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3,018 results for “aire”
IDMT-ISA-Compressed-Air Dataset
<p>The IDMT-ISA-Compressed-Air (IICA) dataset aims to foster research in compressed air leak detection with acoustic emissions in the audible hearing range with recordings of air leaks in a simulated industrial compressed air network. The dataset contains recordings of multiple leak types with different types of industrial background noises played via external loudspeakers at two different volumes during the recording process. </p> <p>Leak Types:</p> <ul> <li>Vent Leak</li> <li>Vent Leak Low Pressure</li> <li>Tube Leak</li> </ul> <p>Noise Types:</p> <ul> <li>Lab Noise (no added background noise)</li> <li>Hydraulic machine noise</li> <li>Hydraulic machine noise, low volume</li> <li>General factory workshop noise</li> <li>General factory workshop noise, low volume</li> </ul> <p>For each combination of leak and noise types, there were three recording sessions. During each session, four Earthworks M30 omnidirectional measurement microphones placed in different configurations recorded the acoustic emission of the compressed air network. Each recording session contains 128 files of 30 seconds each, corresponding to each combination of leak, noise and microphone.</p> <ul> <li>Total Files: 5592</li> <li>Sampling Rate: 48 kHz</li> <li>Resolution: 32-bit</li> <li>Mono Audio</li> </ul> <p>See the above referenced paper and README contained with the data folder for further details.</p>
Dataset related to publication "Non-contact thermometer for improved air temperature measurements"
<p>These datasets were used to produce Fig. 6, 8 and 9 in paper "Non-contact thermometer for improved air temperature measurements" to be published in <em>Sensors </em><strong>2023</strong><em>, 23. </em>The data were produced within the EMPIR project 18SIB01 GeoMetre.</p>
What Is Polluting Delhi's Air? A Review from 1990 to 2022
<p>This supplementary information for open use is part of the publication, "<a href="https://doi.org/10.3390/su15054209">What is Polluting Delhi's Air? A Review from 1990 to 2022</a>" This paper offers insight by reviewing the influence of Delhi’s urban growth since 1990 on pollution levels and sources and the evolution of technical, institutional, and legal measures to control emissions in the National Capital Region of Delhi..</p> <p><strong>The databases and the documents uploaded here are the following</strong></p> <p>Available ambient air quality monitoring data for Delhi</p> <ul> <li>cpcb_delhi_data_2006-2018-raw-cleaned.rar - This is CPCB data from 2006 to 2018 as raw and cleaned files. Raw data is at 15 min internals, which needs some qa/qc checks before use. The cleaned data is hourly. For data cleaning, all null points, all negative points, integer values equal to 9999, 999, 1985, 985, 915, 515, 1200, 1000, 2000, 380, 3800, 675, and 718 were excluded. These values were recognized after searching the raw data for patterns. Instances of sudden jumps, which occur due to malfunctioning of instruments were recognized using running means. </li> <li>1999-2006-CPCB ITO-Hourly.xlsx - This is hourly data from the ITO station only</li> <li>NAMP data for 2011 to 2015 is hosted here - https://doi.org/10.5281/zenodo.6925200</li> <li>Graph-Composite-1989-2022.xlsx -- This is a composite of all the annual average data along with the worksheet to make the image in the preview.</li> </ul> <p>Support documents</p> <ul> <li>1997-CPCB-White-Paper-on-Delhi-Air-Pollution.pdf</li> <li>2011-CPCB-Source-Apportionment-Report-Extracts.pdf</li> <li>2015-04 Infograph Delhi Banning Vehicles to Control AP.jpg</li> <li>2016-03 Inforgraph Delhi Odd Even Emissions.jpg</li> <li>2019-09-Infograph-Delhi-Odd-Even-Buses.jpg</li> <li>NCAP-Planned-Source-Apportionment-studies.pdf</li> <li>SIM-41-2021-Data-Resources-for-Energy-Emissions-Analysis.pdf</li> </ul> <p>Reanalysis fields from WUSTL</p> <ul> <li>wustl_delhi_1998-2021.csv - This data is at 0.01 degree resolution for the city airshed. The long-lat represent the grid mid-point. </li> <li>wustl_delhi_1998-2021.png - This is a composite image of data extracted for all the years</li> </ul> <p>Satellite data extracts</p> <ul> <li>satellite-modis_terra_aod_delhi-covidperiod.xlsx</li> <li>satellite-modis_terra_aod_delhi-longerperiod.xlsx</li> <li>satellite-omi_no2_delhi-covidperiod.xlsx</li> <li>satellite-tropomi_o3_delhi.xlsx</li> <li>satellite-tropomi_so2_delhi.xlsx</li> </ul>
Health Risks Forecast of Regional Air Pollution on Allergic Rhinitis: High-Resolution City-Scale Simulations in Changchun, China
<p>Here presented the forcasted results of Potential Morbidity Risk Index (PMRI) for the personal patients with allerigc rhinitis and the public health administrations, and these results are supplied to the published paper of "Health Risks Forecast of Regional Air Pollution on Allergic Rhinitis: High-Resolution City-Scale Simulations in Changchun, China".</p>
Data for Measurement report: Air pollution emission factors of inland river ships under compliance with the 10 parts per million limit for sulfur content in fuel
<p>Since July 1, 2019, China’s domestic diesel fuel has been limited to 10 ppm of sulfur. Hence, to explore the applicability of the “sniffer” method and the distribution and level of inland river ships (IRSs) emission factors (EFs) under this limitation, we installed “sniffer” monitoring equipment, from August 2020 to June 2022, at the Gezhou Dam of the Yangtze River in China and monitored emissions from 8,238 IRSs in total passing through the lock. We partnered with the maritime department to select 100 ships passing through the lock to extract fuel oilsamples for direct fuel sulfur content detection, which determined the true fuel sulfur content of the passing ships. fuel sulfur content.</p> <p>The “sniffer” monitoring equipment included SO<sub>2</sub>, CO<sub>2</sub>, NO, and NO<sub>2</sub> gas sensors, PM<sub>2.5</sub> and PM<sub>10</sub> particulate matter sensors, as well as wind speed, wind direction, temperature, humidity, and pressure sensors.</p>
Nuclear Power Generation Phaseouts Redistribute U.S. Air Quality and Climate Related Mortality Risk, Data
<p>This dataset accompanies the publication, "Nuclear Power Generation Phaseouts Redistribute U.S. Air Quality and Climate Related Mortality Risk", and can be used with the code located at https://zenodo.org/badge/latestdoi/248010532 to reproduce our results.</p>
SensEURCity: A multi-city air quality dataset collected using networks of open low-cost sensor systems
<p>We provide a unique curated dataset of urban air quality measurements acquired using dense networks of low-cost sensor systems in three European cities for the years 2020 and 2021. The dataset includes the raw sensor data of quality-controlled sensor networks along with co-located reference data sets. Sensor data are collected using the AirSensEUR sensor system, including sensors to monitor NO, NO2, O3, CO, PM2.5, PM10, PM1, CO2, and meteorological parameters. In total, 85 sensor systems were deployed throughout the years 2020 and 2021 in three European cities (Antwerp , Oslo and Zagreb), resulting in a dataset comprising different meteorological and ambient conditions. The main data collection included two co-location campaigns in different seasons at an air quality monitoring station in each city and a deployment at different locations in each city (including also locations at other air quality monitoring stations). The dataset consists of data files with sensor and reference data, and metadata files with description of locations, deployment dates and description of sensors and reference instruments. </p>
CRDS absorption spectra recorded for the P(16) and P(28) transitions of the 20012-00001 band, and the R(24) and R(30) transitions of the 20013-00001 band of 12CO2 in air
<p>The database is made of the set of the CRDS absorption spectra recorded for the P(16) and P(28) transitions of the 20012-00001 band, and the R(24) and R(30) transitions of the 20013-00001 band of 12CO2 in air, following the procedures and with the experimental setups described in the paper entitled "Line shape parameters of air-broadened 12CO2 transitions in the 2.0 µm region, with their temperature dependence" by D. Mondelain, A. Campargue, H. Fleurbaey, S. Kassi, S. Vasilchenko published in JQSRT 298 (2023) 108485. <br> Each averaged spectrum corresponds to a .csv file including the following columns: <br> absolute wavenumber (in cm-1), cavity loss rate 1/ct (in 10-6 cm-1), temperature (in °C) and total pressure (in Torr).</p> <p>Two gas mixtures of CO2 in air (Ar : ~1 Mol-% ; O2 :~20.95 Mol-% ; N2 : ~78.05 Mol-%) are used in this work. <br> The stated sample CO2 mixing ratios are 6.013±0.060 Mol-ppm for the 20012-00001 band and 40.10±0.24 Mol-ppm for the 20013-00001 band.</p>
CRDS absorption spectra recorded for the R(06), R(12), R(16), R(18) and R(20) transitions of the 30013-00001 band of 12CO2 in air
<p>The database is made of the set of the CRDS absorption spectra recorded for the R(06), R(12), R(16), R(18) and R(20) transitions of the 30013-00001 band of 12CO2 in air following the procedures and with the experimental setups described in the paper entitled "CRDS measurements of air-broadened lines in the 1.6 µm band of 12CO2: Line shape parameters with their temperature dependence" by D. Mondelain, A. Campargue, H. Fleurbaey, S. Kassi, S. Vasilchenko published in JQSRT 288 (2022) 108267. <br> Each averaged spectrum corresponds to a .csv file including the following columns: <br> absolute wavenumber (in cm-1), cavity loss rate 1/ct (in 10-6 cm-1), temperature (in °C) and total pressure (in Torr).</p> <p>A gas mixture of 398.9±2.0 (2sigma) mol-ppm of CO2 in air (Ar: ~1 mol-% ; O2: ~20.95 mol-%; N2: ~ 78.05 mol-%) is used in this work.</p>
Data and analysis scripts for: Lung adenocarcinoma promotion by air pollutants
<p>Code for "Lung adenocarcinoma promotion by air pollutants" manuscript</p> <p>egfrm_lc_incidence</p> <ul> <li>Epidemiological analyses of EGFRm lung cancer incidence and PM2.5 levels in England (NHS England), South Korea and Taiwan.</li> </ul> <p>ukbb</p> <ul> <li>Epidemiological analyses of lung cancer incidence and PM2.5 levels in England, using the UKBB data set.</li> </ul> <p>mouse_RNA_seq</p> <ul> <li>Analysis of RNA-seq data derived from lung tumour tissue of pollution-exposed mice.</li> </ul> <p>mouse_WGS</p> <ul> <li>Analysis of WGS data derived from lung tumour tissue of pollution-exposed mice.</li> </ul> <p>normal_lung</p> <ul> <li>Analysis of ddPCR for EGFRm from normal lung tissue from the TRACERx and PEACE cohorts.</li> <li>Analysis of Duplex-seq data from normal lung tissue from the PEACE and BDRE cohorts.</li> </ul>
Hourly Air Temperature Datasets from city of Novi Sad - NSUNET system
<p>This dataset captures hourly air temperature data from 12 urban sites in Novi Sad (Serbia) over a period of 2 years covering 2016 and 2017 (measurement time is in UTC). There are 2 datasets in the collection: one dataset provides details about the 12 sites at which the temperature sensors are placed (Table 1, excel file), while the second file contains air temperature data at the 12 locations (.csv file). In all, the second dataset contains 17,544 instances of air temperature data. The temperature data has been cleaned and gap-filled so there are 24 measures at each site for each day. There are multiple potential uses for this data. It can provide insights when trying to understand intra-urban and inter-urban research, urban climate modeling on local or micro scales, heat-related public health investigations and urban environment inquiries. It can also be used in machine learning experiments, for example, to test the accuracy of classification algorithms or to build and validate spatio-temporal machine learning functions, either for classification purposes or for gap filling.</p>
Data and source code for "Spherical Air Mass Factors in One and Two Dimensions with SASKTRAN 1.6.0"
<p>Supplementary data for "Spherical Air Mass Factors in One and Two Dimensions with SASKTRAN 1.6.0":</p> <ul> <li>source: source code and data required to reproduce all figures</li> <li>amf-tables: data in NETCDF4 format for the air mass factor tables discussed in Section 5, which were used in the following publications by Griffin et al.: <ul> <li>"High-Resolution Mapping of Nitrogen Dioxide With TROPOMI: First Results and Validation Over the Canadian Oil Sands" (2018, <a href="https://doi.org/10.1029/2018GL081095">https://doi.org/10.1029/2018GL081095</a>)</li> <li>"Biomass burning nitrogen dioxide emissions derived from space with TROPOMI: methodology and validation" (2021, <a href="https://doi.org/10.5194/amt-2021-223">https://doi.org/10.5194/amt-2021-223</a>)</li> </ul> </li> </ul>
Air-Sea fluxes of CO2 in the Indian Ocean between 1985 and 2018: A synthesis based on Observation-based surface CO2, hindcast and atmospheric inversion models.
<p>This data set contains 14 hindcast models (CCSM-WHOI.nc, CESC_ETHZ.nc, CNRM-ESM2-1.nc, EC_Earth3.nc, FESOM_REcoM_LR.nc, MOM6_Princeton.nc, MPIOM_HAMOCC.nc; MRI-ESM2-1.nc, NorESM-OC1.2.nc, ORCA1-LIM3-PISCES.nc, ORCA025-EOMAR.nc, Plankotom12, INCOIS-BIO-ROMS.nc, ROMS-NYUAD.nc), nine empirical models (CMEMS-LSCE-FFNN.nc, CSIRML6.nc, Jena-MLS.nc, JMAMLR.nc, Spco2_LDEO_HPD.nc, SOMFNN.nc, NIES-MLR3.nc, UOEX-WAT20.nc, OceanSODAETHZ.nc) and CO2 flux climatology data (CO2_Climatology.nc). This data set also has two atmospheric inversion models output and those are - MACTM (MACTM.nc) and CAMSv20r1 (CMSv20r1.zip format and inside the zip folder files are .nc format).</p>
Data for Numerical Study on the Impact of Large Air Purifiers, Physical Distancing, and Mask Wearing in Classrooms
<p>This is a reference case setup to re-generate all the data for the paper titled "Numerical Study on the Impact of Large Air Purifiers, Physical Distancing, and Mask Wearing in Classrooms"<br> </p>
Cartas de Suelos República Argentina - Provincia de Buenos Aires
<p>El Plan Mapa de Suelos de la Región Pampeana en el año 1964 marca la etapa de mayor trascendencia del inventario de los suelos de nuestro país a escala de semidetalle y de reconocimiento (1:50.000 - 1:100.000). Las Cartas de Suelos de la Provincia de Buenos Aires son el resultado de este plan y la continuación de las tareas de relevamiento y cartografía de suelos hasta la actualidad. El Instituto Nacional de Tecnología Agropecuaria (INTA) pone a disposición de la comunidad, para su libre descarga la información vectorial de suelos a escala 1:50.000 de la Provincia de Buenos Aires elaborado a partir de la digitalización del material original de las Cartas de Suelos de la República Argentina. La información de las series de suelos se encuentra basada en lo publicado en http://anterior.inta.gob.ar/suelos/cartas/.</p> <p>También se presenta para la descarga la Carta de suelos del partido de Villarino a escala 1: 250.000. En el siguiente link se puede acceder al informe de dicha carta y la descarga de mapas temáticos: https://inta.gob.ar/documentos/carta-de-suelos-de-la-republica-argentina-partido-de-villarino-provincia-de-buenos-aires.</p> <p>En los siguientes scripts (https://github.com/INTA-Suelos/SuelosBA) se puede trabajar con las unidades cartográficas y series.</p>
Regional distribution of annual sea-to-air OCS fluxes for the present day atmosphere with 500 ppt OCS and the two OCS geoengineering scenarios with 4.8 ppb and 35.5 ppb.
<p>This dataset was prepared for a publication by von Hobe et al. (2023):</p> <p><strong>Comment on “An approach to sulfate geoengineering with surface emissions of carbonyl sulfide” by Quaglia et al. (2022)</strong></p> <p>In that publication, the data are displayed in Figure 3.</p> <p>Average annual sea-to-air OCS fluxes on a 2.8 ° latitude x 2.8 ° longitude grid for the present day atmosphere and for the two OCS emission scenarios considered by Quaglia et al. (2022) were obtained from a 2003 - 2019 simulation, using a model described in Lennartz et al. (2021).</p> <p>- - - - - - - - - - -</p> <p>File format:</p> <p> netCDF</p> <p>Index Variables:</p> <p> latitude</p> <p> longitude</p> <p>Parameters:</p> <p> ocsem500: mean annual sea-to-air OCS flux calculated for an atmospheric OCS mole fraction of 500 ppt</p> <p> ocsem4800: mean annual sea-to-air OCS flux calculated for an atmospheric OCS mole fraction of 4.8 ppb</p> <p> ocsem35500: mean annual sea-to-air OCS flux calculated for an atmospheric OCS mole fraction of 35.5 ppb</p> <p>- - - - - - - - - - -</p> <p><strong>References:</strong></p> <p>Lennartz, S. T., Gauss, M., von Hobe, M., and Marandino, C. A.: Monthly resolved modelled oceanic emissions of carbonyl<br> sulphide and carbon disulphide for the period 2000–2019, Earth Syst. Sci. Data, 13, 2095-2110, 10.5194/essd-13-2095-2021, 2021.</p> <p>Quaglia, I., Visioni, D., Pitari, G., and Kravitz, B.: An approach to sulfate geoengineering with surface emissions of carbonyl<br> sulfide, Atmos. Chem. Phys., 22, 5757-5773, 10.5194/acp-22-5757-2022, 2022.</p> <p>von Hobe, M., Brühl, C., Lennartz, S. T., Whelan, M. E., and Kaushik, A.: Comment on “An approach to sulfate geoengineering with surface emissions of carbonyl sulfide” by Quaglia et al. (2022) , EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-268, 2023.</p>
Uncovering local aggregated air quality index with smartphone captured images leveraging efficient deep convolutional neural network
<p>Short Description:</p> <p>In this research, we vigorously analyze the difficulties of predicting location-specific PM2.5 concentration from photos captured by smartphone cameras. Here, we particularly focus on Dhaka, the capital of Bangladesh, considering its very high level of air pollution exposure to a huge number of its dwellers. In our research, we develop a Deep Convolutional Neural Network (DCNN) and train it using more than a thousand outdoor photos captured and labeled by us. We capture the photos at various locations in Dhaka, Bangladesh, and label them based on PM2.5 concentration data extracted from the local US consulate as computed by the NowCast algorithm. During training with the dataset, our model learns a correlation index through supervised learning, which improves the model's ability to act as a Picture-based Predictor of PM2.5 Concentration (PPPC) making it capable of detecting comparable daily aggregated AQI index from a photo captured by a smartphone.</p> <p>Code and More Details: https://github.com/lepotatoguy/aqi</p>
Supporting data and tool, for the paper "A standardized methodology for the validation of air quality forecast applications (F-MQO): Lessons learnt from its application across Europe"
<p>This 'Zenodo' contains supporting data and tools, for the paper 'A standardized methodology for the validation of air quality forecast applications (F-MQO): Lessons learnt from its application across Europe'.</p> <p>The repository includes the source code (<a href="https://zenodo.org/api/files/fdcaf2a5-5289-44f5-96db-6dda29c5cb6c/DELTA_7.2.zip">DELTA_7.2.zip</a>) and the dataset (<a href="https://zenodo.org/api/files/fdcaf2a5-5289-44f5-96db-6dda29c5cb6c/GMD_Vitali_et_all_data_20230516.zip">GMD_Vitali_et_all_data_20230516.zip</a>).</p>
US Army Aviation air movement request problem instances
<p>Although lacking the same preeminent status of air assault planning, air movement operations comprise a majority of Army utility and cargo helicopter combat aviation operations in terms of volume of customers and the endless appetite for rapid movement of troops across the battlespace. The data provided enabled research and development of a US Army Aviation air movement mission planning model to assist the mission planner by rapidly providing courses of action based on the commander's priorities. Features of the problem and the data provided include priority demand, multi-node refueling, aircraft and passenger time windows, maximum passenger transportation time, and the minimization of unsupported demand, aircraft utilization, and total flight time. The mathematical model provided is an extension of the dial-a-ride problem (DARP) that will coordinate air mission requests (AMRs) at the aviation task force-level or lower to generate courses of action that optimize helicopter fleet resourcing and routing decisions against mission variables, while supporting the optimal number of AMRs that sustain combat power over time. </p>
Outdoor air temperature dataset - IN-HALE Project
<p>Dataset for network IN01 in project IN-HALE PROJECT</p> <p>IN-HALE project aims to identify the thermal summer conditions inside the residences of residents over 65 years of age and to identify the individual determinants of exposure to heat, as well as the barriers (material and/or immaterial) for thermal adaptation. To this end, we will monitor the indoors thermal environment of 20 dwellings, through visits of objective assessment of the thermal behaviour of the dwellings and using the installation of thermal dataloggers during the summer period.</p> <p> </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
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.