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323 results for “air pollution”
Dataset for "Public Health Benefits from Improved Identification of Severe Air Pollution Events with Geostationary Satellite Data"
<p>Dataset for "Public Health Benefits from Improved Identification of Severe Air Pollution Events with Geostationary Satellite Data" to be published in GeoHealth doi: 10.1029/2023GH000890</p>
Data related to "Complementary Taxation of Carbon Emissions and Local Air Pollution"
<p>This is the database necessary to run the underlying model codes in GAMS. See https://github.com/MathiasMier/CO2andAPtaxation for codes and description.</p>
MuAP Spatial distribution of various air pollutants in China at 1 km(SO2 2021-01-01:2023-12-31) (Version1.1)
<p>MuAP Spatial distribution of various air pollutants in China at 1 km(SO2)</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> <p>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.</p> <p>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</p> <p>Note: The MuAP for 2015-2020 can be obtained by:</p> <p>1.PM2.5:https://zenodo.org/records/8093749</p> <p>O3:https://zenodo.org/records/8180923</p> <p>SO2:https://zenodo.org/records/8093749</p> <p>NO2:Please contact the author at fjcyfeng@qq.com.</p> <p> </p> <p>2. Daily MuAP data volume exceeds Zenodo platform limits. Please contact the author at fjcyfeng@qq.com.</p> <p> </p>
MuAP Spatial distribution of various air pollutants in China at 1 km(O3 2021-01-01:2023-12-31) (Version1.1)
<p>MuAP Spatial distribution of various air pollutants in China at 1 km(O3)</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>
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>
Biomagnetic characterisation of air pollution particulates in Lahore, Pakistan
<p>This dataset includes raw magnetic data measured at room and low temperatures. </p>
Supplementary Data: Sensitivity of Air Pollution Exposure and Disease Burden to Emission Changes in China using Machine Learning Emulation.
<p>Temporary duplicate of:</p> <p>Conibear, L., Reddington, C. L., Silver, B. J., Chen, Y., Arnold, S. R., & Spracklen, D. V. (2022). <em>Supplementary Data: Sensitivity of Air Pollution Exposure and Disease Burden to Emission Changes in China using Machine Learning Emulation. University of Leeds. [Dataset]</em>. https://doi.org/doi.org/10.5518/1055</p>
Gaseous elementary mercury and other air pollutants data during COVID-19
<p>This dataset contains gaseous elementary mercury, particulate ions, organics, and trace metals, and meteorological parameters measured at the Dianshan Lake site in Shanghai during the 2020 COVID-19 period.</p>
Data for: COVID-19 lockdowns cause global air pollution declines
<p>Data in support of published manuscript: <a href="https://www.pnas.org/doi/full/10.1073/pnas.2006853117" rel="nofollow">https://www.pnas.org/doi/full/10.1073/pnas.2006853117</a></p> <p>For details of how the data are used in analysis please refer to the GitHub repository: https://github.com/NINAnor/covid19-air-pollution</p> <p>Authors of the paper acknowledge support from the <span>EU H2020 EXHAUSTION project.</span></p>
Supplementary material to Increasing health burdens driven by global trade induced air pollution
<p><span>Supplementary material to Increasing health burdens driven by global trade induced air pollution</span></p> <p><span>File Name: Supplementary Data 1 </span></p> <p><span>Description: The net flow tables of atmospheric emissions (kt) between worldwide regions in 2000, 2005, 2010, and 2015.</span></p> <p><span>File Name: Supplementary Data 2 </span></p> <p><span>Description: The PM2.5-related health burdens for all regions from 2000 to 2015 with confidence intervals</span></p> <p><span>File Name: Supplementary Data 3 </span></p> <p><span>Description: The contributions of different drivers to the changes in PM2.5-related health burdens for all regions from 2000 to 2015.</span></p>
Future intensification of co-occurrences of heat, PM2.5 and O3 extremes in China and India despite stringent air pollution controls
<p>This dataset provides WRF-Chem simulation outputs of the manuscript titled "Future intensification of co-occurrences of heat, PM2.5 and O3 extremes in China and India despite stringent air pollution controls".</p>
Association Between Air Pollution Exposure and Respiratory Function Decline in Urban Adults: A Cross-Sectional Study
<p>In our cross-sectional study examining the association between air pollution exposure and respiratory function decline in urban adults, we found compelling evidence of a significant relationship. Over a span of six months, we assessed respiratory function using spirometry measurements among 500 participants living in densely populated urban areas with varying levels of air pollution. Our results indicate a clear correlation between higher levels of particulate matter and nitrogen dioxide exposure and decreased lung function parameters, such as forced expiratory volume in one second (FEV1) and forced vital capacity (FVC). These findings underscore the critical impact of urban air quality on respiratory health, emphasizing the need for stringent air quality regulations and public health interventions to mitigate the adverse effects of air pollution on urban populations' respiratory well-being.</p>
Input data for the exposure assessment case study on air pollution and noise for the province of Utrecht, the Netherlands
<p>Input datasets for the case study described in the manuscript "A computational framework for agent-based assessment of multiple environmental exposures".<br>Use the free 7-Zip to uncompress. Uncompressed size ca 103 GiB.</p> <p>The datasets are licensed under a Creative Commons license (CC-BY 4.0). Contact: o.schmitz@uu.nl</p>
Interaction of household air pollution and healthy lifestyle on the risk of sarcopenia: China Health and Retirement Longitudinal Study
Open the record for dataset details and reuse information.
The code of Regional multi-Air Pollutant Assimilation System (RAPAS v1.0) for emission estimates
<p><strong>RAPAS</strong> is a Regional multi-Air Pollutant Assimilation System, which can quantitatively optimize gridded source emissions of CO, SO<sub>2</sub>, NO<sub>x</sub>, primary PM<sub>2.5</sub> and coarse PM<sub>10</sub> on regional scale via simultaneously assimilating surface measurements of CO, SO<sub>2</sub>, NO<sub>2</sub>, PM<sub>2.5</sub> and PM<sub>10</sub>.</p> <p>These codes correspond to the paper entitled "A Regional multi-Air Pollutant Assimilation System (RAPAS v1.0) for emission estimates: system development and application".</p> <p><strong>3DVar</strong> directory contains original GSI DA codes and extended codes by the author used to optimize CMAQ initial conditions.</p> <p><strong>EnKF</strong> directory contains DA codes originally created by the author used to optimize emissions.</p>
Effects of ozone air pollution on crop pollinators and pollination
<p>Article dataset.</p>
Supporting data for ''Nonlinearity of the cloud response postpones climate penalty of mitigating air pollution in polluted regions"
<p>Supporting data for our work on Nature Climate Change. You will find:</p> <p> - python codes for generating the plots in the manuscript.</p>
Urban and suburban decadal variations in air pollution of Beijing and its meteorological drivers
<p>The hourly PM<sub>2.5</sub>, PM<sub>10</sub>, CO, SO<sub>2</sub>, NO<sub>2</sub>, and O<sub>3</sub> data used in this study from the Beijing Municipal Ecological and Environmental Monitoring Center, and can be obtained from this ZIP file.</p>
Replication files for: "Air Pollution from Agricultural Fires Increases Hypertension Risk"
<p>Replication files for: "Air Pollution from Agricultural Fires Increases Hypertension Risk"</p>
Investigation of the summer 2018 European ozone air pollution episodes using novel satellite data and modelling - Dataset
Open the record for dataset details and reuse information.
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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)
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.