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206 results for “air quality”
Emission primarily drives nationwide air quality changes during and after the COVID-19 lockdown in China
<p>Research data includes air pollutant observation data, wildfire data and meteorological data.</p>
Contrasting Activation Characteristics of Biomass Burning and Fossil Fuel Combustion Aerosols in Fogs and Clouds: Implications for Regional Air Quality and Climate
<p>The key 'jul' in data use 2021-01-01 as the referece day, for example, 2021-01-02 12:00:00 corresponding to jul of 2.5. </p>
Characterizing ambient air quality and oil and gas air pollution emissions in Broomfield County, CO
<p>Unconventional oil and natural gas development (UOGD) has expanded rapidly across the United States in recent decades and raised concerns about associated air quality impacts. While significant effort has been made to quantify methane emissions, relatively few observations have been made of Volatile Organic Compounds (VOCs), especially during drilling and completion of new wells. Extensive air monitoring during development of several large, multi-well pads in Broomfield, Colorado, in the Denver-Julesburg Basin, provides a novel opportunity to examine changes in local air toxics and other VOC concentrations during well drilling and completions and production.</p>
Resources for "BMF CP 91: Socio-demographic factors, illness experience, severity perception, and sensitivity to air quality index"
<p><span>The current study is conducted to examine the following research questions:</span></p> <ul> <li><span>What are the factors associated with the sensitivity towards the air quality rating index to reduce outdoor activities?</span></li> </ul>
Impact on energy and air quality of connected and autonomous vehicles in an urban context
<p>Despite numerous studies related to autonomous vehicles and connected vehicles (CAVs) and their impact on the economy or on traffic performance (eg, flow management, accidents), there are not many studies that relate these benefits to the environmental component. In this context, the objective of this work consisted in the integrated assessment of the impacts of CAVs on traffic performance, atmospheric emissions CO<sub>2</sub> and NO<sub>x, </sub>and air quality.</p> <p>To this end, a roundabout in the city of Aveiro was selected as a case study, and different scenarios were created: base scenario, considering the current typology of vehicles (conventional); scenario 2, considering defensive behavior CAVs; scenario 3, considering assertive behavior CAVs; and scenario 1, considering all types of vehicles mentioned above. To ensure a comprehensive analysis, all scenarios were evaluated for a period of 24 hours, corresponding to the period of the experimental campaign carried out, and a cascade of models was applied.</p> <p>First, the PTV VISSIM model was applied which allowed, configuring, calibrating and validating the network under study for an evaluation of the traffic performance. Second, the VSP model was applied to estimate atmospheric emissions, Finally, the CFD VADIS model was applied to air quality assessment.</p> <p>The results obtained allowed us to conclude that the introduction of CAVs, promotes longer travel times, especially during times of higher traffic, and an increase in emissions, mainly by the CAVs with defensive behavior. In terms of air quality, there were large differences in terms of NO<sub>2</sub> concentrations, with the CAVs promoting a degradation of air quality, especially during peak traffic hours.</p>
Air-quality data simulated over Great Paris area
<p>High local simulated concentrations of NO2, PM10, O3 and PM2.5 over Paris at surface for selected dates for 2018.</p>
Evaluation data for "Global, high-resolution, reduced-complexity air quality modeling for PM2.5 using InMAP (Intervention Model for Air Pollution)"
<p>This zip file contains data for performing Global InMAP model runs and evaluations. To the extent that any of the data is covered by third party licenses, it is the responsibility of the user to follow the terms of those licenses. A description of the contents of this directory is below:</p> <p>measurements.csv<br> Vetted global dataset of ground-level annual-average measurements of total PM2.5 and species (pNO3, pSO4, pNH4) compiled from monitoring networks, used for model performance evaluation. Data sources are: World Health Organization (Global), European Environment Agency (Europe), National Air Pollution Surveillance Program (Canada), Environmental Protection Agency (United States of America), Central Pollution Control Board (India), Australian Government State of the Environment (Australia), and Acid Deposition Monitoring Network In East Asia (EANET) (East Asia).</p> <p>population directory<br> Population count data is from the Gridded Population of The World (v4.10) projected to year 2020. The data is in 15x15 arcminute grids, except for in grid cells where the population is above 80,000, where the population data is 30x30 arcseconds.</p> <p>GlobalInMAPData_v1.ncf<br> Regular-grid Global InMAP input data for the year 2005 for use as the "InMAPData" variable in the InMAP configuration file. It was created from GEOS-Chem v.11-01 simulation outputs with the 'inmap preproc' command.</p> <p>global_inmap_004x003_v1.1.0.gob<br> Global InMAP variable grid resolution input data for coords for year 2016 for use as the "VariableGridData" variable in the InMAP configuration file. It was created with the 'inmap grid' command using GlobalInMAPData_v1.ncf and population.shp.</p> <p>2016_emissions directory<br> Total PM2.5 and precursor emissions to arrive at total PM2.5 concentrations from Global InMAP. Units for polygonized emissions inputs (shapefiles) are short (US) tons/yr, and units for gridded emissions inputs (NetCDF files) are kg/yr.</p> <p>global_emission_changes directory<br> nh3.nc, nox.nc, and sox.nc are gridded emissions for changes in inorganic precursors for comparing Global InMAP and GEOS-Chem. Units are kg/yr. NH4-gc.nc, NIT-gc.nc, and SO4-gc.nc are results for changes in concentrations arising from these changes in emissions for 3 months, 1 month, and 2 months.</p> <p>usa_emission_changes directory<br> Emissions for comparing Global InMAP and US InMAP (described in Tessum et al., 2017).<br> Emissions are derived using the United States National Emissions Inventory (NEI) 2014v.1, processed exactly as in Thakrar et al., 2020.<br> Emissions are coal-powered electricity generation (NEI Source Classification Code: 10100212) and gasoline passenger vehicles (NEI Source Classification Code: 2201210080).<br> Units are ug/s.</p> <p>Tessum, C.W.; Hill, J.D.; Marshall, J.D. InMAP: A model for air pollution interventions. PloS One 2017, 12 (4) e0176131.<br> Thakrar, S.K.; Balasubramanian, S.; Adams, P.J.; Azevedo, I.M.; Muller, N.Z.; Pandis, S.N.; Polasky, S.; Pope III, C.A.; Robinson, A.L.; Apte, J.S.; Tessum, C.W.; Marshall, J.D.; Hill; J.D. Reducing mortality from air pollution in the United States by targeting specific emission sources. Environmental Science & Technology Letters 2020, 7(9), pp.639-645.<br> Gridded Population of the World, Version 4 (GPWv4): National Identifier Grid. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H41V5BX1.</p>
Impacts of wind power on air quality, premature mortality, and exposure disparities in the United States
<p>This repo includes supporting material for the publication:</p> <p>Qiu, M., Zigler, C. M., & Selin, N. E. (2022). Impacts of wind power on air quality, premature mortality, and exposure disparities in the United States. <em>Science Advances</em>, <em>8</em>(48), eabn8762.</p> <p>Please download and unzip the file "<a href="https://zenodo.org/api/files/bef77248-ca65-4ae6-9a92-ad00fc068665/mhqiu/wind_pollution_EJ-v1.0.zip?versionId=a9b5b8e9-273c-4431-a101-4f9a4c331675">mhqiu/wind_pollution_EJ-v1.0.zip</a>". Please see README for a full description of the sample data included in this repo. </p> <p><strong>README</strong></p> <p><strong>1. Regression results: </strong><br> <em>EGU_regression_scenario_results.xlsx </em><br> It includes regression results for each EGU in our sample (1264 EGUs in total). </p> <p><strong>2. Air quality simulation results:</strong><br> <strong>2.1 GEOS-Chem simulation</strong><br> <em>GC_daily_pm25_o3_scenarios.nc </em><br> It contains surface level annual mean PM2.5 and MDA8 O3 concentration under different emission scenarios. We include four scenarios in total:<br> - baseline scenario: air quality **without** the amount of wind power associated with 2014 RPS targets <br> - expost scenario: air quality with the wind power associated with 2014 RPS targets under the current dispatch decisions<br> - health damage minimizing scenario: air quality with the wind power associated with 2014 RPS targets under a hypothetical dispatch scenario that minimizes the health damage<br> - CO2 minimizing scenario: air quality with the wind power associated with 2014 RPS targets under a hypothetical dispatch scenario that minimizes the CO2 emissions</p> <p>Therefore, to calculate the air quality impacts of wind power under different dispatch decisions: <br> current (ex post) = ex post - baseline. <br> health damage minimizing = health damage minimizing - baseline. <br> CO2 minimizing scenario = CO2 minimizing - baseline. </p> <p><strong>2.2 InMAP simulations</strong><br> <em>InMAP/xx.shp </em><br> The shapefiles contain annual mean PM2.5 concentration simulated with InMAP under different emission scenarios. <br> baseline.shp: baseline scenario <br> ex post.shp: ex post scenario <br> health_damage_minimizing.shp: health damage minimizing scenario <br> co2_minimizing.shp: CO2 minimizing scenario</p> <p>Descriptions of the four scenarios are the same as above for GEOS-Chem.</p> <p><strong>3. County-level air quality change for different demographic groups (GEOS-Chem)</strong><br> <em>county_pm_o3_changes_by_groups_GEOS_CHEM.xlsx </em><br> This file contains changes in county-level simulated PM2.5 and O3 concentrations due to wind power under different scenarios. It also includes the total population at the county level and the population for each subgroup. This data can be used to calculate the distributional effects of air quality benefits across different population groups.</p> <p> </p> <p> </p>
Fig. 4 in Landuse Patterns, Air Quality And Bird Diversity In Urban Landscapes Of Delhi
Fig. 4. Variation in abundance of foraging guilds (%) of birds among different sampling sites.
Fig. 1 in Landuse Patterns, Air Quality And Bird Diversity In Urban Landscapes Of Delhi
Fig. 1. Map of Delhi showing location of sampling sites.
Madrid datasets (air quality, meteorological and traffic data)
<p>The datasets consist of air quality, meteorological, and traffic data from January to June 2019 and from January to June 2020. The following are the features of the datasets: Nitrogen dioxide, Wind speed, Wind direction (u component, v component, north, northeast, east, southeast, south, southwest, west, northwest), Pressure, Temperature, Humidity, Solar irradiance, Intensity, Occupancy time, Load, Average traffic speed.</p>
Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions
<p>This repo includes the GEOS-Chem simulations and R scripts that are needed to replicate and evaluate the conclusions from Qiu, Zigler, and Selin, ACP, 2022 "Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions".</p> <p><strong>The GEOS-Chem simulations</strong></p> <ul> <li>For the US (2011-2017): <ul> <li><em>observational_o3_pm_2011_2017_us.rds:</em> the simulated daily PM2.5 and O3 concentrations, and MERRA-2 meteorological features in the observational scenarios (<strong>changing</strong> meteorology, <strong>changing </strong>emissions).</li> <li><em>counterfactual_o3_pm_2011_2017_us.rds</em><em>:</em> the simulated daily PM2.5 and O3 concentrations, and MERRA-2 meteorological features in the counterfactual scenarios (<strong>constant</strong> meteorology, <strong>changing</strong> emissions).</li> <li><em>constant_emis_o3_pm_2012_2017_us.rds: </em>the simulated daily PM2.5 and O3 concentrations in the constant-emission scenarios (<strong>constant</strong> meteorology, <strong>constant</strong> emissions).</li> <li><em>regional_features_2011_2017_4x5_us.rds: </em>the MERRA-2 meteorological features in the observational scenarios (aggregated to 4x5 degrees), inputs for the "RF-regional" model.</li> </ul> </li> <li>For China (2013-2017): <ul> <li><em>observational_o3_pm_2013_2017_china.rds:</em> the simulated daily PM2.5 and O3 concentrations, and MERRA-2 meteorological features in the observational scenarios (<strong>changing</strong> meteorology, <strong>changing </strong>emissions).</li> <li><em>counterfactual_o3_pm_2013_2017_china.rds:</em> the simulated daily PM2.5 and O3 concentrations, and MERRA-2 meteorological features in the counterfactual scenarios (<strong>constant</strong> meteorology, <strong>changing</strong> emissions).</li> <li><em>constant_emis_o3_pm_2014_2017_china.rds</em><em>: </em>the simulated daily PM2.5 and O3 concentrations in the constant-emission scenarios (<strong>constant</strong> meteorology, <strong>constant</strong> emissions).</li> <li><em>regional_features_2013_2017_4x5_china.rds: </em>the MERRA-2 meteorological features in the observational scenarios (aggregated to 4x5 degrees), inputs for the "RF-regional" model.</li> </ul> </li> </ul> <p><strong>R scripts:</strong></p> <ul> <li><a href="https://zenodo.org/api/files/065be469-ef8d-4c9b-9bd8-a6a808275237/main.r">main.r</a>: the main script to perform statistical correction of meteorological variability.</li> <li>main.r uses functions from the other R script files (see below) which perform different statistical correction methods, respectively. </li> <li><a href="https://zenodo.org/api/files/065be469-ef8d-4c9b-9bd8-a6a808275237/parametric_regression_methods.r">parametric_regression_methods.r</a>: performs meteorological correction with parametric regression methods (MLR, polynomial, spline, GAM)</li> <li><a href="https://zenodo.org/api/files/065be469-ef8d-4c9b-9bd8-a6a808275237/tune_RF_regional.r">tune_RF_regional.r</a> and <a href="https://zenodo.org/api/files/065be469-ef8d-4c9b-9bd8-a6a808275237/RF_regional.r">RF_regional.r</a>: perform the meteorological correction with the "RF-regional" model</li> <li><a href="https://zenodo.org/api/files/065be469-ef8d-4c9b-9bd8-a6a808275237/GEOS_Chem_constant_emis.r">GEOS_Chem_constant_emis.r</a>: performs the meteorological correction using the simulations from the constant emission scenarios from the GEOS-Chem model</li> </ul> <p> </p>
PM10, SO2, and NO2 Ambient Air Quality Monitoring Data from India's National Ambient Monitoring Program (NAMP) 2011-2015
<p>India's Central Pollution Control Board (CPCB) operates and maintains the National Ambient Monitoring Program (<a href="https://cpcb.nic.in/about-namp/">NAMP</a>) which includes both continuous and manual ambient monitoring stations. This dataset is a collation of manual monitoring data by day for years 2011, 2012, 2013, 2014, and 2015 for PM10, SO2, and NO2. These stations collect for a maximum of 104 days in a year. This cleaned dataset was utilized for understanding trends and conducting comparisons with modeled concentrations under the APnA city program, published <a href="https://doi.org/10.1016/j.uclim.2018.11.005">here</a> (<a href="https://doi.org/10.1016/j.uclim.2018.11.005">Urban Climate, 2019</a>).<br> <br> Data format - year, month, day, SO2, NO2, PM10, Stn Code, State, City<br> All units - micro-gm/m3 (ug/m3)</p> <p>Official annual summary reports (PDFs) are available <a href="https://cpcb.nic.in/namp-data/">here</a>.</p> <p>For guidelines for ambient and emissions monitoring, summaries of available data, and other resources on monitoring in India, visit <a href="https://urbanemissions.info/resources-energy-emissions-analysis-in-india/#monitoring">https://urbanemissions.info/resources-energy-emissions-analysis-in-india</a></p>
A synchronized estimation of hourly ground-level concentrations of six criteria air pollutants in China using data from the first geostationary air-quality monitoring satellite
<p>This dataset provides the ground-level concentrations of six criteria air pollutants estimated from the first geostationary air quality monitoring satellite GEMS with a multi-output random forest model.</p>
A Comprehensive Analysis of Air Quality in the NYC Subway System
<p>Average PM2.5 concentration in the subway stations and inside the train of nine subway lines in NYC. </p>
The dataset of the manuscript "GPU-HADVPPM4HIP V1.0: higher model accuracy on China's domestically GPU-like accelerator using heterogeneous compute interface for portability (HIP) technology to accelerate the piecewise parabolic method (PPM) in an air quality model (CAMx V6.10)"
<p><strong>bcfile.zip:</strong> the clean boundary condition files.</p> <p><strong>CAMxv6x_cpp.zip: </strong>the source code of CAMx-HIP version which coupled with HIP-HADVPPM scheme.</p> <p><strong>data.zip:</strong> final data tables used to plot figures.</p> <p><strong>emisfile.zip: </strong>the emission files.</p> <p><strong>icfile.zip:</strong> the clean initial condition files.</p> <p><strong>tuvfile.zip </strong>and <strong>o3mapfile.zip:</strong> the photolysis files.</p> <p><strong>outputfile.zip:</strong> the computation results outputted by CAMx model for Fortran version on the Intel Xeon E5-2682 v4 CPU, CUDA version on the NVIDIA K40m and V100 clusters, and HIP version on the China' s domestically heterogeneous cluster A.</p> <p><strong>wrfcamx.zip:</strong> the meteorological files.</p> <p><strong>offline_test_cuda.zip: </strong>the advection module code written in CUDA C language</p> <p><strong>offline_test_fortran.zip:</strong> the advection module code written in Fortran language</p> <p><strong>offline_test_hip.zip: </strong>the advection module code written in HIP C language</p>
Smogseer: A Convolutional LSTM for forecasting air quality from Sentinel-5P data
<h1><strong>Training and checkpoint datasets </strong></h1> <h2><a href="#h_1501865622611722161313186" target="_blank" rel="noopener">S5PL2_5D.nc</a></h2> <p>This is the Sentinel-5P traning dataset for the Smogseer ConvLSTM model. The dataset was created using the xcube Sentinel Hub data store from the <a href="https://deepesdl.readthedocs.io/en/latest/datasets/datastores/#xcube-sentinel-hub-data-store">Deep Earth System Data Lab</a>.</p> <p>The dataset has the following characteristics:</p> <ul> <li>bbox= [68.137207,24.886436,84.836426,34.379713] #WGS84 // lon,lat,lon,lat</li> <li>res = (bbox[2]-bbox[0])/512 # ~3629m</li> <li>date_range = ['2019-01-01', '2023-12-31']</li> <li> <div>timesteps = '5D'</div> </li> </ul> <h2><a href="#h_4062378263291722161323566">X_val.npy</a></h2> <p>Validation feature data with shape: (74, 1, 291, 512, 6)</p> <ul> <li>74: dates</li> <li>1: time steps</li> <li>291: latitudes</li> <li>512: longitudes</li> <li>6: Features ['SO2', 'NO2', 'CH4', 'O3', 'CO', 'HCHO']</li> </ul> <h2><a href="#h_9861938573981722161330905">Y_val.npy</a></h2> <p>Validation target data with shape: (74, 1, 291, 512, 1)</p> <ul> <li>74: dates</li> <li>1: time steps</li> <li>291: latitudes</li> <li>512: longitudes</li> <li>6: Features ['SO2', 'NO2', 'CH4', 'O3', 'CO', 'HCHO']</li> </ul> <h2><a href="#h_321677095651722161353847">smogseer50.keras</a></h2> <p>Model weights for training the ConvLSTM with 50 epochs.</p> <h2><a href="#h_5931467847131722161401921">smogseer100.keras</a></h2> <p>Model weights for training the ConvLSTM with 10 epochs.</p>
Updated Smoke Exposure Estimate for Indonesian Peatland Fires using a Network of Low-cost PM2.5 sensors and a regional air quality model - Model Simulation Data
<p>WRF-Chem simulated daily mean PM2.5 concentrations for:</p> <p>1) with fires </p> <p>2) without fires</p> <p>simulations. </p>
Updated Smoke Exposure Estimate for Indonesian Peatland Fires using a Network of Low-cost PM2.5 sensors and a regional air quality model - Purple Air data
<p>Daily mean PM2.5 concentrations collected by Purple Air sensors between 2023-08-16 and 2023-12-01. Concentrations have been RH adjusted using the Nilson et al (2022) adjustment. </p>
Resources for "BMF CP 92: Who had no wildlife smoke notifications and knowledge of the air quality rating system?"
<p>The current study is conducted to examine the following research questions:</p> <ul> <li>Who were the people who had no access to wildfire smoke notifications during the smoke event in the summer of 2018 in the Boise Metropolitan Area in Idaho?</li> <li>Who were the people who were not familiar with the air quality rating system?</li> </ul>
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.