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157 results for “particulate matter”
Carbon and nitrogen content and stable isotope compositions from particulate organic matter samples from lagoon, river, and open ocean sites along the Alaska Beaufort Sea coast, 2018-ongoing
Multiple water types (river, lagoon, ocean) from the North Slope of Alaska and nearshore Beaufort Sea are sampled seasonally by the Beaufort Lagoon Ecosystems LTER (BLE LTER) Core Program to investigate biogeochemical linkages between terrestrial, lagoon, and open ocean ecosystems. Water samples are collected during full ice cover (April), ice break-up (mid-June to early July), and open water (late July and August) periods, and analyzed for particulate organic carbon (POC) and particulate organic nitrogen (PON) content and stable isotopic composition.
Stream Suspended Sediment and Particulate Organic Matter at Harvard Forest 2009-2010
In addition to conveying water and nutrients and providing habitat to a variety of ecosystems, streams transport downstream mineral sediment and other particulate matter washed in from hillslopes and eroded from its channel and banks. At high levels, suspended sediment can be a devastating pollutant for aquatic organisms. The amount of suspended material in a stream varies tremendously with discharge; typically, suspended sediment increases with discharge, as stormwater runoff and overland flow carry particles from the hillslopes into the channel. Suspended sediment can also be a function of land use and vegetation, both of which affect the infiltration capacity of the landscape; more infiltration generally means less surface runoff and thus less sediment. Forested watersheds such as the Bigelow Brook watershed will typically have less suspended sediment than similar watersheds in urban environments. By analyzing how suspended sediment varies with discharge, I will be able to compare the relative effectiveness of overland flow of stormwater in washing materials into the streams. It is also possible that tree loss due to the wooly adelgid, ice storms, or fire in the watershed may increase the amount of sediment to Bigelow Brook, as a loss in tree canopy may result in more soil erosion due to rain splash and more water overall reaching the stream. For this reason, I hope to continue monitoring sediment in Bigelow Brook for an extended period of time to record any significant changes due to changing vegetation. Furthermore, by determining how much of the suspended sediment consists of particulate organic matter (using standard LOI techniques), I will be able to estimate the net carbon export from the two watersheds via that pathway. Preliminary, back-of-the-envelope calculations suggest that as much as 3-5% of the total annual carbon export leaves the Harvard Forest watershed via stream-transported particulate organic matter. To this end, I propose to measure suspended
North Temperate Lakes LTER: Total Particulate Matter - Madison Lakes Area 2000 - 2013
Total particulate matter is measured at one station in the deepest part of each lake at the top of the epilimnion for lakes Mendota, Monona, Fish and Wingra. Sampling Frequency: bi-weekly during ice-free season from late March or early April through early September, then every 4 weeks through late November; sampling is conducted usually once during the winter (depending on ice conditions). Number of sites: 4
Parsimonious Random-Forest-Based Land-Use Regression Model Using Particulate Matter Sensors in Berlin, Germany
<p>The dataset consists of particulate matter pollution concentration, measured in three localities - Hermsdorf, Charlottenburg and Adlershof, in Berlin, Germany.</p> <p><a href="../api/records/10076056/draft/files/pm25_summer_rd_30s.geojson/content" target="_blank" rel="noopener noreferrer">pm25_summer_rd_30s.geojson</a> shows the observed PM2.5 concentration in a 30 second interval.</p> <p><a href="../api/records/10076056/draft/files/pm25_summer.geojson/content" target="_blank" rel="noopener noreferrer">pm25_summer.geojson</a> shows the concentrations shown is the local concentration (observed concentration - background concentration) in a 30 second interval. The background concentration is calculated as the lowest 5 percentile of the measured concentration for each measurement round. </p> <p><a href="../api/records/10076056/draft/files/PM2.5_lc_max.geojson/content" target="_blank" rel="noopener noreferrer">PM2.5_lc_max.geojson</a> contains the information from <a href="../api/records/10076056/draft/files/pm25_summer.geojson/content" target="_blank" rel="noopener noreferrer">pm25_summer.geojson</a> in a 25m resolution. Additionally, it contains the land use information for each coordinate.</p> <p>The original publication providing all necessary background information on study sites, methodology and data processing is the following: Venkatraman Jagatha, J., T. Sauter, C. Schneider (2024): Parsimonious Random-Forest-Based Land-Use Regression Model Using Particulate Matter Sensors in Berlin, Germany. MDPI Sensors, 24(13), 4193, DOI: 10.3390/s24134193. The paper is fully open access and can be downloaded at <a href="https://doi.org/10.3390/s24134193">https://doi.org/10.3390/s24134193</a>.</p> <p>Information on working with geojson file can be found under <a href="https://geojson.readthedocs.io/en/latest/">GeoJSON</a> .</p>
Fine particulate matter (PM2.5) concentrations in downtown Phoenix, Arizona (USA) on August 20, 2024
This dataset contains a collection of estimated particulate matter (PM2.5) concentrations for downtown Phoenix, Arizona (USA), on a typical summer day (August 20, 2024) at three critical times of day (7 a.m., 1 p.m., and 5 p.m.). The 100-m resolution estimates were generated using a pre-trained support vector regression model. This dataset can inform public health interventions related to air quality.
Ionic composition of particulate matter (PM10) from high-volume sampling over the Southern Ocean during the austral summer of 2016/2017 on board the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>Aerosol particles originate from a variety of sources (Tomasi and Lupi, 2017). Information on particle chemical composition can be utilized to access particle origin. During the Antarctic Circumnavigation Expedition (ACE) cruise around the Southern Ocean, off-line filter sampling of ambient air was performed. Filters were stored on the ship (at -20 degrees C) and after the cruise concluded analysed at Leibniz-Institute for Tropospheric Research (TROPOS) concerning ionic composition of sampled material. Here, we give mass concentrations for inorganic ions (chloride, sodium, potassium, magnesium, calcium, ammonium, nitrate, sulphate, and bromide), organic constituents (methane-sulfonic acid and oxalate), and total filter load of particles with a mobility diameter smaller 10 micrometers (PM10) for each 24 hour-sampled filter.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ACESPACE_particulate_matter_pm10_ionic_composition_highvolume.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This ionic composition of particulate matter (PM10) from high-volume sampling dataset during ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Regional Estimates of Chemical Composition of Fine Particulate Matter Using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors: V4.NA.02.MAPLE
<p>We estimate ground-level fine particulate matter (PM<sub>2.5</sub>) total and compositional mass concentrations over North America by combining Aerosol Optical Depth (AOD) retrievals from the NASA MODIS, MISR, and SeaWIFS instruments with the GEOS-Chem chemical transport model, and subsequently calibrated to regional ground-based observations of both total and compositional mass using Geographically Weighted Regression (GWR) as detailed in the provided reference for V4.NA.02. V4.NA.02.MAPLE further modified the V4.NA.02 GWR method with additional developments as part of the MAPLE (Mortality–Air Pollution Associations in Low-Exposure Environments) project. This adjustment was of particular value over low concentrations. The GWR method of individual components remains unchanged from V4.NA.02, but are provided are percentages to ensure mass closure and recommended to be applied to the V4.NA.02.MAPLE total PM<sub>2.5</sub>.</p> <p>Annual datasets are provided in NetCDF [.nc]. Gridded files use the WGS84 projection. Compositional estimates are provided for sulfate (SO4), nitrate (NO3), ammonium (NH4), organic matter (OM), black carbon (BC), mineral dust (DUST), and sea-salt (SS). Percentages are denoted with a ‘p’ after component identifiers within filenames. A slight change in file name has been included for 2017, corresponding to minor internal changes compared to earlier years. Overall, however, the dataset is consistent throughout its entire time period and can be appropriately used for trend analysis.</p> <p><strong>Reference:</strong><br> van Donkelaar, A., R. V. Martin, et al. (2019). <strong>Regional Estimates of Chemical Composition of Fine Particulate Matter using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors.</strong> Environmental Science & Technology, 2019, doi:10.1021/acs.est.8b06392.</p>
HYPSTAR hyperspectral water reflectance and derived water quality products (suspended particulate matter and chlorophyll-a concentration) at the Blankaart surface water reservoir (BE)
<p><strong>Hyperspectral Water Leaving Reflectance spectra (2988)</strong> measured between 2021-02-03 and 2022-08-03 at the Blankaart Surface Water Reservoir (Belgium, 50.98857N, 2.835213E) with the <strong>HYPSTAR®</strong> (ID: HYPSTAR_12120241). Detailed description of the data collection, processing and analysis can be found in <strong>Goyens et al.- Remote Sens. 2022</strong> - 14(21)- 5607; https://doi.org/10.3390/rs14215607.</p> <ol> <li>HYPSTAR_W_BSBE_L2A_REFL_20210203_20220803_v1.csv</li> </ol> <p><strong>Chlorophyll-a (Chl-a) concentration and Suspended Particulate Matter (SPM) </strong>were derived from the above dataset of hyperspectral water reflectance and estimated according to different algorithms found in the litterature, i.e.,</p> <ol> <li>HYPSTAR_W_BSBE_CHLA_SIMIS_20210203_20220803_v1.csv<strong>:</strong> <strong>Chlorophyll-a concentration</strong> estimated from the <strong>HYPSTAR®</strong> reflectance measurements and following the algorithm suggested by Simis et al. (2005; https://doi.org/10.4319/lo.2005.50.1.0237) with a variable absorption coefficient of phytoplankton per unit of Chl-a concentration as described in Goyens et al. (2022)</li> <li>HYPSTAR_W_BSBE_CHLA_CRAT_20210203_20220803_v1.csv: <strong>Chlorophyll-a concentration</strong> estimated with the <strong>HYPSTAR®</strong> reflectance measurements and following the algorithm suggested by Ruddick et al. (2001; https://doi.org/10.1364/AO.40.003575.) with a variable absorption coefficient of phytoplankton per unit of Chl-a concentration as described in Goyens et al. (2022)</li> <li>HYPSTAR_W_BSBE_SPM_20210203_20220803_v1.csv: <strong>Suspended particulate matter </strong>estimated with the <strong>HYPSTAR®</strong> reflectance measurements and following the algorithm suggested by Nechad et al. (2010) at 700 nm</li> </ol>
SBC LTER: OCEAN: Particulate Organic Matter Content and Composition of Stream, Estuarine, and Marine Sediments
An unprecedented five-year drought in California, coupled with conditions of anomalously low ocean productivity and the prospect of one of the strongest El Niño periods on record with above average rainfall were the impetus for this RAPID award, which seeks to test specific hypotheses pertaining to the origin, distribution, processing, and bioavailability of terrestrial organic matter in coastal marine sediments and their potential for serving as a reservoir of nitrogen storage to fuel nearshore primary production during periods when nitrate concentrations are low. The goals of the research were to: (1) measure bulk properties and biomarker tracers of particulate organic matter (POM) in stream water and in coastal marine sediments at SBC LTER and other reef sites differing in exposure to terrestrial runoff prior to and following large storm events, (2) determine the bioavailability of dissolved organic matter (DOM) released from POM in marine sediments following large runoff events, and (3) measure changes in concentrations of dissolved inorganic and organic nitrogen in pore water of marine sediments near to and distant from stream mouths in the Santa Barbara Channel. Samples were analyzed for organic matter content using a loss-on-ignition combustion method, and samples were also analyzed for organic carbon and nitrogen content and isotopes using a stable isotope mass spectrometer interfaced with an elemental analyzer. Subsamples were shipped to the laboratory of Marc Lucotte at the University of Québec, Montréal for analysis of lignin content using the cupric oxidation method.
Daily Emission of Fine Particulate Matter (PM2.5) Associated with Biomass Burning in South America During 2002-2020
<p>The dataset "Daily Emission of Fine Particulate Matter (PM2.5) Associated with Biomass Burning in South America During 2002-2020" contains the emissions analysed in the manuscript "Updated Land Use and Land Cover Information Improves Biomass Burning Emission Estimates", published in Fire 2023, 6(11), 426; <a href="https://doi.org/10.3390/fire6110426">https://doi.org/10.3390/fire6110426</a>.</p>
Laboratory comparison of low-cost particulate matter sensors to measure transient events of pollution - Dataset
<p>This repository contains the data associated with the paper: Laboratory comparison of low-cost particulate matter sensors to measure transient events of pollution.</p> <p>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.</p> <p><a href="https://doi.org/10.3390/s20082219">https://doi.org/10.3390/s20082219</a> </p> <p>It contains:</p> <p>- DHT22.csv measurements from the DHT22 humidity and temperature sensor</p> <p>- dusttrak.csv measurements from the DustTrak</p> <p>- ops.csv measurements from the OPS TSI 3330</p> <p>- sensors.csv measurement from the low-cost PM sensors</p> <p>- sensors_blank.csv measurements from the low-cost PM sensors during the blank test</p> <p> </p> <p>sensors_blank.csv contains the following variables:</p> <ul> <li>Bin1 to Bin15: particle numbers for different bin sizes reported by the Alphasense OPCR1, as defined by its user's manual available here https://www.alphasense.com/products/optical-particle-counter/</li> <li>SamplingPeriod: sampling period of the Alphasense OPCR1 in seconds</li> <li>SFR: sampling flow rate of the Alphasense OPCR1 in ml/s</li> <li>PM1, PM25, PM4, PM10: PM concentrations reported by the sensors in ug/m3.</li> <li>gr03um to gr100um: particle number concentrations for different bin sizes for the Plantower PMS5003, in particle per 100ml, as defined by its user's manual https://aqicn.org/air/view/sensor/spec/pms5003-manual_v2-3</li> <li>n05 to n10: particle number concentrations for different bin sizes for the Sensirion SPS30, in particles per cm3, as defined by its user's manual: https://www.sensirion.com/fileadmin/user_upload/customers/sensirion/Dokumente/9.6_Particulate_Matter/Datasheets/Sensirion_PM_Sensors_Datasheet_SPS30.pdf</li> <li>humidity and temperature: relative humidity (%) and temperature (Celsius) recorded by the SHT35 sensors</li> <li>sensor: sensor identifier</li> <li>site: name of the air quality monitor containing the sensors</li> <li>exp: name of the experiment considered</li> <li>source: source of PM used</li> <li>variation: peak or stable concentration</li> <li>date: date and time of the experiment</li> </ul>
Hourly LC impacts - Particulate Matter - current mix and future scenarios, average demand
<p>Dataset on LCA results of electricity generation and supply in Italy for 2018, 2019 and 2020 (current mix) and two future scenarios (2030) - Particulate Matter, average demand perspective.</p> <p>Modelling materials and methods are described in the paper "Life-cycle assessment of current and future electricity supply in Italy: addressing average and marginal hourly demand".</p>
A study of comparative (2019-2023) trends and current acceleration in Particulate Matter (PM2.5) concentration in India
<p><span> For PM<sub>2.5</sub><span> </span>monitoring model, the data was procured from the Central Pollution Control Board’s functional and selected air monitoring stations. The data is available online at the <span> Central Pollution Control Board but in form of daily trends with numerous air quality monitoring stations in an area; monthly and Annual average level especially PM2.5 trends processed from the original data. </span></span></p>
Emission factors and chemical composition of particulate matter from residential biomass combustion
<p>Emission factors and chemical composition of particulate matter from residential biomass combustion.</p>
Soil aggregate size distribution and particulate organic matter content from Arctic LTER moist acidic tundra nutrient addition plots, Toolik Field Station, Alaska, sampled July 2011.
Soil aggregate size distribution, aggregate carbon and nitrogen, and light fraction carbon were determined for mineral soils in moist acidic tundra. Soil was sampled in control, and N+P plots of the Arctic LTER Moist Acidic Tundra plots established in 1989 and 2006.
Gauging Size Resolved Ambient Particulate Matter Concentration Solely Using Biometric Observations: A Machine Learning and Causal Approach
<p>Notebook and data to accompany the (unpublished) paper titled "Gauging Size Resolved Ambient Particulate Matter Concentration Solely Using Biometric Observations: A Machine Learning and Causal Approach". This work expands a previous study, relating particulate matter concentrations and short-term biometric features across multiple participants. </p><p>Github link: https://github.com/mi3nts/DUEDARE_multiple_participants</p>
Minute-Level Human Activity and Particulate Matter Exposure Dataset from Ljubljana, Slovenia
<p>This dataset encompasses detailed measurements of human activities and particulate matter exposure at a minute-level resolution, collected in Ljubljana, Slovenia from September 24 to October 31, 2020. Data was gathered from 18 participants using a combination of devices: a personal particulate matter monitor (PPM), a Garmin Vivosmart 3 smart activity tracker (SAT), and the Clockify app. The PPM provided real-time measurements of particulate matter concentrations (PM1, PM2.5, and PM10), as well as environmental parameters like temperature, humidity, and altitude. The SAT tracker offered insights into personal health data, including average heart rate and metabolic equivalent of task (MET). Clockify app was utilized for detailed logging of various activities categorized with minute accuracy.</p> <p>Key variables included in the dataset are:</p> <ol> <li>Participant ID</li> <li>Date of data collection</li> <li>Time of data recording (minute accuracy)</li> <li>Specific task or activity performed</li> <li>Particulate matter - PM1 - concentrations</li> <li>Particulate matter - PM2.5 - concentrations</li> <li>Particulate matter - PM10 - concentrations</li> <li>Environmental temperature</li> <li>Relative humidity</li> <li>Altitude</li> <li>Speed</li> <li>Average heart rate</li> <li>Metabolic equivalent of task</li> </ol> <p>This dataset offers a resource for exploring the interplay between individual behaviors and air pollution exposure, with applications in environmental health research and the development of machine learning models for activity recognition.</p>
LongPMInd: long-term (1980-2022) daily ground particulate matter datasets in India
<p>The LongPMInd dataset, including daily PM2.5 and PM10 concentration (10km) for India during 1980-2022, is publicly accessible. All data are provided with NetCDF format with a spatial resolution of 10 km.<br><br></p>
Suspended particulate matter concentrations in Lake Taihu from 1984 to 2020 based on Landsat TM and OLI
<p>The Suspended particulate matter (SPM) concentrations product in Lake Taihu from 1984 to 2020 based on Landsat TM and OLI. We applied Landsat-5 TM and Landsat-8 OLI satellite data to track SPM concentration changes from 1984 to 2020 in Lake Taihu. Landsat-7 ETM+ data was not used due to its striping noise. The SPM estimation models were calibrated and validated using in situ SPM, remote sensing reflectance (Rrs) data and synchronous satellite data. After comparison, it was found that the accuracy of the model based on band combination ((𝑅𝑟𝑠(𝑟𝑒𝑑)+𝑅𝑟𝑠(𝑛𝑒𝑎𝑟−𝑖𝑛𝑓𝑟𝑎𝑟𝑒𝑑))/𝑅𝑟𝑠(𝑔𝑟𝑒𝑒𝑛) was the highest, with the average unbiased relative error <35%. The spatiotemporal distribution pattern of the SPM analyses in Lake Taihu could facilitate local water resource conservation.</p>
Particulate matter concentrations (PM1, PM2.5, PM10) since 2009 for a measurement sites in Zagreb, Croatia
<p>Daily samples of PM<sub>1</sub>, PM<sub>2.5</sub> and PM<sub>10</sub> fractions have been collected continuously during 12-years period (2009-2020) at Zagreb, Croatia (45°50’7’’ N, 15°58’42’’ E, 116 m a.s.l.,). A sampling site was located in the northern, residential part of city which was characterized by modest traffic and population density. The main sources during the household heating season which usually started in October and lasted until April were gas and/or wood. Mass concentrations of PM<sub>1</sub>, PM<sub>2.5</sub> and PM<sub>10</sub> fractions were determined gravimetrically, while meteorological parameters (temperature, RH, wind speed and direction, pressure, and precipitation) were obtained from the Croatian Meteorological and Hydrological Service.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.