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ShareScore release 0.9.0
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10 results for “pm10 concentration”
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>
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>
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>
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>
First comparision of field measured PM10 concentration between the Northwestern China and Tibet Plateau
<p> field measured PM10 concentration in Fig. 3; relationships between shear velocity and PM10 concentration in Fig. 4.</p>
Environmental Impact Dataset of PM10 Concentration and Signal Strength
<p><span>This dataset captures 30 days of environmental data collected from densely populated urban and suburban areas in Thailand. It includes detailed measurements of PM10 air pollutant concentrations (µg/m³) alongside radio signal strength (RSSI in dBm) at 15-minute intervals throughout each day. Supplementary data on temperature (°C), humidity (%), wind speed (km/h), and other network parameters are also provided, enabling an in-depth analysis of how environmental and atmospheric factors interact with signal quality.</span></p> <p><strong><span>Data attributes:</span></strong></p> <p><span><span>ü<span> </span></span></span><em><u><span>Timestamp</span></u></em><em><span>:</span></em><span> Date and time of data collection at 15-minute intervals.</span></p> <p><span><span>ü<span> </span></span></span><em><u><span>Location</span></u></em><em><span>:</span></em><span> Type of area, categorized as urban or suburban.</span></p> <p><span><span>ü<span> </span></span></span><em><u><span>Latitude and Longitude</span></u></em><em><span>:</span></em><span> Geospatial coordinates of measurement points.</span></p> <p><span><span>ü<span> </span></span></span><em><u><span>PM10 Values (µg/m³)</span></u></em><em><span>:</span></em><span> Particulate matter concentrations based on AQI standards.</span></p> <p><span><span>ü<span> </span></span></span><em><u><span>AQI Category</span></u></em><em><span>:</span></em><span> Air Quality Index categorization (e.g., Severe, Very Poor).</span></p> <p><span><span>ü<span> </span></span></span><em><u><span>Temperature (°C)</span></u></em><em><span>:</span></em><span> Recorded ambient temperature.</span></p> <p><span><span>ü<span> </span></span></span><em><u><span>Humidity (%)</span></u></em><em><span>:</span></em><span> Relative humidity percentage.</span></p> <p><span><span>ü<span> </span></span></span><em><u><span>Wind Speed (km/h)</span></u></em><em><span>:</span></em><span> Wind speed at the time of data collection.</span></p> <p><span><span>ü<span> </span></span></span><em><u><span>RSSI (dBm)</span></u></em><em><span>:</span></em><span> Received signal strength indicator in dBm, a key feature for analyzing signal attenuation.</span></p> <p><span><span>ü<span> </span></span></span><em><u><span>Transmission Power (dBm)</span></u></em><em><span>:</span></em><span> Power of signal transmission.</span></p> <p><span><span>ü<span> </span></span></span><em><u><span>Frequency Band (MHz)</span></u></em><em><span>:</span></em><span> Network frequency used during data recording.</span></p> <p><span><span>ü<span> </span></span></span><em><u><span>Signal Strength</span></u></em><em><span>:</span></em><span> Categorical signal quality (Excellent, Fair).</span></p> <p><span>This dataset is valuable for research on the effects of air pollution on wireless signal propagation, and it offers a basis for further sensitivity analysis related to signal degradation under various environmental conditions.</span></p>
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