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206 results for “air quality”
China Air Quality Observation
<p>20171007-20171010 China Air Quality Observation uesd in the sutdy“Impact of aerosols on convective system over the North China Plain: a numerical case study in autumn”</p>
Madrid datasets (air quality, meteorological and traffic data) of 2019 and 2022
<p>The datasets consist of air quality, meteorological, and traffic data from January to June 2019 and from January to June 2022. The following are the features of the datasets: Nitrogen dioxide, Wind speed, Wind direction (in degrees), Temperature, Humidity, Pressure, Solar irradiance, Intensity, Occupancy time, Load, Average traffic speed, Wind direction (after converting to categorical data (north, northeast, east, southeast, south, southwest, west, northwest) and passing through One Hot Encoder). The following are the names of the above features (the names of the columns in the files): 'NO2', 'windSpeed', 'windDir', 'Temp', 'Humidity', 'Pressure', 'SolarRad', 'intensidad', 'ocupacion', 'carga', 'vmed', 'windDir_Categ_east', 'windDir_Categ_north', 'windDir_Categ_northeast', 'windDir_Categ_northwest', 'windDir_Categ_south', 'windDir_Categ_southeast', 'windDir_Categ_southwest', 'windDir_Categ_west'.</p> <p><a href="https://zenodo.org/api/files/5af044df-ea61-41ee-a43f-4d095f43ca55/Mad_2019_winddir.csv">Mad_2019_winddir.csv</a> and <a href="https://zenodo.org/api/files/5af044df-ea61-41ee-a43f-4d095f43ca55/Mad_2019_winddir.csv">Mad_2022_winddir.csv</a> include the above features for the defined grid in the city of Madrid for January-June 2019 and January-June 2022, respectively, with the following dimension: 4344×340×19: (January-June 2019); 4343 × 340 × 19 (January-June 2022).</p> <p><a href="https://zenodo.org/api/files/5af044df-ea61-41ee-a43f-4d095f43ca55/Mad_Station_2019.csv">Mad_Station_2019.csv</a> and <a href="https://zenodo.org/api/files/5af044df-ea61-41ee-a43f-4d095f43ca55/Mad_Station_2019.csv">Mad_Station_2022.csv</a> include the above features with the following dimension: 4344 × 24 × 19: (January-June 2019); 4343 × 24 × 19 (January-June 2022), extracted from the defined grid for cells only where air quality monitoring stations exist.</p>
Air Quality - Genova City - Various measurement points
<p> </p> <p>Concentration of various pollutants in several measurement points located in the area of the City of Genova. Source: <a href="http://www.cartografiarl.regione.liguria.it/SiraQualAria/script/Pub2AccessoDatiAria.asp">http://www.cartografiarl.regione.liguria.it/SiraQualAria/script/Pub2AccessoDatiAria.asp</a></p>
Air Quality - Genova Demo Area - Gavoglio Park
<p>Conentration of PM10, PM2.5, NO<sub>2</sub>, and O3, temperature, relative humidity and pressure, measured at two different sites (inside and outside) of the DEMO area of UNaLab project in Genova</p>
Air quality data from the article "Typhoon-associated air quality over the Guangdong–Hong Kong–Macao Greater Bay Area, China: machine-learning-based prediction and assessment"
<p>This dataset consists of 26 files. The descriptions of the files are as follows:</p> <ul> <li>aqi_TY.csv, pm25_TY.csv, pm10_TY.csv, so2_TY.csv, no2_TY.csv and o3_TY.csv are the observed values of AQI and concentrations of PM<sub>2.5</sub>, PM<sub>10</sub>, SO<sub>2</sub>, NO<sub>2</sub> and O<sub>3</sub> of 36 monitoring stations used in model establish stage on TY days. The time range is June 2014 to December 2020.</li> <li>aqi_NTY.csv, pm25_NTY.csv, pm10_NTY.csv, so2_NTY.csv, no2_NTY.csv and o3_NTY.csv are the observed values of AQI and concentrations of PM<sub>2.5</sub>, PM<sub>10</sub>, SO<sub>2</sub>, NO<sub>2</sub> and O<sub>3</sub> of 36 monitoring stations used in model establish stage on NTY days. The time range is June 2014 to December 2020.</li> <li>station_info.csv is the detailed information of the 36 monitoring stations used in model establish stage, including station number, city, longitude and latitude.</li> <li>aqi_TY_testing.csv, pm25_TY_testing.csv, pm10_TY_testing.csv, so2_TY_testing.csv, no2_TY_testing.csv and o3_TY_testing.csv are the observed values of AQI and concentrations of PM<sub>2.5</sub>, PM<sub>10</sub>, SO<sub>2</sub>, NO<sub>2</sub> and O<sub>3</sub> of 3 monitoring stations used for testing the model on TY days. The time range is June 2014 to December 2020.</li> <li>aqi_NTY_testing.csv, pm25_NTY_testing.csv, pm10_NTY_testing.csv, so2_NTY_testing.csv, no2_NTY_testing.csv and o3_NTY_testing.csv are the observed values of AQI and concentrations of PM<sub>2.5</sub>, PM<sub>10</sub>, SO<sub>2</sub>, NO<sub>2</sub> and O<sub>3</sub> of 3 monitoring stations used for testing the model on NTY days. The time range is June 2014 to December 2020.</li> <li>sta_testing.csv is the detailed information of the 3 monitoring stations used for testing the model, including station number, city, longitude and latitude.</li> </ul>
Google Street View vehicle-based mobile air quality observations in Salt Lake City (May 2019-March 2020)
<p>Google Street View (GSV) mobile air quality data collected in Salt Lake City as part of an Environmental Defense Fund-supported project that took place between May 2019 and March 2020. <br> The data collection, resolved at 1-second frequency, was carried out with two GSV vehicles.</p> <p>The data is in a single CSV (comma-separated value) text file. </p>
Data used to create figures and tables in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China"
<p>This dataset contains all simulation output and observational data of ground-based/satellite-retrieved meteorological and air quality for computing statistical metrics in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China", as follows:</p> <p>1. Simulation and observational results of meteorological and air quality including four folders:</p> <p> Day_PBLH: Daily PBLH data</p> <p> Hour_air: Hourly air quality data regarding PM2.5, O3, SO2, NO2 and CO</p> <p> Hour_met: Hourly meteorological data regarding T2, Q2, RH2, WS10 and precipitation</p> <p> Hour_radiation: Hourly surface radiation data</p> <p>2. Simulation and satellite-retrieved results of meteorological and air quality including nine folders:</p> <p> AOD: Yearly and seasonal AOD data</p> <p> CF: Yearly and seasonal CF data</p> <p> CO: Yearly and seasonal CO data</p> <p> LWP: Yearly and seasonal LWP data</p> <p> NO2: Yearly and seasonal NO2 data</p> <p> O3: Yearly and seasonal O3 data</p> <p> Precipitation: Yearly and seasonal precipitation data</p> <p> Radiation: Yearly and seasonal radiation data</p> <p> SO2: Yearly and seasonal SO2 data</p>
qechairquality: Air Quality Data at Queen Elizabeth Central Hospital (QECH), Blantyre, Malawi.
<p>Air quality data with measurements in 5-minute intervals for particulate matter (PM 2.5 & PM 10) at eight sensor locations over two months at Queen Elizabeth Central Hospital (QECH) in Blantyre, Malawi.</p>
Data used to simulations in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China"
<p>This dataset contains input data of simulations by WRF-CMAQ, WRF-Chem and WRF-CHIMERE in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China", as follows:</p> <p>1. WRF-CMAQ input data including emission, ICs and lateral BCs of meteorology and air quality:</p> <p>YYYYMM.zip represents the input data for each month for simulations. Due to the large size of the compressed file containing input data each month, there may be interruptions when uploading it to Zenodo. Therefore, we will split each compressed file into 50MB. If users want to browse the file, they can download the segmented files, and then merge them into the YYYYMM.zip file using the Linux command line "unzip 'YYYYMM.zip.*' -d combined"</p>
Incorporating real-time traffic data into air quality mapping uncovers more severe exposure disparities at the community level
<p>Data and codes for the XGBoost model training in our manuscript titled "Incorporating real-time traffic data into air quality mapping uncovers more severe exposure disparities at the community level"</p>
Air Quality-Related Equity Implications of U.S. Decarbonization Policy
<p>This repo includes supporting material for the publication:</p> <p>Paul Picciano, Minghao Qiu, Sebastian Eastham, Mei Yuan, John Reilly, Noelle E. Selin. Air Quality-Related Equity Implications of U.S. Decarbonization Policy. <em>Nature Communications (2023).</em></p> <p>Please download and unzip the file <strong>"climate_policy_pollution_equity.zip"</strong>. Please see README below for a full description of the scripts and data included in this repo. </p> <p>For correspondence on the publication, please contact Noelle Selin selin@mit.edu.</p> <p>If you have questions about this data repo, please contact Minghao Qiu mhqiu@stanford.edu or Paul Picciano pauldpicciano@gmail.com</p> <p> </p> <p><strong>README</strong></p> <p>The materials in this repository allow users to reproduce the main results and figures of the paper. The analysis is performed using R (version 4.3.0). </p> <p><strong>-- Scripts:</strong></p> <ul> <li><strong><em>initial_setup.R</em>: </strong> Used to load R packages, set up file paths, variable names, and the functions used in other scripts. Please load this script first before running other scripts.</li> <li><strong><em>Figure1 - 5.R</em>: </strong> Used to generate figures 1 - 5 in the main paper. </li> <li><strong><em>optimization_scenarios.R: </em></strong><em>Used to generate the optimization scenarios and 5000 possible emission reduction scenarios that achieve the same level of CO2 reductions. The results generated by this script are then used to generate Figure 5.</em></li> </ul> <p> </p> <p><strong>-- Data:</strong></p> <ul> <li><strong><em>emission_aggregate_scenarios.csv</em>: </strong> Nationally-aggregated emissions of CO2 and non-CO2 species by different sectors under the three main emission scenarios (scenarios number 1 - 3 as labeled in the paper). The unit of CO2 emissions is billion metric tons. Units of non-CO2 emissions are million metric tons. Used to plot Figure 1.</li> <li><strong><em>InMAP_pm25_conc_scenarios.rds</em>: </strong> PM2.5 concentration for each InMAP grid cell due to emissions from each sector (and combined sectors) under different emission scenarios. InMAP simulates annual mean PM2.5 concentration (unit: μg/m3). Used to plot Figure 2.</li> <li><em><strong>US_shapefiles.RData</strong></em>: Shape files of US states. Used to plot Figure 2.</li> <li><em><strong>exposure_main_scenarios.csv: </strong></em>Population-weighted PM2.5 for each population group xxx under different scenarios (i.e. AvgExposure_xxx, units of exposure: μg/m3); disparities in population-weighted PM2.5 between each population group xxx and the total population (i.e. AvgDisparity_xxx, units of disparity: %). Used to plot Figures 3 and 4. Rows 2-4 correspond to the main scenarios (scenario numbers 1 - 3). Rows 5 - 16 correspond to the sensitivity scenarios (scenario number 4 as labeled in the paper).</li> <li><strong><em>source_emission_baseline_2030.rds: </em></strong>Emissions from each individual source as projected by the baseline 2030 scenario (scenario number 2). Used as input data for the optimization analysis.</li> <li><strong><em>source_emission_cap50_2030.rds: </em></strong>Emissions from each individual source as projected by the cap50% scenario (scenario number 3). Used as input data for the optimization analysis.</li> <li><strong><em>minority_exposure_by_sector.rds: </em></strong>Changes in population-weighted PM2.5 exposure and disparities for the racial/ethnic minority group between the cap50% scenario and baseline 2030 scenario. Used to plot Figure 5.</li> <li><em><strong>optimization_scenarios.rds: </strong></em>Population-weighted PM2.5 exposure and disparities for the racial/ethnic minority group under different optimization scenarios (scenarios number 5 -10). This file is generated by <strong><em>optimization_scenarios.R</em></strong> and further used to plot Figure 5.</li> <li><em><strong>cap50_nation_total_5000draws.rds: </strong></em>Population-weighted PM2.5 exposure and disparities for the racial/ethnic minority group under 5000 possible scenarios that achieve the same level of CO2 reductions without any constraints (i.e. the same as the "nation-total" scenario). This file is generated by <strong><em>optimization_scenarios.R</em></strong> and further used to plot Figure 5.</li> </ul> <ul> </ul> <p> </p>
Improving the Behavioural Impact of Air Quality Alerts
ClinicalTrials.gov study NCT03552198. IPD Sharing: NO. Countries: 1. Publications: 4.
Data from: Mechanical ventilation and indoor air quality in recently constructed homes in cool and humid climates of the U.S.
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Characterizing ambient air quality and oil and gas air pollution emissions in Broomfield County, CO
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Data from: Mechanical ventilation and indoor air quality in recently constructed homes in the humid climate of the southeast U.S.
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Longitudinal assessment of thermal and perceived air quality acceptability in relation to temperature, humidity, and CO2 exposure in Singapore
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Indoor air quality in new and renovated low‐income apartments with mechanical ventilation and natural gas cooking in California
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Data from: Indoor air quality in California homes with code-required mechanical ventilation
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PyonAir: An open design, open source, air quality monitor for community driven particulate matter sensing.
<p>This dataset present some of the data recorded by two low-cost PM sensors, a Plantower PMS5003 and a Sensirion SPS030 located at Southampton AURN reference station on the 2nd December 2019 between 17:00 and 22:00 during a fire that occurred in the city. It also present the data from the Fidas 200, averaged every 15min also located at the AURN station.</p> <p>The file SPS_PMS_fire.csv contain the following rows:</p> <ul> <li>date</li> <li>pm25 - PM<sub>2.5</sub> mass concentration in ug/m<sup>3</sup></li> <li>sensor - Serial number of the sensor</li> <li>site - name of the location of the sensor</li> </ul> <p>The file fidas_15min.csv contains the following rows:</p> <ul> <li>date</li> <li>PM2.5 - PM<sub>2.5</sub> mass concentration in ug/m<sup>3</sup></li> </ul> <p> </p>
Air Quality Monitoring and People Counting
<p>This dataset contains data on air quality monitoring and people counter of an office room at the University of Messina. Data is stored and labeled with the corresponding people number.</p> <p>A single record is composed by;</p> <p><strong>Date</strong> s is a timestamp; </p> <p><strong>Id</strong> is an Identifier number; </p> <p><strong>Pm1</strong>, <strong>Pm 2.5</strong> and <strong>Pm10</strong> are the dust sensors;</p> <p><strong>Temp</strong> is the temperature;</p> <p><strong>Hum</strong> is humidity;</p> <p><strong>Press</strong> is the atmospheric pressure; </p> <p><strong>CO</strong> and <strong>CO2</strong> are the concentrations;</p> <p><strong>Label </strong>is the number of people. </p> <p> </p> <p> </p>
ScienceDex guides
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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.