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247 results for “PM2.5”
Costs of attributable burden disease to PM2.5 ambient air pollution exposure in Medellín, Colombia, 2010-2016
<pre>The repository includes the consolidated databases design to analyze the economic cost of local attributable burden disease to PM2.5 ambient air pollution in Medellín from 2010-2016.</pre>
Global PM2.5 Dataset: Hybrid Calibration of CAMS and MERRA-2 PM2.5 Reanalysis Products during 2017-2019
<p>By integrating two reanalysis PM<sub>2.5</sub> products (CAMSRA, MERRA-2), a <strong>global daily hybrid-calibrated PM<sub>2.5</sub> concentration dataset</strong> is generated through the proposed CM-HC scheme with ERT model. This products include <strong>1095</strong> global PM<sub>2.5</sub> GeoTIFF files, starting from Jan 01, 2017 to Dec 31, 2019 (about <strong>0.95GB</strong> memory after uncompressing this zip file).</p> <p>To prove the superiority of this product, some experiments are implemented as follows: comparing with 1) two original products; 2) results of two separate calibration schemes with ERT; 3) results of CM-HC with other three ML models (RF, GBDT, XGBoost). Above analyses include two aspects, that is, the accuracy results and mapping effects. More details can be viewed at the thesis.</p> <p>One <strong>GeoTIFF</strong> file includes one-day calibrated PM<sub>2.5</sub> data. It is worth noting that the files contain the data of ocean area, however, our thesis only shows the results of land area in order to visually display the mapping effect before and after calibrating. Also, there is no site on the sea to validate in our study.</p>
Global PM2.5 Dataset: Hybrid Calibration of CAMS and MERRA-2 PM2.5 Reanalysis Products during 2017-2019
<p>By integrating two reanalysis PM<sub>2.5</sub> products (CAMSRA, MERRA-2), a <strong>global daily hybrid-calibrated PM<sub>2.5</sub> concentration dataset</strong> is generated through the proposed CM-HC scheme with ERT model. This products include <strong>1095</strong> global PM<sub>2.5</sub> GeoTIFF files, starting from Jan 01, 2017 to Dec 31, 2019 (about <strong>0.95GB</strong> memory after uncompressing this zip file).</p> <p>To prove the superiority of this product, some experiments are implemented as follows: comparing with 1) two original products; 2) results of two separate calibration schemes with ERT; 3) results of CM-HC with other three ML models (RF, GBDT, XGBoost). Above analyses include two aspects, that is, the accuracy results and mapping effects. More details can be viewed at the thesis.</p> <p>One <strong>GeoTIFF</strong> file includes one-day calibrated PM<sub>2.5</sub> data. It is worth noting that the files contain the data of ocean area, however, our thesis only shows the results of land area in order to visually display the mapping effect before and after calibrating. Also, there is no site on the sea to validate in our study.</p>
Full-coverage 1 km daily ambient PM2.5 and O3 concentrations of China in 2005-2017 based on multi-variable random forest model
<p>The aim of our study was to construct random forest models with high-performance, and estimate daily average PM<sub>2.5</sub> concentration and O<sub>3</sub> daily maximum 8h average concentration (O<sub>3</sub>-8hmax) of China in 2005-2017 at a spatial resolution of 1km×1km. The model variables included meteorological variables, satellite data, chemical transport model output, geographic variables and socioeconomic variables. Random forest model based on ten-fold cross validation was established, and spatial and temporal validations were performed to evaluate the model performance. According to our sample-based division method, the daily, monthly and yearly simulations of PM<sub>2.5</sub> gave average model fitting R<sup>2</sup> values of 0.85, 0.88 and 0.90, respectively; these R<sup>2</sup> values were 0.77, 0.77, and 0.69 for O<sub>3</sub>-8hmax, respectively. The meteorological variables and their lagged values can significantly affect both PM<sub>2.5</sub> and O<sub>3</sub>-8hmax simulations. During 2005-2017, PM<sub>2.5</sub> exhibited an overall downward trend, while ambient O<sub>3</sub> experienced an upward trend. Whilst the spatial patterns of PM<sub>2.5</sub> and O<sub>3</sub>-8hmax barely changed between 2005 and 2017, the temporal trend had spatial characteristic.</p> <p>Each dataset is the annual mean concentration of PM<sub>2.5</sub> or O<sub>3</sub>-8hmax based on the standard grid (Grid.csv) for that year. The coordinate system of the grid is WGS-84.</p>
Impacts of biomass burning in peninsular Southeast Asia on PM2.5 concentration and ozone formation in southern China during springtime – A case study
<p>Abstract: Biomass burning (BB) affects fine particulate matter (PM<sub>2.5</sub>) and ozone (O<sub>3</sub>) formations by emitting their gaseous precursors and primary aerosols. Impacts of BB in peninsular Southeast Asia (BB-PSEA) are evaluated on the PM<sub>2.5</sub> and O<sub>3</sub> formations in southern China, using a source-oriented WRF-Chem model to simulate an air pollution episode from 21 to 25 March 2015. The source-oriented model separates the emission from the BB-PSEA and other sources and is able to evaluate the effect of aerosol-radiation interactions (ARI) and aerosol-photolysis interactions (API) from the BB-PSEA. Comparisons with observations reveal that the model performs well in simulating the air pollution episode. Sensitivity experiments show that BB-PSEA increases PM<sub>2.5</sub> concentrations by 39.3 μg m<sup>-3</sup> (68.0%) in Yunnan Province (YNP) and 8.4 μg m<sup>-3</sup> (24.1%) in other downwind areas (ODA) in southern China (including the provinces of Guizhou, Guangxi, Hunan, Guangdong, Jiangxi, Fujian, and Zhejiang) on the regional average. The PM<sub>2.5</sub> enhancement is mainly contributed by primary aerosols in YNP but by secondary aerosols in the ODA. The BB-PSEA increases O<sub>3</sub> concentrations of 18.1 μg m<sup>-3 </sup>(19.4%) in YNP and decreases O<sub>3 </sub>concentrations in the ODA by 3.7 μg m<sup>-3</sup> (5.3%). The O<sub>3</sub> increase in YNP is contributed by the gaseous emissions of the BB-PSEA, and the O<sub>3</sub> decrease in the ODA is caused by the effects of ARI and API of the BB-PSEA. The NH<sub>3</sub> emissions from the BB-PSEA plays a key role in enhancing secondary inorganic aerosols in southern China, and also determine the PM<sub>2.5</sub> increase in the ODA.</p>
Process-level quantification on opposite PM2.5 changes during COVID-19 lockdown over North China Plain
<p>Observations and WRF-Chem simulation results for manuscript (Process-level quantification on opposite PM<sub>2.5</sub> changes during COVID-19 lockdown over North China Plain) submitted to GRL</p>
Surface hourly measurement data of O3, NO2 and PM2.5 for "Large modeling uncertainty in projecting decadal surface ozone changes over urban and industrial regions of China"
<p>Surface hourly measurement data of O3, NO2 and PM2.5 during summer of 2017.</p> <p>In the .csv files, the first column contains the ID for each measurement site. "lon", "lat" are longitude and latitude, respectively.</p> <p>Date format is "YYYYMMDD_hour".</p>
Molecular and 13C isotopic composition of diacids and related compounds in wintertime PM2.5 at three sites over Northeast Asia – Data set
<p>To investigate the spatial changes in atmospheric organic aerosols (OA) loading and composition over the northeast Asian region, fine aerosols (PM<sub>2.5</sub>) were collected at three sites: Tianjin (TJ), North China and in Padori (PD) and Daejeon (DJ), South Korea, during winter 2019. We studied the molecular distributions and compound-specific carbon isotopic composition (δ<sup>13</sup>C) of dicarboxylic acids (diacids), oxocarboxylic acids (oxoacids) and α-dicarbonyls as well as the carbonaceous and inorganic ionic components in two sets of selected samples, representing a clean and a polluted period, during the campaign. Based on the molecular distributions and δ<sup>13</sup>C of diacids and related compounds and mass ratios and linear relations of selected species, we discuss the origins of OA and the influence of long-range transported air masses on their loading and composition over Northeast Asia.</p>
QGIS Data for Canadian Population, PM2.5, Nighttime lights.
<p>This is the dataset for the QGIS analysis for Canadian Population, PM2.5, Nighttime lights. </p>
Dataset for Optimal reactive nitrogen control pathways identified for cost-effective PM2.5 mitigation in Europe
<p>This folder includes six folders:</p> <p>The "Nr anthropogenic emissions" folder includes the anthropogenic Nr emissions used in the baseline simulations for January, April, July, and October 2015.</p> <p>The "Concentration" folder includes daily surface ammonia (NH<sub>3</sub>), nitric acid (HNO<sub>3</sub>), nitrate (NO<sub>3</sub><sup>-</sup>), ammonium (NH<sub>4</sub><sup>+</sup>), sulfate (SO<sub>4</sub><sup>2-</sup>), and fine particulate matter (PM<sub>2.5</sub>) concentrations for January, April, July, and October 2015 from WRF-Chem simulations (one BASE simulation and twelve sensitivity simulations as stated in Method).</p> <p>The "N-share" folder includes the N-share caused by anthropogenic Nr (NH<sub>3</sub> and NO<sub>x</sub>)/ NH<sub>3</sub>/NO<sub>x</sub> emissions in 2015.</p> <p>The "Health" folder includes the PM2.5-related premature deaths from WRF-Chem combined with GEMM simulations in 2015. </p> <p>The "Instant efficiency" folder includes the instant efficiency of Nr/ NH<sub>3</sub>/NO<sub>x</sub> emission controls from WRF-Chem simulations in 2015. </p> <p>The "G ratio" folder includes the G ratio from WRF-Chem simulations for the year 2015 and the indicator months (January, April, July, and October). </p> <p>For any questions, please contact Zehui Liu<br> Email: liuzh18@pku.edu.cn<br> </p>
Aerosol indirect-effect-derived PM2.5 contributions during the development of convective clouds over the Yellow Sea in July of 2017
<p>Meteorological processes are investigated during the development of deep convective clouds over the Yellow Sea inside a quasi-stationary rainy front in the East Asian region. WRF-Chem simulations were conducted to assess the aerosol-indirect-effect (AIE)-derived PM<sub>2.5</sub> contributions to the cloud microphysical process in deep convective clouds and the associated precipitation.</p> <p>In our dataset, you can find output of WRF-Chem baseline simulations (BASE) and other sensitivity simulated outputs (CONTROL). Furthermore, model variables of precipitations, cloud top temperature (CTT), surface-level pressure (SLP), convective available potential energy (CAPE), and hydrometer mixing ratios of cloud water (qcloud), raindrops (qrain), and ice (qice) can be found in these zip files.</p> <p>If you have any difficulty downloading these datasets, please feel free to contact the first author, kimhsung@gmail.com. All references and acknowledgments can be found in our paper</p>
Dataset for "Calibration of CAMS PM2.5 data over Hungary: A machine learning approach"
<p>This study utilized air quality information from eleven particular checking locales arranged all through Hungary. The informational index contains in-situ estimations of particulate matter with a width of 2.5 micrometers or less (PM2.5). Moreover, the dataset incorporates matching PM2.5 gauges from the Copernicus Climate Checking Administration (CAMS) model, which makes it a valuable asset for correlation and adjustment. The dataset incorporates various seasons, considering an exhaustive assessment of PM tainting under different climatic circumstances.</p>
Gap-free 1km PM2.5 dataset in China (2000-present)
<p>This is the monthly PM2.5 estimates across China from 2000 to 2022. If you want daily dataset, please go to <a href="https://zenodo.org/record/7229348">10.5281/zenodo.7229348</a>. More datasets can be found on the right navigation panel of <a href="https://zenodo.org/record/4569557">10.5281/zenodo.4569557</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>We have estimated full-coverage, daily 1-km PM2.5 data from 2000 to 2020 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 monthly average data during 2000-2022 are released here in CSV format (if you want GEOTIFF files, please go to <a href="https://zenodo.org/record/8347128">10.5281/zenodo.8347128</a>) 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><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> </p> <p> </p>
Allegheny County PM2.5 LCS data
<p>Calibrated 15-min data from low-cost PM2.5 sensors deployed as a part of Center for Air, Climate, and Energy Solutions (CACES) air quality monitoring network.</p>
Data included in "Canopy leaching rather than desorption of PM2.5 from leaves is the dominant source of throughfall dissolved organic carbon in forest"
<p>Concentrations, optical properties, and carbon isotopic ratios of dissolved organic carbon in precipitation and throughfall, as well as concentrations and carbon isotopic ratios of total carbon in PM2.5, at the Taehwa Research Forest in South Korea</p> <p> </p>
Gap-free 1-km PM2.5 dataset in China (2000-present)
<p>This is the monthly PM2.5 estimates across China from 2000 to 2022. If you want daily dataset, please go to <a href="https://zenodo.org/record/7229348">10.5281/zenodo.7229348</a>. More datasets can be found on the right navigation panel of <a href="https://zenodo.org/record/4569557">10.5281/zenodo.4569557</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>We have estimated full-coverage, daily 1-km PM2.5 data from 2000 to 2020 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 monthly average data during 2000-2022 are released here GEOTIFF format (if you want CSV files, please go to <a href="https://zenodo.org/record/8084388">10.5281/zenodo.8084388</a>) and free for non-commercial use. <i><strong>If you want use our dataset, please cite the following publication. </strong></i></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><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. </strong><i><strong>Journal of Environmental Management</strong></i><strong>, </strong><i><strong>342</strong></i><strong>, 118145.[</strong><a href="https://doi.org/10.1016/j.jenvman.2023.118145"><strong>url</strong></a><strong>]</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. </strong><i><strong>Atmospheric Research</strong></i><strong>, </strong><i><strong>281</strong></i><strong>, 106481.[</strong><a href="https://doi.org/10.1016/j.atmosres.2022.106481"><strong>url</strong></a><strong>]</strong></p><p> </p><p>If you want other atmospheric data, e.g., CO2 dataset, please go to <a href="https://zenodo.org/records/10022905">10.5281/zenodo.10022904.</a></p>
Model results for Statistical bias correction for CESM-simulated PM2.5
<p>This NC file includes CESM-simulated annual mean aerosol concentrations for 100 years. And the model results are used in paper Statistical bias correction for CESM-simulated PM2.5 (doi: 10.1088/2515-7620/acf917)</p>
Unchanged PM2.5 levels over Europe during COVID-19 were buffered by ammonia
Open the record for dataset details and reuse information.
Local incomplete combustion emissions define the PM2.5 oxidative potential in Northern India
Open the record for dataset details and reuse information.
New Mexico PM2.5 concentrations by ZIP code for 4 smoke exposure estimates
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.