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206 results for “air quality”
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>
Mobile and stationary air pollution measurements around an air quality monitoring site on Mäkelänkatu in Helsinki, Finland
<p>Mobile and stationary measurements of air pollution around an air quality monitoring site on Mäkelänkatu in Helsinki, Finland. The datasets are used in evaluating high-resolution air quality simulations conducted with the PALM model system 6.0. The dataset contains:</p> <ul> <li>drone_data: measurements of vertical profiles of lung-deposited surface area (LDSA) conducted using a drone in summer and winter 2017</li> <li>kumpula_airquality_data: <ul> <li>airquality: basic air quality observations from the SMEAR III station</li> <li>dmps: aerosol size distribution observations using DMPS from the SMEAR III station</li> </ul> </li> <li>met_data: <ul> <li>kivenlahti_mast_DDMMYYYY.txt: Kivenlahti mast observations</li> <li>Meteorology_YYMM_10.txt: SMEAR III observations</li> </ul> </li> <li>sniffer_data/long/[timeofday]YYYYMMDD.dat: horizontal distribution of air pollutants measured by the mobile laboratory Sniffer</li> <li>supersite_data: measurements from the air quality monitoring station on Mäkelänkatu <ul> <li>AQdata: air quality observations</li> <li>DMPS: aerosol size distribution observations using DMPS</li> <li>ACSM: aerosol chemical composition measurements using ACSM</li> </ul> </li> </ul> <p> </p>
TRAFAIR Air Quality Dashboard
<p>Videos that show the functionalities of the TRAFAIR Air Quality Dashboard: a web application that was active betweeen 2020 and the beginning of 2023. The dashboard was realized within the scope of the TRAFAIR Eurpean Project.</p>
Air Quality
<p>NO2 and O3 air concentration data aquired as baseline before the NBS implementation. The data were recorded in June 2019 in the proximity of the NBS and in a control site. In total 74 passive diffusion tubes (Gradko International Ltd) were installed in selected NBS of the three Front Runner Cities (Dortmund, Turin and Zagreb), in particular 16 in Dortmund (NBS3), 28 in Zagreb (NBS3 and NBS5) and 30 in Turin (NBS2, NBS3, NBS5). Half of the tubes monitored NO2 while the other half O3. The placed samplers were removed after 21 days and sent for analysis.</p> <p>More details are reported in Baldacchini, C. (2019): Monitoring and Assessment Plan, Deliverable No. 4.1, proGIreg. Horizon 2020 Grant Agreement No 776528, European Commission, 124.</p>
Application of regional meteorology and air quality models based on MIPS and LoongArch CPU Platform
<p>bcfile.zip: the clean boundary condition files.</p> <p>emisfile.zip: the emission files.</p> <p>icfile.zip: the clean initial condition files.</p> <p>tuvfile.zip and o3mapfile.zip: the photolysis files.</p> <p>outputfile.zip: the computation results outputted by CAMx model for MIPS and X86 platforms.</p> <p>wrfcamx.zip: meteorological files.</p> <p>outputfile_LoongArch_platform.tar.gz: the computation results outputted by CAMx model for LoongArch platforms.</p> <p>bin_executable_on_LoongArch_platform.tar.gz: The executable files of CAMx model which can run stably on LoongArch platform, including noMPI, MPICH and OpenMP version.</p> <p>bin_executable_on_MIPS_platform.tar.gz: The executable files of CAMx model which can run stably on MIPS platform, including noMPI, MPICH and OpenMP version</p>
Climate and air quality relevant output diagnostics from UKESM1 for the additional AerChemMIP simulation ssp370SSTpdEmis
<p>This dataset contains model output data for radiative fluxes, emissions, aerosols and ozone produced from an additional AerChemMIP model experiment where anthropogenic precursor emissions and trace gas constituents were held fixed at 2014 values. This model experiments was conducted by UKESM1, a model contributing to CMIP6, and was run over the period 2015 to 2100 to investigate the effect of anthropogenic emissions on near-term climate forcers. The atmosphere only CMIP6 configuration of UKESM1 was used to run this experiment. Model simulations were conducted at a global resolution of 1.875° x 1.25°.</p>
CAIRDIO simulation results and air-quality observations for Leipzig
<p>This dataset contains results with the model CAIRDIO applied for a simulation case study of Leipzig from 1-3 March 2020, and measured concentration vs. time series of black carbon and particulate matter at 5 different air-monitoring sites for comparison. </p>
Supplemental data and code for Improved air quality in China can enhance solar power performance and accelerate carbon neutrality targets
<p>Supplemental data and code for Improved air quality in China can enhance solar power performance and accelerate carbon neutrality targets</p>
Impacts of transboundary transport on coastal air quality of south China
<p>observation data for air pollutants from China’s Ministry of Ecology and Environment</p>
ground-based air quality measurements during the 2021 spring super dust storms
<p>The attachment stores the hourly ground-based PM10 concentration measurements from the China air quality monitoring network during the 2021 spring super dust storms. </p>
Smell Pittsburgh: Engaging Community Citizen Science for Air Quality
<p>Link to the files and description of the Smell Pittsburgh Dataset –<br> <a href="https://eur04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2FCMU-CREATE-Lab%2Fsmell-pittsburgh-prediction%2Ftree%2Fmaster%2Fdataset%2Fv2&data=05%7C01%7Cy.c.hsu%40uva.nl%7C89562067341d40d0bad308da2c475652%7Ca0f1cacd618c4403b94576fb3d6874e5%7C0%7C0%7C637870982141190827%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=gWn0nGRUl5EAHvDfJwTlTNJN%2BWFH62NX6Mw%2B2web6XE%3D&reserved=0">https://github.com/CMU-CREATE-Lab/smell-pittsburgh-prediction/tree/master/dataset/v2</a></p> <p>Smell Pittsburgh (<a href="https://smellpgh.org">https://smellpgh.org</a>) is a mobile application for crowdsourcing reports of bad odors, such as those generated from air pollution. The data is used to train a machine learning model to predict the presence of bad smell and create push notifications to inform citizens about the bad smell. The motivation, background, and design of the Smell Pittsburgh application is described in the following paper.</p> <ul> <li>Yen-Chia Hsu, Jennifer Cross, Paul Dille, Michael Tasota, Beatrice Dias, Randy Sargent, Ting-Hao (Kenneth) Huang, and Illah Nourbakhsh. 2020. Smell Pittsburgh: Engaging Community Citizen Science for Air Quality. ACM Transactions on Interactive Intelligent Systems. 10, 4, Article 32. DOI:<a href="https://doi.org/10.1145/3369397">https://doi.org/10.1145/3369397</a>. Preprint:<a href="https://arxiv.org/pdf/1912.11936.pdf">https://arxiv.org/pdf/1912.11936.pdf</a>.</li> </ul>
Tool and python programs for the paper "The Impact of Altering Emission Data Precision on Compression Efficiency and Accuracy of Simulations of the Community Multiscale Air Quality Model"
<p>Here is the content:</p> <p> * file dir_list which contains information about each file's content</p> <p> * the tool is used to alter a data file by keeping a specific number of significant digits for the paper "The Impact of Altering Emission Data Precision on Compression Efficiency and Accuracy of Simulations of the Community Multiscale Air Quality Model'</p> <p> * pythons program and its associated data to create each figure and table in the paper (data for Table 07 is not included due to size is larger than 50GB)</p>
Impacts of on-road vehicular emissions on U.S. air quality: A comparison of two mobile emission models (MOVES and FIVE)
<p>Here presented CMAQ outputs used for paper of "MImpacts of on-road vehicular emissions on U.S. air quality: A comparison of two mobile emission models (MOVES and FIVE)"</p>
Dataset for Co-benefit of forestation on ozone air quality and carbon storage in South China
<p>This folder includes three folders:</p> <p>The "Concentration_ppbv" folder includes hourly surface ozone (O3) concentrations (unit: ppbv) for April, July, and October 2015 from WRF-Chem simulations (one BASE simulation and ten sensitivity simulations as stated in Method).</p> <p>The "Concentration_ugm-3" folder includes hourly surface ozone (O3) concentrations (unit: ug/m3) for April, July, and October 2015 from WRF-Chem simulations (one BASE simulation and ten sensitivity simulations).</p> <p>The "GPP_damage" folder includes gross primary productivity (GPP) for the year 2015 from YIBs simulations. GGP_0, GPP_L, and GPP_H are results with no ozone damage, with low ozone damage, and with high ozone damage, respectively.</p> <p>Ten sensitivity simulations: Pre-greening, Canopy (Turbulence), Deposition, Emission, LAI2030, SSP126, SSP370, SSP126LAI2030, SSP370LAI2030, LAI2050.</p> <p>For any questions, please contact Zehui Liu<br>Email: liuzh18@pku.edu.cn</p> <p> </p>
Europe SSP air quality PM2.5 (VD Scaled) O3 mortality output
Open the record for dataset details and reuse information.
Model settings and surface measurements for air quality study
<ol> <li>Source code, run directories and initial condition for GEOS-Chem model simulations.</li> <li>Surface O3, NO2 and PM2.5 measurements in China in 2014-2019</li> </ol>
Air Quality Forecasts Improved by Combining Data Assimilation and Machine Learning with Satellite AOD
<p>Input data for random forest model. </p> <p> </p> <p>1) UM_RDAPS.egg file: It provides analysis and forecast products four times a day (00, 06, 12, 18 UTC) in 12 km x 12 km spatial resolution. In this study, analysis products were only considered as the input variables (i.e., 2m temperature and dew-point temperature, relative humidity (RH), maximum wind speed, visibility at height above the ground, planetary boundary layer height (PBLH), and surface pressure). The accumulated maximum wind speed during 1, 3, 5, 7 days were also used in this study.</p> <p>2) data_1.zip file: GOCI Aerosol product, MODIS Land cover, MODIS NDVI, Population density, Road density, SRTM_DEM. </p> <p> </p> <p>The detailed information of input variables is written in the supporting information of the paper.</p> <p> </p> <p> </p> <p> </p>
City scale particulate matter monitoring using LoRaWAN based air quality IoT devices
<p>Air Quality (AQ) is a very topical issue for many cities and has a direct impact on citizen health. The AQ of a large UK city is being investigated using low-cost Particulate Matter (PM) sensors, and the results obtained by these sensors have been compared with government operated AQ stations. In the first pilot deployment six AQ Internet of Things (IoT) devices have been designed and built, each with four different low-cost PM sensors, and they have been deployed at two locations within the city. These devices are equipped with LoRaWAN wireless network transceivers to test city scale Low-Power Wide Area Network (LPWAN) coverage. The study concludes that i) the physical device developed can operate at a city scale ii) some low-cost PM sensors are viable for monitoring AQ and for detecting PM trends iii) LoRaWAN is suitable for city scale sensor coverage where connectivity is an issue. Based on the findings from this first pilot project a larger LoRaWAN enabled AQ sensor network is being deployed across the city of Southampton in the UK.</p>
Supplementary Materials for 'Spatiotemporal Prediction of Air Quality Using Machine Learning Techniques'
<p>This package includes supplementary materials used to implement air quality prediction in the city of Madrid. It consists of two main subdirectories: Data and Code. The Data directory contains Raw-Data (air quality, meteorological and traffic data from the period of January-June 2019 and January-June 2020, and the location of air quality and meteorological monitoring stations and traffic measurement points of the city of Madrid) and Processed-Data (the output after raws data has gone through the workflow to meet the requirements corresponding to the implementation of the proposed forecasting approaches). The Code directory contains Process Raw Data, Chapter4-ConvLSTM, Chapter5-BiConvLSTM, and Chapter6-A3T_GCN, which provides the procedure for constructing and implementing the proposed approaches.</p>
Strengthened PM2.5 air quality improvement and health benefits by synergies of carbon peak, carbon neutrality, and clean air policies in China
<p>Dataset and code used in this research: (1) emission, major air pollutants (i.e., SO2, NOx, PM25, NMVOCs, NH3), and CO2 emissions during 2020-2060 under the scenario ensembles (i.e., reference, clean air, on-time peak-clean air, on-time peak-net zero-clean air, early peak-net zero-clean air). (2) PM2.5 exposure (NetCDF, 0.1×0.1), future PM2.5 concentrations (2025, 2030, 2035, 2040, 2045, 2050, 2055, 2060) under the scenario ensembles, re-gridded from the corresponding CMAQ simulations. (3) population, future population grid under the SSP1 scenario, re-gridded from SSP Datasets (<a href="http://clima-dods.ictp.it/Users/fcolon_g/ISI-MIP/">http://clima</a><a href="http://clima-dods.ictp.it/Users/fcolon_g/ISI-MIP/">-</a><a href="http://clima-dods.ictp.it/Users/fcolon_g/ISI-MIP/">dods.ictp.it/Users/fcolon_g/ISI</a><a href="http://clima-dods.ictp.it/Users/fcolon_g/ISI-MIP/">-</a><a href="http://clima-dods.ictp.it/Users/fcolon_g/ISI-MIP/">MIP/</a>). (4) death, PM2.5-related premature deaths (2025, 2030, 2035, 2040, 2045, 2050, 2055, 2060) under the scenario ensembles. (5) code for premature death calculation, with the method of GBD2019. (6) code for re-grid PM2.5 concentrations from CMAQ output.</p>
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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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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.