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247 results for “PM2.5”
LGHAP v2: Global annual mean 1-km gap-free PM2.5 grids (2000-2021)
<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 dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.</p>
Historical PM2.5 dataset across North America
<p>This dataset is the estimated long-term (1981-2016) concentrations of ambient fine particulate matter across North America, which combines information from chemical transport modeling, satellite remote sensing, and ground-based monitoring. The estimates included information from updated historical emissions inventories and meteorological data, fine resolution satellite-based estimates of PM<sub>2.5</sub>, and ground-based measurements of PM<sub>2.5</sub>, PM<sub>10</sub> and total suspended particles (TSP) measurements.</p>
Knowledge-inspired fusion strategies for the inference of PM2.5 values with a Neural Network - CAMS data for experiments
<p>Contains data generated by the CAMS model (during a global reanalysis), used to train and evaluate the models presented article "Knowledge-inspired fusion strategies for the inference of PM2.5 values with a Neural Network" - DOI of this article will be provided as soon as it is available.</p> <p>This data can be downloaded from the Copernicus Atmospheric Data Store (https://ads.atmosphere.copernicus.eu/#!/home), and is also hosted by the ICARE Data and Services Center (https://www.icare.univ-lille.fr/).</p> <p>This dataset only contains the specific data collection used for the experiments presented in aforementioned article. It is only a portion of the data available from these two websites.</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>
Downscaled Base, Sector and Fuel based PM2.5 from Stretched Grid Simulations using GEOS-Chem High Performance over South Asia.
<p>The <a href="https://zenodo.org/api/files/aed6de20-c761-4560-9cb8-b8f9e4e7b038/Gridded_Base_Sector_Fuel_PM25_South_Asia.mat">Gridded_Base_Sector_Fuel_PM25_South_Asia.mat</a> file that contains LAT_South_Asia, LON_South_Asia, PM25_base, PM25_sectors,PM25_fuels</p> <p>This is the order of the gridded sectors*:</p> <p>PM25_sectors(:,:,1) = AFCID;</p> <p>PM25_sectors(:,:,2) = OPEN_FIRES;</p> <p>PM25_sectors(:,:,3) = INDUSTRY;</p> <p>PM25_sectors(:,:,4) = POWER GENERATION;</p> <p>PM25_sectors(:,:,5) = RESIDENTIAL COMBUSTION;</p> <p>PM25_sectors(:,:,6) = TRANSPORT;</p> <p>PM25_sectors(:,:,7) = WASTE ;</p> <p>PM25_sectors(:,:,8) = AGRICULTURE ;</p> <p>PM25_sectors(:,:,9) = OTHER ;</p> <p>PM25_fuels(:,:,1) = BIOFUEL;</p> <p>PM25_fuels(:,:,2) = REMAINING_SOURCES;</p> <p>PM25_fuels(:,:,3) = COAL;</p> <p>PM25_fuels(:,:,4) = OIL_AND_GAS;</p> <p>PM25_fuels(:,:,5) = DUST_AND_FIRES;</p> <p>* The sectors here have been customized to prioritize particular sectors by adding lesser contributing sectors together. Please contact the author for more information on this. </p> <p>The NetCDFs contain the scaled ratios between CEDS 2019 and 2017 for Emissions for 31 species that were used in the simulations.</p>
Full-coverage high-resolution (Daily, 1-km) PM2.5 dataset in China (2000-present)
<p>We have estimated full-coverage, daily 1-km PM2.5 data from 2000 to 2022 in China using a random forest-based hindcast modeling method. <strong>Our modeling method focused on improving pre-2013 PM2.5 estimates because for those years no available PM2.5 measurements can be directly used for constructing the model and evaluating the model performance. </strong>In our proposed method, observed predictor information before 2013 was incoporated into the modeling for the first time. Multiple sources were used as inputs, including MAIAC AOD, meteorological data from CMA, reanalysis data from ERA-5, and other land-related data. The daily average data during 2000-2022 are released here and free for non-commercial use. <em><strong>If you want use our dataset, please cite the following publication. </strong></em></p> <p>The estimates in 2021-2022 are separately predicted using the same modeling method developed in the publication below and samples in the corresponding predictive year (sample-based 10-fold cross validation R2 [RMSE] values are 0.91 [8.84 ug/m3] for 2021 and 0.93 [7.42 ug/m3] for 2022, respectively. </p> <p> </p> <p><strong>-He, Q., Ye, T., Wang, W., Luo, M., Song, Y., & Zhang, M. (2023). Spatiotemporally continuous estimates of daily 1-km PM2. 5 concentrations and their long-term exposure in China from 2000 to 2020. <em>Journal of Environmental Management</em>, <em>342</em>, 118145.[<a href="https://doi.org/10.1016/j.jenvman.2023.118145">url</a>]</strong></p> <p><strong>-He, Q., Wang, W., Song, Y., Zhang, M., & Huang, B. (2023). Spatiotemporal high-resolution imputation modeling of aerosol optical depth for investigating its full-coverage variation in China from 2003 to 2020. <em>Atmospheric Research</em>, <em>281</em>, 106481.[<a href="https://doi.org/10.1016/j.atmosres.2022.106481">url</a>]</strong></p> <p>Full-coverage daily estimates spanning the years 2015 to Jun 2021 are archived here. These records, organized by month, are available for download in CSV format. For Jul-Dec 2021, please go to <a href="https://zenodo.org/record/8084388">10.5281/zenodo.8084388</a>.</p> <p>If you want more data (e.g.daily estimates before 2015), have any question, or further collaborate with us, please contact us via qqhe@whut.edu.cn.</p> <p>If you want to use <strong>monthly</strong> estimates from 2000 to 2022, please go to <a href="https://zenodo.org/record/8084388">10.5281/zenodo.8084388</a>.</p> <p><strong>We also estimate other atmospheric data:</strong></p> <p>For full-coverage, 1-km, AOD data in China, please go to <a href="https://dataverse.harvard.edu/dataverse/atmospheric_data_by_WHUT">harvard dataverse</a>. This dataset was imputed based on MODIS MAIAC 1-km AOD retrievals.</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 parent CARB 19RD004: Daily pollutant concentrations of NO2, PM2.5 and O3 of 100 m resolution for California 2012-2019
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Flint Hills, KS PurpleAir PM2.5 data from the 2022 prescribed fire season
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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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Spatially interpolated non-smoke and smoke PM2.5 concentrations for the US from 2006-2023
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PurpleAir PM2.5 from the 2022-23 Florida agricultural-fire season
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Data from: PM2.5 exposure disparities persist despite strict vehicle emissions controls in California
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Dataset 4 of 8: Particulate matter PM2.5 concentrations in California (2014-2015) (CARB 19RD004)
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Closure Study on Hygroscopic Properties of Water-soluble Matter in Atmospheric PM2.5 at a Rural Site in Northwest China
<p>The data supports the manuscript entitled “<strong>Closure Study on </strong><strong>Hygroscopic Properties</strong> <strong>of Water-soluble Matter in Atmospheric PM<sub>2.5</sub> at a Rural Site in Northwest China”</strong>. The data can be used freely for scientific purposes with the appropriate citation.</p>
ChinaHighPM2.5: MODIS/Terra+Aqua 1 km Ground-level PM2.5 Dataset for the Beijing-Tianjin-Hebei Region
<p>ChinaHighPM<sub>2.5</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). This dataset is generated from MODIS/Terra+Aqua MAIAC AOD products together with other auxiliary data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using the linear mixed effect (LME) model. </p> <p>This is the MODIS/Terra+Aqua monthly 1 km ground-level PM<sub>2.5</sub> dataset in the Beijing-Tianjin-Hebei region from 2000 to 2018, and this dataset yields a high quality with a cross-validation coefficient of determination (CV-R<sup>2</sup>) reaching 0.85 and a root-mean-square error (RMSE) of 21.49 µg m<sup>-3</sup> on a daily basis.</p> <p>If you use this dataset for related scientific research, please cite the corresponding reference (Xue et al., 2021, JCP):</p> <p>Xue, W., Zhang, J., Zhong, C., Li, X., and Wei, J. Spatiotemporal PM<sub>2.5</sub> variations and its response to the industrial structure from 2000 to 2018 in the Beijing-Tianjin-Hebei region, <em>Journal of Cleaner Production</em>, 2021, 279, 123742. https://doi.org/10.1016/j.jclepro.2020.123742</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>
Dataset for the decay process of PM2.5 pollution episodes around Beijing
<p>PM2.5 concentrations in the 28 cities around Beijing (Apm); </p> <p>Time series of the decay phase days (LocaDate);</p> <p>Time series of the dry-day decay phase days (LocaDateNoPre)</p>
InMAP Source-Receptor PM2.5 Concentrations from 2014 EPA NEI
<p>These are the PM2.5 concentration datasets computed using the InMAP source-receptor (SR) matrices, with the 2014 EPA National Emissions Inventory (NEI) as inputs.</p> <p>There are 120 total files in this zip file:</p> <ol> <li>19 sectors * 5 pollutants (NH<sub>4</sub>, NO<sub>3</sub>, primary PM<sub>2.5</sub>, SO<sub>4</sub>, SOA) = 95 files, representing the <strong>contribution of each pollutant in each of those sectors to the total PM<sub>2.5</sub> concentration</strong>; these files are directly calculated from the NEI and SR matrices;</li> <li>An additional 19 files representing <strong>each sector's total contribution to the PM<sub>2.5</sub> concentration</strong>, calculated by summing together the contributions from all 5 of the pollutants for each of the 19 sectors,</li> <li>An additional 5 files representing the <strong>overall PM<sub>2.5</sub> concentration across all sectors</strong>, calculated by summing together the contributions from all of the 19 sectors, for each of the 5 pollutants,</li> <li>An additional 1 file representing the <strong>overall total PM<sub>2.5</sub> concentration across all sectors and pollutants</strong>, calculated by summing together the contributions of all 19 sectors and all 5 pollutants.</li> </ol> <p>Every file is saved as a .mat file and can be loaded into MATLAB using the load() function. Each .mat file has dimensions of 52411x1, where each value represents a PM<sub>2.5</sub> concentration in one of the 52411 InMAP grid cells. You will need to download the InMAP grid shapefile in order to project and map this data.</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>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.