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157 results for “particulate matter”
Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution - Part B - Particle Number Concentrations - Dataset
<p>This repository contains the data used for the analysis of the paper "Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution - Part B - Particle Number Concentrations (PNC)" which is under submission.</p> <p> </p> <p>The experimental conditions and the instruments used are detailed in Bulot, F.M.J.; Russell, H.S.; Rezaei, M.; Johnson, M.S.; Ossont, S.J.J.; Morris, A.K.R.; Basford, P.J.; Easton, N.H.C.; Foster, G.L.; Loxham, M.; Cox, S.J. Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution. <em>Sensors</em> <strong>2020</strong>, <em>20</em>, 2219. https://doi.org/10.3390/s20082219</p> <p>The files are available in .csv and in .rds (for R) formats. For details about the measurement equipment used<br> during this study, please refer to the methods section of the paper.</p> <p> </p> <p>sensors_raw.csv contains the following headers:</p> <ul> <li>Bin0 to Bin15: Alphasense OPC-R1 particle number concentrations for different size bins</li> <li>Bin[1-3-5-7]MToF: mean time of flight of particles within the corresponding size bins of the Alphasense OPC-R1</li> <li>Checksum: checksum of the Alphasense OPC-R1</li> <li>SFR: sample flow rate of the Alphasense OPC-R1</li> <li>Humidity: relative humidity measured by the Alphasense OPC-R1</li> <li>Temperature: temperature measured by the Alphasense OPC-R1</li> <li>SamplingPeriod: sampling period of the Alphasense OPC-R1</li> <li>gr03um, gr05um, gr10um, gr25um, gr50um, gr100um: PNC measured by the Plantower PMS5003</li> <li>n05, n1, n25, n4, n10: PNC measured by the Sensirion SPS30</li> <li>humidity: relative humidity measured by a Sensirion SHT-3x</li> <li>temperature: temperature measured by a Sensirion SHT-3x</li> <li>sensor: id of the sensors</li> <li>site: name of the air quality monitor hosting the sensors</li> <li>exp: name of the experiment conducted</li> <li>source: source used to generate PM (incense or candle)</li> <li>variation: whether the sensors were exposed to stable or peak concentrations of PM pollution</li> <li>date: date in format yyyy-mm-dd HH:MM:SS</li> </ul> <p>For more explanations about the fields of individual sensors, please refer to their manual (Alphasense OPC-R1: https://kolegite.com/EE_library/datasheets_and_manuals/sensors/OPC/072-0500_OPC-R1_manual_issue_1_250219.pdf ; Plantower PMS5003: https://www.aqmd.gov/docs/default-source/aq-spec/resources-page/plantower-pms5003-manual_v2-3.pdf ; Sensirion SPS30: https://sensirion.com/products/catalog/SPS30/)</p> <p> </p> <p>ops.csv and ops.rds contains the readings from the OPS with the following cut sizes for the bins:</p> <ul> <li>Bin 1 Cut Point (um),0.300</li> <li>Bin 2 Cut Point (um),0.374</li> <li>Bin 3 Cut Point (um),0.465</li> <li>Bin 4 Cut Point (um),0.579</li> <li>Bin 5 Cut Point (um),0.721</li> <li>Bin 6 Cut Point (um),0.897</li> <li>Bin 7 Cut Point (um),1.117</li> <li>Bin 8 Cut Point (um),1.391</li> <li>Bin 9 Cut Point (um),1.732</li> <li>Bin 10 Cut Point (um),2.156</li> <li>Bin 11 Cut Point (um),2.685</li> <li>Bin 12 Cut Point (um),3.343</li> <li>Bin 13 Cut Point (um),4.162</li> <li>Bin 14 Cut Point (um),5.182</li> <li>Bin 15 Cut Point (um),6.451</li> <li>Bin 16 Cut Point (um),8.031</li> <li>Bin 17 Cut Point (um),10.000</li> </ul> <p>nanotracer.csv and nanotracer.rds contain the measurements from the Nanotracer:</p> <ul> <li>N.1.: particles/cm3</li> <li>dp_avg.1.: mean diameter of the particles (nm)</li> <li>P.1.:</li> <li>S_al.1.: Lung Deposited Surface Area in um2/cm3</li> </ul> <p> </p> <p>experimental_conditions.csv and experimental_conditions.rds contain the end dates and start dates of each of the experiment conducted.</p> <p> </p> <p> </p> <p>"pm100_cf1","pm10_cf1","pm25_cf1"</p> <p> </p> <p> </p>
Dataset 3 of 8: Particulate matter PM2.5 concentrations in California (2012-2013) (CARB 19RD004)
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Dataset 5 of 8: Particulate matter PM2.5 concentrations in California (2016-2017) (CARB 19RD004)
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Dataset 6 of 8: Particulate matter PM2.5 concentrations in California (2018-2019) (CARB 19RD004)
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Dataset 4 of 8: Particulate matter PM2.5 concentrations in California (2014-2015) (CARB 19RD004)
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Impacts of improved cookstove interventions on personal exposure to carbon monoxide and particulate matter in Zambia
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Time series of environmental parameters and organic matter analyses for dissolved and particulate organic matter in the Neuse River Estuary, North Carolina, USA 2015-2016
Environmental parameters and organic matter analyses (concentration, absorbance, fluorescence) for dissolved and particulate organic matter for the Neuse River Estuary, North Carolina, USA from 20 July 2015-28 July 2016. Samples were collected bi-weekly from July 2015-October 2015 and March 2016-July 2016 and monthly from November 2015-February 2016. The dataset consists of environmental parameters measured (water temperature, salinity, percent dissolved oxygen, turbidity, chlorophyll-a) and calculated (flushing time) as well as organic matter analyses for dissolved and particulate organic matter (concentration, absorbance, fluorescence) from surface (0.2 m below surface) and bottom (0.5 m above bottom) at 11 stations from the furthest extent of saltwater intrusion (Station 0) to the mouth of the estuary (Station 180). Data were collected as part of the Neuse River Estuary Modeling and Monitoring Program (ModMon; http://paerllab.web.unc.edu/projects/modmon/) at the University of North Carolina - Chapel Hill, Institute of Marine Science.
Digitized Particulate Matter Size Distribution Profiles from Literature Sources for Improved Size Representation of PM Emissions in Atmospheric Chemical Transport Models
<p>Processing particulate matter (PM) emissions for use in a chemistry transport model (CTM) such as GEM-MACH (Global Environmental Multiscale Modelling Air-Quality and Chemistry) requires detailed information about particle size distribution and chemical speciation for different PM emissions source types. The current PM size distribution and speciation profile library used at Environment and Climate Change Canada (ECCC) for preparing model-ready emission files for GEM-MACH contains very detailed chemical speciation profiles for PM emissions from 91 source types but only has three generic PM size disaggregation profiles, one each for mobile, point, and area sources. These generic profiles are used to disaggregate bulk PM emissions to a 12-bin sectional size representation, where PM<sub>2.5</sub> emissions are split into size bins 1-8 and PM<sub>10‑2.5</sub> emissions are split into bins 9 and 10. Since there is wide variability in the particle size distribution depending on the source type, the inclusion of source-type-specific PM size disaggregation profiles should lead to better representation of PM particle size for emissions from different source types in the model.</p> <p>A presentation entitled “Expansion of a Size Distribution Profile Library for Particulate Matter (PM) Emissions Processing from Three to 32 Source Categories” was given recently at the Community Modeling and Analysis System (CMAS) conference in Chapel Hill, North Carolina in October 2019 (<a href="https://www.cmascenter.org/conference/2019/slides/1300_zhang_expansion_size_2019.pptx">https://www.cmascenter.org/conference//2019/slides/1300_zhang_expansion_size_2019.pptx</a>) . This presentation described work carried out at ECCC to improve the PM size disaggregation profile library used to generate model-ready emissions. In particular, the number of PM size disaggregation profiles in the library was increased from three generic profiles to 32 source-type-specific profiles. After the conference, four more profiles were added to the library for a total of 36 PM size disaggregation profiles. In order to carry out this study, over 100 PM size distribution profiles from various PM emissions sources were gathered from literature publications, analyzed, and transformed into size disaggregation profiles that correspond to the GEM-MACH 12-bin sectional configuration. The 36 PM size disaggregation profiles that were obtained were then combined with detailed PM chemical speciation data to compile a new PM size disaggregation and chemical speciation library for emissions processing using the SMOKE (Sparse Matrix Operator Kernel Emissions) emissions processing system.</p> <p>This Excel workbook provides the digitized particle size distribution data for PM emissions from 36 different source types that were used as input to calculate the PM size disaggregation profiles for the GEM-MACH 12-bin sectional configuration. The digitized particle size distribution profiles were obtained by digitizing images of size distribution plots obtained from the literature publications using graph digitizing software such as Engauge Digitizer (<a href="http://markummitchell.github.io/engauge-digitizer/">http://markummitchell.github.io/engauge-digitizer/</a>) and WebPlot Digitizer (<a href="https://directory.fsf.org/wiki/WebPlotDigitizer">https://directory.fsf.org/wiki/WebPlotDigitizer</a>). By manually defining the axes and selecting points along the curve by computer mouse, a comma-separated-values file was generated for each size distribution profile image. From there, a series of transformations were carried out as required, including particle diameter conversions from aerodynamic diameter to Stokes diameter, and conversion of number-weighted size distributions to volume-weighted size distributions, in order to obtain a harmonized set of profiles. This Excel workbook contains the raw digitized data for all literature size distributions included in the compilation of the new library, as well as the diameter and size distribution weighting conversions. There are 39 worksheets: the first is an introductory worksheet entitled “Spreadsheet_Info” while the next 36 worksheets are ordered alphabetically and correspond to each of the 36 emissions source types for which a PM size disaggregation profile was generated. The final two worksheets contain digitized particle penetration data for common PM control devices.</p> <p>These digitized profiles may be used and adapted for use with other emissions processing systems and other CTMs with a size-resolved representation for PM. More details are provided in the following publication:</p> <p>Elisa I. Boutzis, Junhua Zhang & Michael D. Moran (2020) Expansion of a size disaggregation profile library for particulate matter emissions processing from three generic profiles to 36 source-type-specific profiles, <em>Journal of the Air & Waste Management Association</em>, 70:11, 1067-1100, DOI: <a href="https://doi.org/10.1080/10962247.2020.1743794">10.1080/10962247.2020.1743794</a></p>
Test data for Sonic Kayaks particulate matter sensor, temperature and GPS
<p>These data sets are the first trials for adding a particulate matter sensor to the Sonic Kayak project https://fo.am/activities/kayaks/</p> <p>There are three data files - gps.csv is the GPS co-ordinates, pm.csv is the particulate matter data (using a PMS7003), and temp.csv is the temperature data (two separate but identical digital thermometer sensors). Time is included in all datasets and can be used to align them. Together the data can be used to make a fine scale heat line map of particulate matter and temperature.</p>
Population impact of fine particulate matter on tuberculosis risk in China: A causal inference
<p>Supplementary to "Population impact of fine particulate matter on tuberculosis risk in China: A causal inference"</p>
Decoding Physical and Cognitive Impacts of Particulate Matter Concentrations at Ultra-fine Scales
<p>Data, plots, and software to accompany (unpublished) paper: Decoding Physical and Cognitive Impacts of Particulate Matter Concentrations at Ultra-fine Scales. This work uses an ultra-fine, holistic environmental and biometric sensing paradigm to generate empirical particulate matter models estimated by biometric variables.</p> <p>GitHub repository: <a href="https://github.com/mi3nts/DUEDARE">https://github.com/mi3nts/DUEDARE</a></p>
Influence of suspended particulate matter on salinity measurements
<p>Data from a laboratoy study on the influence of suspended particulate matter on salinity measurements. Measurements were carried out in mud samples from the German Wadden Sea and from the Ems estuary.</p> <p>Prior to the measurements, the mud samples were desalinated. This was done with a combination of dialysis tubing and distilled water over several days.</p> <p>The desalted mud was then diluted with distilled water to produce solutions with sediment concentrations of 0.5, 1, 5, 10, 25, 50, 75, 100, 150, 200 and 300 g/l. Then the salintiy was stepwise increase by adding NaCl. The adjusted salinities were 0, 1, 5, 10, 15, 20, 25, 30, 35 and 40 g/kg. The conductivties were measured in each step with a handheld salinometer (CON 3310 by WTW) and a CTD (Sea & Sun Technology CTD90). These measurements were carried out at the Institute of Geosciences at Kiel University (Germany) in September 2011.</p> <p>The data were stored in .ods format (OpenOffice / LibreOffice).</p>
Scanning electron microscopy (SEM) images of particulate matter collected on air filters
<p>Airborne PM sampling was conducted within a larger study on the PM composition of different areas in Santa Rosa, La Pampa, Argentina by Prof. Dr. Mendez Mariano. Airborne PM10 samples were collected on commercial 47mm diameter PTFE membrane filters (Image 1-blank) and Nylon filters (Image 2-blank). The PM10 was collected using an electrostatic precipitator coupled with the Easy Dust Generator (EDG). Filters were analysed using a Scanning Electron Microscope (Phenom™ ProX Desktop, Thermofisher). Images were taken using an accelerating voltage of 15 kV. SEM-EDX results are presented in this dataset.</p>
Towards improving short-term predictions of fine particulate matter over the United States via assimilation of satellite aerosol optical depth retrievals
<p>This dataset contains paired modeled and observed values of different trace gases and aerosol species over the CONUS for the period of 15 July to 14 August 2014 for the three different data assimilation experiments (BKG, MET_BE and MET+EMIS_BE) described in the paper. </p>
Effect of meteorological variability on fine particulate matter simulations over the contiguous United States
<p>Supporting data files for the paper titled "Effect of meteorological variability on fine particulate matter simulations over the contiguous United States" submitted to JGR-Atmosphere for publication.</p>
Microscopy images - " Particulate matter constituents trigger the formation of extracellular amyloid β and Tau -containing plaques and neurite shortening in vitro"
<p>This repository contains microscopy data from the manuscript "Particulate matter constituents trigger the formation of extracellular amyloid β and Tau -containing plaques and neurite shortening in vitro" by Aleksandar Sebastijanović, Laura Maria Azzurra Camassa, Vilhelm Malmborg, Slavko Kralj, Joakim Pagels, Ulla Vogel, Shan Zienolddiny-Narui, Iztok Urbančič, Tilen Koklič, and Janez Štrancar (published in Nanotoxicology, 18(4), 335–353. https://doi.org/10.1080/17435390.2024.2362367).</p> <p>Raw data are organized in folders named by image number, containing a subfolder with the date (year-day-month) of image acquisition. Followed by a subfolder named by a nanomaterial to which neurons were exposed. Each folder contains images from individual multi-channel, multi-position time-lapse experiments with different combinations of cells exposed to one nanomaterial. Files are named as: IMGxxxx_[ExperimentCode]_ROIxx_[Channel].tif, where each of the varying elements in [..] denotes the following:<br>• [ExperimentCode]: a short name of the experiment<br>• [Channel]: membrane (MEM), cytoplasm neuronal cells (NEU), nanomaterial (NANO), amyloid beta (AMY)</p>
Impact of night-time cold-air flow on particulate matter concentration at Tempelhofer Feld, Berlin, Germany
<p>The dataset consists of particulate matter concentration and meteorology data, measured in Berlin-Hasenheide and Berlin-Temeplhof from June 15, 2022 to June 18, 2022.</p> <p><a href="https://zenodo.org/api/records/14046315/draft/files/Hasenheide_cargo-bike_PM_HUB.geojson/content" target="_blank" rel="noopener noreferrer">Hasenheide_cargo-bike_PM_HUB.geojson</a> : The observed PM concentration and meteorology, at 10 measurement points around Tempelhofer Feld, Hasenheide and Kreuzberg. The mesurements were carried out on a mobile-platform, using a cargo-bike. </p> <p>Information on working with geojson file can be found under <a href="https://geojson.readthedocs.io/en/latest/">GeoJSON</a> .</p> <p><a href="https://zenodo.org/api/records/14046315/draft/files/Hasenheide_Crane_HUB.csv/content" target="_blank" rel="noopener noreferrer">Hasenheide_Crane_HUB.csv</a> : The observed PM concentration and meteorology at 2 m, 5 m, 10 m, 15 m, and 20 m in Hasenheide. The measurments were carried out using a crane to move the instruments to different heights.</p> <p><a href="https://zenodo.org/api/records/14046315/draft/files/Hasenheide_Crane_HUB.csv/content" target="_blank" rel="noopener noreferrer">Hasenheide_Stationary_Meteorology_HUB.csv</a> and <a href="https://zenodo.org/api/records/14046315/draft/files/Hasenheide_Stationary_PM_HUB.csv/content" target="_blank" rel="noopener noreferrer">Hasenheide_Stationary_PM_HUB.csv</a> contain the stationary and continuous, meteorology and PM measurement data collected in Berlin Hasenheide. The data was collected at 0.5m and 2m above ground level.</p> <p><a href="https://zenodo.org/api/records/14046315/draft/files/Hasenheide_Crane_HUB.csv/content" target="_blank" rel="noopener noreferrer">Tempelhof_Stationary_Meteorology_HUB.csv</a> and <a href="https://zenodo.org/api/records/14046315/draft/files/Hasenheide_Stationary_PM_HUB.csv/content" target="_blank" rel="noopener noreferrer">Tempelhof_Stationary_PM_HUB.csv</a> contain the stationary and continuous, meteorology and PM measurement data collected in Berlin Hasenheide. The data was collected at 0.5m and 2m above ground level.</p> <p>Units: <br>PM : µg/m³<br>Air temperature: °C<br>Surface temperature: °C<br>Potential temperature: K<br>Relative humidity: %<br>Specific humidity: g Water vapour per kg dry air<br>Pressure: hPa<br>Solar radiation: W/m²<br>Wind speed: m/s<br>Wind direction: Degree</p> <p> </p> <p>The original article providing all necessary background information on study sites, methodology and data processing is the following: Venkatraman Jagatha, J., T. Sauter, C. Schneider (2024): Impact of night-time cold-air flow on particulate matter concentration at Tempelhofer Feld, Berlin, Germany. </p> <p>The article will be submitted to the journal "Die Erde", ISSN: 0013-9998. </p> <p> </p>
EPA particulate matter data – Analyses using Local Control Strategy
<p><span class="fontstyle0">Analyses of large observational datasets tend to be complicated and prone to fault depending upon the variable selection, data cleaning and analytic methods employed. Here, we discuss the analysis of 2016 US environmental epidemiology data and outline a new "benchmark" Non-parametric and Unsupervised analysis of these data. Readers are invited to download our CSV file from the archive and apply whatever analytic approach they think appropriate. We hope to encourage the development of a widely-held "consensus view" on the effects of Secondary Organic Aerosols (Volatile Organic Compounds that have predominantly Biogenic or Anthropogenic origin) within PM</span><span class="fontstyle2">2</span><span class="fontstyle3">.</span><span class="fontstyle2">5 </span><span class="fontstyle0">particulate matter on Circulatory and/or Respiratory mortality. For example, the reanalyses described here focus on the question: "Can life in a region with an abundance of trees be relatively dangerous?"</span></p>
Biogeochemical data of sinking particulate matter collected by sediment traps at E2M3A mooring (2013-2020) in the Southern Adriatic Sea
<p>The dataset presented (NetCDF format), includes sediment trap derived fluxes (total mass, POC, CaCO<sub>3</sub>, bSiO<sub>2</sub>) at ~1200 m depth from the E2M3A deep-sea mooring in the center of the Southern Adriatic Sea in the period from December 2013 to September 2020. The E2M3A observatory is positioned in the centre of the cyclonic gyre where deep convection process takes place, involving both the atmosphere and the ocean dynamics and forming new dense and oxygenated waters, thus triggering the biological pump. The E2M3A mooring is included in the southern Adriatic regional facilities of EMSO-ERIC consortium managed from OGS Trieste and CNR-ISP. Sediment traps, positioned at two levels (below the photic layer at -120m and near the bottom at -1050m), allow the understanding of the processes responsible for the high-frequency and interannual variability of new production and of phytoplankton biomass. Total mass fluxes (TMF) measured at the shallower trap were generally lower that those measured at the bottom trap, ranging from 16 to 706 mg m<sup>-2</sup> d<sup>-1</sup>, with a time-weighted average of 133 mg m<sup>-2</sup> d<sup>-1</sup>, whereas at the bottom trap TMF varied from 33 to 885 mg m<sup>-2</sup> d<sup>-1</sup>, with a time-weighted average of 190 mg m<sup>-2</sup> d<sup>-1</sup>. The organic carbon flux, followed the same seasonal trend, with higher values below the photic zone, varying from 1.7 to 31.9 mg m<sup>-2</sup> d<sup>-1</sup>, with a mean of 5.8 mg m<sup>-2</sup> d<sup>-1</sup> at the shallow trap.</p>
A new method for the quantification of ambient particulate matter emission fluxes - Data
<p>An inversion method has been developed in order to quantify the emission fluxes of certain aerosol pollution sources<br> across a wide region in the Northern hemisphere, mainly in Europe and Western Asia. The data employed are the aerosol<br> contribution factors deducted by Positive Matrix Factorization (PMF) on a PM2.5 chemical composition dataset from 16<br> European and Asian cities for the period 2014 to 2016. The spatial resolution of the method corresponds to the geographic<br> grid cell size of the Lagrangian particle dispersion model 5 (FLEXPART 10.4 , 1 x 1 degree) which was utilized for the air mass backward simulations. The area covered is also related to the location of the 16 cities under study.</p>
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Allen Brain Atlas
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