Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
247
datasets available to search
ShareScore release 0.9.0
Dataset results
247 results for “PM2.5”
Wildland fire PM2.5 modeled estimates for the US from 2008-2018
Open the record for dataset details and reuse information.
Wildfire, prescribed burn, and agricultural burn smoke PM2.5 estimates for CA, WA, and OR 2014-2020
Open the record for dataset details and reuse information.
The dataset of the manuscript "Numerical study of the initial condition and emission on simulating PM2.5 concentrations in Comprehensive Air Quality Model with extensions version 6.1 (CAMx v6.1): Taking Xi'an as example"
<ul> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/bcfile.rar?versionId=4909d094-5877-408e-bd4f-0c969c54e585">bcfile.rar</a>: the clean initial and boundary condition files.</li> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/Emis_forNov.rar?versionId=0a1e8b66-5157-4819-8c03-20fb7797d8ef">Emis_forNov.rar</a> and <a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/Emis_forDec.rar?versionId=d4db00f1-ec1b-4096-973e-6a87133e4eac">Emis_forDec.rar</a>: the emission files in November and December 2016.</li> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/tuvfile.rar?versionId=5dcf0977-e416-466e-9089-bbf0726c788d">tuvfile.rar</a> and <a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/o3mapfile.rar?versionId=9c25cae9-f00e-4ad3-b4dc-7a721f7f44d7">o3mapfile.rar</a>: the photolysis files.</li> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/camx.cp1.rar?versionId=09ded31b-4c42-4e40-a21c-0f18877e9e41">camx.cp[1-5].rar</a>: the results of sensitivity experiments for using clean initial condition files.</li> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/camx.r1120p1.rar?versionId=45d6e209-8e66-43ed-bc0b-dc8af5521352">camx.r1120p[1-3].rar</a>: the results of sensitivity experiments for R1120.</li> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/camx.r1124.rar?versionId=c138e436-0416-4701-948d-ce761cf6c5cf">camx.r1124.rar</a>: the results of sensitivity experiments for R1124.</li> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/contnuous_B12.rar?versionId=34485c43-77ac-4001-8a6d-a57b7ff821e3">contnuous_B12.rar</a>: the results of sensitivity experiments for CT12.</li> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/contnuous_B24.rar?versionId=0f325a61-f19c-4bac-b8b4-7e229c332bf9">contnuous_B24.rar</a>: the results of sensitivity experiments for CT24.</li> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/scripts.zip?versionId=b030444c-51a5-4673-b9d1-7e80ec42a3b9">scripts.zip</a>: all scripts covering every data processing action for all the results reported in the paper.</li> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/data.zip?versionId=52917a53-fca7-4a72-ba2f-a2ce7593adc4">data.zip</a>: final data tables used to plot figures and tables.</li> </ul>
A center of heavy PM2.5 pollution in the Yangtze River middle basin attributed to regional transport of PM2.5 over China
<p>The Multi-resolution Emission Inventory for China (MEIC) emission sources dataset and PM<sub>2.5</sub> datasets</p>
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>
Dataset for "Daily and Hourly Surface PM2.5 Estimation from Satellite AOD"
<p>Dataset for "Daily and Hourly Surface PM2.5 Estimation from Satellite AOD" submitted to Earth and Space Science.</p>
AirNow and Low Cost Sensor PM2.5 During Extreme Wildfires in Sonoma County, 2020
<p>This data collection includes AirNow measurements of PM2.5 from the Sebastopol station as well as PurpleAir measurements of PM2.5 within Sonoma County, California, USA. Data is archived for the manuscript "Air Quality Monitoring and the Safety of Farmworkers in Wildfire Mandatory Evacuation Zones". Code used to process and analyze data can be found on GitHub: https://github.com/rrbuchholz/pm25_atmosphere_analysis_2023</p>
The effect of emission source chemical profiles on simulated PM2.5 components: sensitivity analysis with CMAQ 5.0.2
<p>All input and model configuration data necessary to reproduce the results as well as all output data discussed in the article</p>
LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2001)
<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>
LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2013)
<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>
LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2015)
<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>
Real-world vehicular source indicators for exhaust and non-exhaust contribution to PM2.5 during peak and off-peak hours using tunnel measurement
<p>The data are the species concentrations, traffic and meteorological information during the sampling periods in WJL tunnel, and the estimated function of E<sub>N</sub> curve under vehicle electrification.</p>
Data archive for the peer-reviewed journal article "Major source categories of PM2.5 oxidative potential in wintertime Beijing and surroundings based on online dithiothreitol-based field measurements"
<p>This data archive accompanying the article "Major source categories of PM2.5 oxidative potential in wintertime Beijing and surroundings based on online dithiothreitol-based field measurements", which was accepted in April 2024 in the peer-reviewed journal <strong><em>Science of the Total Environment</em></strong>. This data archive contains the processed OPvDTT measurements, chemical speciation of PM2.5, and source contribution used in the manuscript.</p>
MuAP Spatial distribution of various air pollutants in China at 1 km(PM2.5 2021-01-01:2023-12-31) (Version1.1)
<p>MuAP Spatial distribution of various air pollutants in China at 1 km(PM2.5)</p> <p>Multiple air pollutions dataset (MuAP) </p> <p>Time frame: 2021-2023<br>Area: Most of China<br>Resolution: about 1km<br>File storage format: .xz and GeoTIFF<br>Spatial projection: WGS84<br>Daily file name: year_doy.tif (Daily MuAP data volume exceeds Zenodo platform limits. Please contact the author at fjcyfeng@qq.com.)</p> <p>Monthly file name: year_month.tif</p> <p>Yearly file name: year_month.tif</p> <p>Unit: Please divide by 10 when using. (ug/m3)</p> <p>When you download and use our data, please cite:</p> <ol> <li>Chi, Y., Zhan, Y., Wang, K., and Ye, H.: Sequential spatiotemporal distribution of PM<sub>2.5</sub>, SO<sub>2</sub> and Ozone in China from 2015 to 2020, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-76, in review, 2023.</li> <li>Chi, Y., Zhan, Y., Wang, K., & Ye, H. (2023). Spatial Distribution of Multiple Atmospheric Pollutants in China from 2015 to 2020. Remote Sensing, 15(24). doi:10.3390/rs15245705</li> </ol> <p>Note: The MuAP for 2015-2020 can be obtained by:</p> <p>1.</p> <ul> <li>PM2.5:https://zenodo.org/records/8093749</li> <li>O3:https://zenodo.org/records/8180923</li> <li>SO2:https://zenodo.org/records/8093749</li> <li>NO2:Please contact the author at fjcyfeng@qq.com.</li> </ul> <p> </p> <p>2. Daily MuAP data volume exceeds Zenodo platform limits. Please contact the author at fjcyfeng@qq.com.</p> <p> </p>
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>
PM2.5 composition and VOCs species measured in Tianjin in July-October, 2018
<p>This spreadsheet include the concentrations of water-soluble PM2.5 chemical species (sheet 1) and VOCs species (sheet 2) measured at NKUAQS site in July-October 2018 in Tianjin, China. The dataset was used to investigate changes in these species during tropical cyclone days with respect to normal days. Results were analyzed and presented in the paper entitled "Co-Occurrence of Surface O<sub>3</sub>, PM<sub>2.5</sub> Pollution, and Tropical Cyclones in China" (<a href="https://doi.org/10.1029/2021JD036310">https://doi.org/10.1029/2021JD036310</a>).</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>
PM2.5 data for "Contribution of fire emissions to PM2.5 and its transport mechanism over the Yungui Plateau, China during 2015–2019"
<p>PM2.5 data for "Contribution of fire emissions to PM2.5 and its transport mechanism over the Yungui Plateau, China during 2015–2019"</p>
Long-term (2003-2020) hourly 0.25° global PM2.5 dataset (DeepCAMS) Part-2: 2012-2020
<p>This is part II (2012-2020) of our DeepCAMS.</p> <p>Part I (2003-2011) can be found at: https://doi.org/10.5281/zenodo.6967082</p> <p>Usage: The raw data -- (scaling factor: 0.1) --> the true PM2.5 concentration</p> <p>Paper title: Generating a Long-term (2003-2020) hourly 0.25° global PM2.5 dataset via spatiotemporal downscaling of CAMS with deep learning (DeepCAMS)</p> <p>Paper doi: <a href="https://doi.org/10.1016/j.scitotenv.2022.157747">https://doi.org/10.1016/j.scitotenv.2022.157747</a></p> <pre>If you find our work helpful, please cite it. Thank you very much!</pre>
Treasure Bowl: PM2.5 Aggregation in the Eye of a Tropical Cyclone
<p>The measured data for the manuscript "Treasure Bowl: PM2.5 Aggregation in the Eye of a Tropical Cyclone"</p>
ScienceDex guides
Understand access before you commit
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.