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32 results for “PM10”
Monthly averaged 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 AOD, PM2.5 and PM10 concentration data with monthly 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>
Spectral dependence of light absorption and direct radiative forcing of rural carbonaceous aerosol in TSP, PM10, PM2.5, and PM0.1 in northwestern China
<p>Black carbon (BC) and brown carbon (BrC) are major light absorbing components of aerosol, affecting visibility, radiative forcing balance and human health. In this study, we investigated the light absorption and radiative forcing of carbonaceous aerosol in the total suspended particle (TSP), coarse particle (PM<sub>10</sub>: particulate matter with an aerodynamic diameter less than 10 μm, Dp≤10 μm), fine particle (PM<sub>2.5</sub>: Dp≤2.5 μm), and nanoparticle (PM<sub>0.1</sub>: Dp≤0.1 μm) in a rural area of Guanzhong Plain, China. Similar variations of light absorption coefficients and the absorption Ångstrom exponent (AAE) of TSP, PM<sub>10</sub>, and PM<sub>2.5</sub> were observed. Lower light absorption coefficients and higher AAEs were obtained for PM<sub>0.1 </sub>compared with other particle sizes. The direct radiative forcing (DRE) efficiency of BC decreased with size bins of TSP, PM<sub>10</sub>, PM<sub>2.5</sub>, and PM<sub>0.1</sub>, respectively. The DRE of BCs for all particle sizes at top atmosphere (TOA), surface atmosphere (SUF) and the whole atmosphere (ATM) were estimated, and the levels in TSP were ~3.7 times higher than those in PM<sub>0.1</sub>. The optical properties of primary and secondary BrC (PBrC and SBrC) in PM<sub>0.1</sub> were further analyzed. The levels of AAEs indicated that the light absorbing of SBrC was more wavelength dependent than PBrC in PM<sub>0.1</sub>. The DRE of BC, PBrC, and SBrC in PM<sub>0.1</sub> were estimated firstly with the values of 19.9, 2.1, and 1.1 Wm<sup>-2</sup> in the ATM, respectively.</p>
Dataset of PM10 Level and Predictors in Addis Ababa
<p>The dataset for PM10 concentration prediction was compiled from 25 predictor variables and PM10 measurements from 11 monitoring stations in Addis Ababa. The final dataset consists of 38,408 observations spanning two years, from August 2021 to August 2023. The predictor variables encompass various factors impacting PM10 levels, including atmospheric conditions such as atmospheric density (AD), temperature (Ta), wind exposure (WEX), wind speed (WS), and rainfall (RF); urban features like night light (NL), land surface temperature (LST), urban index (UI), road density (RD), and building density (BD); terrain characteristics such as elevation (ELV), slope (SLP), topographic wetness index (TWI), terrain roughness index (TRI), and Soil-Adjusted Vegetation Index (SAVI); and proximity to potential PM10 sources including airports, petrol stations, roads, quarries, railways, markets, waste sites, bus stations, construction sites, and industries. This dataset facilitates the development of robust predictive models and supports the creation of effective air quality management strategies for Addis Ababa and other urban areas facing similar challenges.</p>
Data from: Variability of the atmospheric PM10 microbiome in three climatic regions of France
<p>Air pollution is a major public-health concern and it is recognized that particulate matter causes damage to human health through oxidative-stress, being responsible for several million premature deaths worldwide each year. Recent findings showed that, airborne microorganisms/spores can modulate aerosol toxicity by altering the oxidative potential of PM10. Primary Biogenic Organic Aerosols (PBOA) appears to be produced by only few genera of microorganisms, emitted by surrounding vegetation in the case of a regionally-homogeneous field site. This study presents the first comprehensive description of the structure and main sources of airborne microbial communities associated with the temporal trends in PM10 SC concentrations at 3 French sites, under different climates. By combining sugar chemistry and DNA Metabarcoding approaches, we intended to identify PM10-associated microbial communities and their main sources at three climatically different sampling-sites in France during summer 2018. This study accounted also for the interannual variability of the structure of summer airborne microbial community associated with PM10-SC concentrations during a consecutive 2-year survey at one site. Our results showed that the temporal evolutions of SC in PM10 in the three sites are associated with the abundance of only few specific airborne fungal and bacterial taxa. These taxa differ significantly between the 3 climatic regions studied. The structure of microbial communities associated with PM10 SC concentrations during a consecutive 2-year survey remained stable in the rural area. The atmospheric concentration levels of PM10 SC-species vary significantly between the 3 studied sites, but with no clear difference according to site typology (rural vs urban), suggesting that SC emissions are more related to regional climatic characteristics. The overall microbial beta diversity in PM10 samples is significantly different from that of the main vegetation around the studied urban sites. This indicates that airborne microorganisms at such urban sites do not originate only from the immediate surrounding vegetation, which is contrasting with observations at the scale of a regionally homogeneous rural site made in 2017. These results improve our understanding of the spatial behavior of tracers of PBOA emission sources, which need to be better characterized to further implement this important OM-mass fraction into CTM models.</p>
Data from: Variability of the atmospheric PM10 microbiome in three climatic regions of France
Open the record for dataset details and reuse information.
Effect of Particulate Matter (PM10) on innate immunity upon H5N1 infection
GEO Series GSE147227. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
Multielemental profile of PM10 of Buenos Aires, Argentina
<p>Content of 13 elements in airborne particulate matter less than 10 microns (PM10) collected in representative zones of the metropolitan area of Buenos Aires in 2004. The sampling strategy followed consisted in collecting simultaneously 67 samples of PM10 in 9 sampling sites covering an area of about 30 km2 during one week. The collection was performed on ash-free fibre-glass filters using high volume samplers. Reported elements: Al, Ca, Cu, Fe, Mn, Mo, Ni, Pb, S, Sb, Sn, Zn and Zr, determined by inductively coupled plasma-optical emission spectrometry (ICP-OES) and inductively coupled plasma-mass spectrometry (ICP-MS)..</p>
SPARCS_WP4_Leipzig_City_Small particulates (pm10) Measurements
<p>Measurement of small particulates (pm10) captured for the city of Leipzig with the annual average value measured in µg/m³ covering the period between 2018 and 2024.</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>
ChinaHighPM10: Big Data Seamless 10 km Ground-level PM10 Dataset for China (Closed)
<p>ChinaHighPM<sub>10</sub> is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. </p> <p>This is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 10 km (i.e., D10K, M10K, and Y10K) ground-level ground-level PM<sub>10</sub> products in China from 2013 to 2020. This dataset yields a high quality with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.86 and a root-mean-square error (RMSE) of 24.34 µg m<sup>-3</sup> on a daily basis.</p> <p><strong>Note that this dataset is closed access since a longer-term, high-resolution (1 km), and higher quality ChinaHighPM<sub>10</sub> dataset is available at: <a href="http://doi.org/10.5281/zenodo.3752465">http://doi.org/10.5281/zenodo.3752465</a></strong></p> <p><strong>More CHAP datasets of different air pollutants can be found at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></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>
ChinaHighPM10: MODIS/Terra+Aqua 1 km Ground-level PM10 Dataset for China (Closed)
<p>ChinaHighPM<sub>10</sub> is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. </p> <p>This is the MODIS/Terra+Aqua daily 1 km (D1K) ground-level PM<sub>10</sub> dataset in Eastern China from 2013 to 2020, and this dataset yields a high quality with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.86 and a root-mean-square error (RMSE) of 24.28 µg m<sup>-3</sup> on a daily basis.</p> <p><strong>Note that this dataset is closed access since a longer-term, high-resolution (1 km), and higher quality ChinaHighPM<sub>10</sub> dataset is available at: <a href="http://doi.org/10.5281/zenodo.3752465">http://doi.org/10.5281/zenodo.3752465</a></strong></p> <p><strong>More CHAP datasets of different air pollutants can be found at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p> <p> </p>
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