime natural and wintertime anthropogenic Arctic organic aerosols
<p>Data presented in the main figures of the peer-reviewed journal article "Equal abundance of summertime natural and wintertime anthropogenic Arctic organic aerosols" by Moschos et al.</p>
Data sets used in "Neural network emulation of the formation of organic aerosols based on the explicit GECKO-A chemistry model"
<p>The training, validation, and testing data sets for toluene, dodecane, and alpha-pinene models described in the manuscript. A link to the manuscript will be added here when it becomes available. All trajectories in the data sets were generated using GECKO-A. The source code for using the data sets can be found at https://github.com/NCAR/gecko-ml </p>
Aerosol Particle Hygroscopicity and Aerosol Water Content is Driven by Nitrate and Organics in the Particle Phase in Houston, TX.
<p>Related datasets and codes utilized in preparing the manuscript by the same name, submitted to JGR Atmosphere in June 2024. </p>
Exploration of the influence of environmental conditions on secondary organic aerosol formation and organic species properties using explicit simulations: development of the VBS-GECKO parameterization [Dataset]
<p>This dataset is composed of simulation outputs of the GECKO-A model that were used to optimize the VBS-GECKO SOA parameterization as described in the study :<br> <br> Lannuque, V., Camredon, M., Couvidat, F., Hodzic, A., Valorso, R., Madronich, S., Bessagnet, B., and Aumont, B.: Exploration of the influence of environmental conditions on secondary organic aerosol formation and organic species properties using explicit simulations: development of the VBS-GECKO parameterization, Atmos. Chem. Phys., https://doi.org/10.5194/acp-18-1-2018, 2018.<br> <br> <strong>Please cite the original ACP article when using these data in a publication.</strong></p> <p><br> The paper also contains more information about how these data were obtained.<br> <br> The "readme.txt" file gives quick explanations on how the dataset is formatted and how to read the different files that compose it.<br> <br> Victor Lannuque</p>
Organic aerosol source apportionment in Zurich using extractive electrospray ionization time-of-flight mass spectrometry (EESI-TOF): Part I, biogenic influences and day/night chemistry in summer
<p>Ambient measurement campaign took place during summer 2016 in Zurich. The sources of organic aerosol were disclosed. The novel extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF) could provide direct chemical evidence linking ambient SOA to its precursor emissions including a strong influence of biogenic emissions. Additionally provided some insight into the day/night reaction environment and high-detailed chemical composition.</p>
Secondary organic aerosol formation from smoldering and flaming combustion of biomass: a box model parametrization based on volatility basis set
<p>Here, we studied the aging of emissions from flaming and smoldering-dominated wood fires in three different residential stoves, across a wide range of aging temperatures (-10°C, 2°C and 15°C) and emission loads. Organic gases (OGs) acting as SOA precursors were monitored by a proton transfer reaction time-of-flight mass spectrometer (PTR-TOF-MS), while the evolution 10 of the aerosol properties during aging in the smog chamber was monitored by a high resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS). We developed a novel box model based on the volatility basis set (VBS) to determine the volatility distributions of the oxidation products from different precursor classes found in the emissions, grouped according to their emission pathways and SOA production rates.</p>
Dataset for "Sources of organic aerosols in Europe: A modelling study using CAMx with modified volatility basis set scheme"
<p>Model data for figures in the publication "Sources of organic aerosols in Europe: A modelling study using CAMx with modified volatility basis set scheme"</p> <p>Jiang, J., Aksoyoglu, S., El-Haddad, I., Ciarelli, G., Denier van der Gon, H. A. C., Canonaco, F., Gilardoni, S., Paglione, M., Minguillón, M. C., Favez, O., Zhang, Y., Marchand, N., Hao, L., Virtanen, A., Florou, K., O’Dowd, C., Ovadnevaite, J., Baltensperger, U., and Prévôt, A. S. H.: Sources of organic aerosols in Europe: A modelling study using CAMx with modified volatility basis set scheme, Atmos. Chem. Phys., 2019.</p> <p> All the data are stored in .mat file, and the variable names are self-explanatory.</p> <p> </p> <p> </p> <p> </p>
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OpenNeuro
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