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32 results for “PM10”
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>
PM2.5, PM10, NO2, O3 from Copernicus Air Quality Forecast March-June 2019, 2020 and 2021
<p>PM2.5, PM10, NO2, O3 Copernicus Air Quality Forecasts March-June 2019, 2020 and 2021 retrieved from the ADAM platform data cube (http://reliance.adamplatform.eu). Datasets are monthly averaged.</p> <p>The resulting extracted datasets are stored in netCDF format and cover Europe.</p>
NO2, O3, PM10 and PM2.5 concentrations - Daily geographical aggregates at NUTS3 level from CAMS European Air Quality Re-analyses.
<p>This dataset offers daily aggregated measurements of air pollutants – NO2, O3, PM10, and PM2.5 – across distinct NUTS3 regions in continetal Europe. The temporal coverage spans from January 1, 2013, to December 31, 2022, providing a comprehensive temporal context for analyzing long-term air quality dynamics.</p> <p>Each daily entry comprises key statistical descriptors, encompassing mean, maximum, minimum, and standard deviation values of pollutant concentrations specific to each NUTS3 area. Additionally, for O3, the dataset includes an eight-hour rolling mean daily maximum.</p> <p>Spatial reference is established via shapefiles (EPSG:4326) sourced from Eurostat's official repository (<a href="https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/nuts">https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/nuts</a>). These shapefiles link the air quality data to precise NUTS3 regions through unique identifiers.</p> <p>The concentration data spanning from 2018 to 2022 originate from the European Air Quality Reanalyses dataset of the Atmosphere Data Store (ADS), an initiative by the Copernicus Atmosphere Monitoring Service (CAMS). Accessible via <a href="https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-reanalyses?tab=doc">https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-reanalyses?tab=doc</a>, this dataset offers a robust foundation for assessing air quality. For the years 2013 to 2017, data were previously obtained from a former download platform for the same dataset. Important: in future all data will be migrated to the Atmosphere Data Store (ADS) platform.</p> <p>The native resolution of the CAMS data is 0.1° x 0.1° spatially and hourly temporally. To enhance spatial accuracy, the spatial resolution was virtually increased by a factor of 5 using bilinear interpolation, resulting in a refined grid. The daily mean concentrations were subsequently computed for this augmented grid.</p> <p>Aggregated statistics were derived for each NUTS3 polygon, employing all grid cells intersecting with the polygons. The computation was based on the proportion of cell area included within the respective polygons.</p> <p>This dataset constitutes a valuable resource for conducting ecologically designed epidemiological studies, as it facilitates the exploration of potential associations between air quality and health trends across broad geographical areas.</p>
Daily 1-km gap-free PM10 grids in China, v1 (2000–2020)
<p>A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP aerosol dataset (LGHAP.v1), we provide a 21-year-long (2000–2020) gap free PM10 concentration product with daily 1-km resolution covering the land area of China. The dataset was generated from the daily gap free AOD (https://doi.org/10.5281/zenodo.5652257) that was derived through an integration of a set of data tensors of AOD and other related datasets such as air pollutants concentration and atmospheric visibility acquired from diversified sensors or platforms via a machine learned regression model. The dataset was provided in the NetCDF format, while data in each individual year were archived in a zip file. Python, Matlab, R, and IDL codes were also provided to help users read and visualize the LGHAP data.</p>
Annual mean 1-km gap-free AOD, PM2.5, and PM10 grids in China, v1 (2000–2020)
<p>A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP aerosol dataset (LGHAP.v1), we provide 21-year-long (2000–2020) gap free annual mean AOD, PM2.5 and PM10 concentration data with a 1-km resolution covering the land area of China. The dataset was generated from the daily gap free AOD (https://doi.org/10.5281/zenodo.5652257) that was derived through an integration of a set of data tensors of AOD and other related datasets such as air pollutants concentration and atmospheric visibility acquired from diversified sensors or platforms via a machine learned regression model. The dataset was provided in the NetCDF format, while data in each individual year were archived in a zip file. Python, Matlab, R, and IDL codes were also provided to help users read and visualize the LGHAP data.</p>
Annual Mean Levels of Fine PM10 particules in Valladolid city
<p>Road transport and construction operations are identified as major sources of air pollutants in cities. Airborne particulate matter is associated with harmful effects on human cardiovascular and respiratory health. Particles ≤ 10 microns (PM10), and particularly the finer particles ≤ 2.5 microns (PM2.5) associated with road transport vehicles, are of concern due to their small size; (a micron, or micrometre = one-millionth of a meter: 0.001 millimetre). Green walls (or screens) in urban streets may act as barriers to direct dispersal of pollutants from combustion engine vehicles to pedestrian areas. Particulates may be deposited on the leaf surface of trees or taken up into the leaf surface wax layer, reducing atmospheric particulate concentrations. Monitoring of air quality parameters is complex; involving many potentially interacting variables. Variation in weather conditions; prevailing wind direction and speed; tree species, density, location and structure; and the configuration of built urban infrastructure are among factors which may affect the trajectory and rate of dispersal of particulate pollutants. We aim to compare outdoor air concentrations of PM10 and PM2.5 at child and adult head heights at locations with and without street trees or green walls to evaluate whether these NBS are associated with reduced local concentrations of airborne PM10 and PM2.5.</p>
NO2, O3, PM10 and PM2.5 concentrations - Daily geographical aggregates at ZIP-code level from CAMS European Air Quality Re-analyses.
<p>This dataset offers daily aggregated measurements of air pollutants – NO2, O3, PM10, and PM2.5 – across distinct ZIP-code areas in Germany. The temporal coverage spans from January 1, 2013, to December 31, 2022, providing a comprehensive temporal context for analyzing long-term air quality dynamics.</p> <p>Each daily entry comprises key statistical descriptors, encompassing mean, maximum, minimum, and standard deviation values of pollutant concentrations specific to each ZIP-code area. Additionally, for O3, the dataset includes an eight-hour rolling mean daily maximum.</p> <p>Spatial reference is established via shapefiles provided by ESRI Deutschland (<a href="https://opendata-esri-de.opendata.arcgis.com/datasets/5b203df4357844c8a6715d7d411a8341_0">https://opendata-esri-de.opendata.arcgis.com/datasets/5b203df4357844c8a6715d7d411a8341_0</a>). These shapefiles link the air quality data to precise ZIP-code areas .</p> <p>The concentration data spanning from 2018 to 2022 originate from the European Air Quality Reanalyses dataset of the Atmosphere Data Store (ADS), an initiative by the Copernicus Atmosphere Monitoring Service (CAMS). Accessible via <a href="https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-reanalyses?tab=doc">https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-reanalyses?tab=doc</a>, this dataset offers a robust foundation for assessing air quality. For the years 2013 to 2017, data were previously obtained from a former download platform for the same dataset. Important: in future all data will be migrated to the Atmosphere Data Store (ADS) platform.</p> <p>The native resolution of the CAMS data is 0.1° x 0.1° spatially and hourly temporally. To enhance spatial accuracy, the spatial resolution was virtually increased by a factor of 5 using bilinear interpolation, resulting in a refined grid. The daily mean concentrations were subsequently computed for this augmented grid.</p> <p>Aggregated statistics were derived for each ZIP-code polygon, employing all grid cells intersecting with the polygons. The computation was based on the proportion of cell area included within the respective polygons.</p> <p>This dataset constitutes a valuable resource for conducting ecologically designed epidemiological studies, as it facilitates the exploration of potential associations between air quality and health trends across broad geographical areas.</p> <p>Generated using Copernicus Atmosphere Monitoring Service Information 2013-2022</p>
PM10 concentrations in the region of West Macedonia, Greece for a 10-year period
<p>Dataset of PM10 daily average concentrations for a 10-year period The data presented were collected from nine locations from 2021 to 2020 in the region of western Macedonia, Greece</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>
Iron Trace Elements Concentration in PM10 and Alzheimer's Disease in Lima, Peru: Ecological Study - dataset
<p>This dataset was created to evaluate the association between iron trace-elements concentration in PM10 with Alzheimer´s Disease cases in different districts in Lima, Peru. The database was constructed using open-access repositories of the Peruvian Ministry of Health and the Peruvian CDC.</p> <p>The uploaded datasets are in .dta and .csv formats.</p>
Data for publication "Switzerland's PM10 and PM2.5 environmental increments show the importance of non-exhaust emissions"
<p>Data for publication "Switzerland's PM10 and PM2.5 environmental increments show the importance of non-exhaust emissions". Please see README.md for information. </p>
Beijing PM10 Dataset
<p>This dataset is part of the Monash, UEA & UCR time series regression repository. <a href="http://tseregression.org/">http://tseregression.org/</a></p> <p>The goal of this dataset is to predict PM10 air quality in the city of Beijing. This dataset contains 17532 time series with 9 dimensions. This includes hourly air pollutants measurments (SO2, NO2, CO and O3), temperature, pressure, dew point, rainfall and windspeed measurments from 12 nationally controlled air quality monitoring sites. The air-quality data are from the Beijing Municipal Environmental Monitoring Center. The meteorological data in each air-quality site are matched with the nearest weather station from the China Meteorological Administration. The time period is from March 1st, 2013 to February 28th, 2017. <br> <br> Please refer to <a href="https://archive.ics.uci.edu/ml/datasets/Beijing+Multi-Site+Air-Quality+Data">https://archive.ics.uci.edu/ml/datasets/Beijing+Multi-Site+Air-Quality+Data</a> for more details<br> <br> Relevant Papers <br> Zhang, S., Guo, B., Dong, A., He, J., Xu, Z. and Chen, S.X. (2017) Cautionary Tales on Air-Quality Improvement in Beijing. Proceedings of the Royal Society A, Volume 473, No. 2205, Pages 20170457 <br> <br> Citation Request <br> Zhang, S., Guo, B., Dong, A., He, J., Xu, Z. and Chen, S.X. (2017) Cautionary Tales on Air-Quality Improvement in Beijing. Proceedings of the Royal Society A, Volume 473, No. 2205, Pages 20170457 </p>
PM10 and PM2.5 Concentrations of chemical tracers for natural sources
<p>The contribution of natural sources in particulate matter (PM) concentrations has been assessed for 5 Southern European cities: Porto (Portugal), Barcelona (Spain), Milan and Florence (Italy) and Athens (Greece). A database on the impact of natural source has been compiled, including concentrations of PM and chemical tracers used for the identification and quantification of African dust and sea salt contributions, as well as the calculated African net dust, and sea salt concentrations for each city. In addition, wildfires’ contribution is provided for Porto. Both PM<sub>10</sub> and PM<sub>2.5</sub> concentrations are reported for a total of six sites:</p> <ul> <li>Porto urban traffic site, POR-TR</li> <li>Barcelona urban background site, BCN-UB</li> <li>Milano urban background site, MLN-UB</li> <li>Florence urban background site, FI-UB</li> <li>Athens suburban site, ATH-SUB</li> <li>Athens urban traffic site, ATH-TR.</li> </ul> <p> </p>
AirGAM 2022r1 PM10 results for all stations 2005-2019
<p>Contains all trend, cross-validation and evaluation results for PM10.</p>
PM10, SO2, and NO2 Ambient Air Quality Monitoring Data from India's National Ambient Monitoring Program (NAMP) 2011-2015
<p>India's Central Pollution Control Board (CPCB) operates and maintains the National Ambient Monitoring Program (<a href="https://cpcb.nic.in/about-namp/">NAMP</a>) which includes both continuous and manual ambient monitoring stations. This dataset is a collation of manual monitoring data by day for years 2011, 2012, 2013, 2014, and 2015 for PM10, SO2, and NO2. These stations collect for a maximum of 104 days in a year. This cleaned dataset was utilized for understanding trends and conducting comparisons with modeled concentrations under the APnA city program, published <a href="https://doi.org/10.1016/j.uclim.2018.11.005">here</a> (<a href="https://doi.org/10.1016/j.uclim.2018.11.005">Urban Climate, 2019</a>).<br> <br> Data format - year, month, day, SO2, NO2, PM10, Stn Code, State, City<br> All units - micro-gm/m3 (ug/m3)</p> <p>Official annual summary reports (PDFs) are available <a href="https://cpcb.nic.in/namp-data/">here</a>.</p> <p>For guidelines for ambient and emissions monitoring, summaries of available data, and other resources on monitoring in India, visit <a href="https://urbanemissions.info/resources-energy-emissions-analysis-in-india/#monitoring">https://urbanemissions.info/resources-energy-emissions-analysis-in-india</a></p>
Aggregated PM10 data for Europe
<p>JSON-formatted and z-standard compressed data on PM10 emissions in the EU since 2013.</p>
Data for "Measurement report: A one-year study to estimate maritime contributions to PM10 in a coastal area in Northern France."
<p>The characterization and the source apportionment of PM10 data have been used for the article "<strong>Measurement report: A one-year study to estimate maritime contributions to PM<sub>10</sub> in a coastal area in Northern France</strong>," which is under revision in the journal <em>Atmospheric Chemistry and Physic</em><em>s. </em></p>
Regional E-Atlas of the Greater Phoenix Region: PM10 concentration in Greater Phoenix area
These data represent the PM10 concentration across the Greater Phoenix area. This spatial distribution was produced by digitizing the contour map provided by ADEQ.
Highly time-resolved measurements of element concentrations in PM10 and PM2.5: Comparison of Delhi, Beijing, London, and Krakow
<p>Data presented in the manuscript "Highly time-resolved measurements of element concentrations in PM10 and PM2.5: Comparison of Delhi, Beijing, London, and Krakow" (https://doi.org/10.5194/acp-2020-618) by Rai et al. (2020).</p>
PM10 scenarios in Northern Italy
<p>The dataset contains PM10 grided concentrations for Northern Italy in the reference year 2010 obtained with the chemical transport models CAMx and FARM for different emissive scenarios.</p>
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