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323 results for “air pollution”
Pediatric Asthma Healthcare Utilization, Viral Testing, and Air Pollution Changes during the COVID-19 Pandemic
<p>Data related to asthma care encounters during the COVID19 pandemic.</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>
MuAP Spatial distribution of various air pollutants in China at 1 km(NO2 2021-01-01:2023-12-31) (Version1.1)
<p>MuAP Spatial distribution of various air pollutants in China at 1 km(NO2)</p> <p>Multiple air pollutions dataset (MuAP) </p> <p>Time frame: 2021-2023<br>Area: Most of China<br>Resolution: about 1km<br>File storage format: .xz and GeoTIFF<br>Spatial projection: WGS84<br>Daily file name: year_doy.tif (Daily MuAP data volume exceeds Zenodo platform limits. Please contact the author at fjcyfeng@qq.com.)</p> <p>Monthly file name: year_month.tif</p> <p>Yearly file name: year_month.tif</p> <p>Unit: Please divide by 10 when using. (ug/m3)</p> <p>When you download and use our data, please cite:</p> <ol> <li>Chi, Y., Zhan, Y., Wang, K., and Ye, H.: Sequential spatiotemporal distribution of PM<sub>2.5</sub>, SO<sub>2</sub> and Ozone in China from 2015 to 2020, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-76, in review, 2023.</li> <li>Chi, Y., Zhan, Y., Wang, K., & Ye, H. (2023). Spatial Distribution of Multiple Atmospheric Pollutants in China from 2015 to 2020. Remote Sensing, 15(24). doi:10.3390/rs15245705</li> </ol> <p>Note: The MuAP for 2015-2020 can be obtained by:</p> <p>1.</p> <ul> <li>PM2.5:https://zenodo.org/records/8093749</li> <li>O3:https://zenodo.org/records/8180923</li> <li>SO2:https://zenodo.org/records/8093749</li> <li>NO2:Please contact the author at fjcyfeng@qq.com.</li> </ul> <p> </p> <p>2. Daily MuAP data volume exceeds Zenodo platform limits. Please contact the author at fjcyfeng@qq.com.</p> <p> </p>
Urban Air Pollution and Child Neurodevelopmental Conditions: A Systematic Bibliometric Review
<p>The dataset for this research was compiled through an advanced PubMed search targeting publications from a one-year period, with keywords focused on air pollution, neurodevelopment, and associated disorders. From an initial pool of 450 publications, filtering based on the co-occurrence of relevant keywords reduced this to approximately 44 papers. The analysis was conducted using VOSviewer to generate a visual map of relationships between air pollution and child neurodevelopment. To ensure consistency, a thesaurus was applied to standardize terminology, refining the final network for a detailed examination of keyword clusters and their interactions. For those seeking to replicate this process, Appendix A provides an in-depth, step-by-step methodology for building the networks and utilizing the data files to recreate the visualizations.</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>
Data and code for "Meeting U.S. Greenhouse Gas Emissions Goals with the International Air Pollution Provision of the Clean Air Act"
<p>For the files and data associated with the Yuan et al. 2022 "Meeting U.S. Greenhouse Gas Emissions Goals with the International Air Pollution Provision of the Clean Air Act"</p> <p>Description: Data/code used in energy-economic impacts and health impacts analysis.</p> <p>Directory contents:</p> <p><strong>Energy Economic Impacts</strong></p> <ul> <li><strong>Code </strong>used for producing figures and data tables <ul> <li>'paperFigs_March2022.Rmd' contains the R code used for data analysis and visualization in the paper. (<em>The code runs with R v4.0.0, RStudio v1.4.1106, and the following packages: scales_1.1.1, ggpubr_0.4.0, cowplot_1.1.0, readxl_1.3.1, here_0.1, forcats_0.5.0, stringr_1.4.0, dplyr_1.0.4, purrr_0.3.4, readr_1.3.1, tidyr_1.1.0, tibble_3.0.6, ggplot2_3.3.4, and tidyverse_1.3.0.</em>)</li> <li>'ERL_Figure4.py' contains the Python code used for generating Figure 4 in the paper</li> </ul> </li> <li><strong>Table</strong>: data tables for figures in the paper and supplementary materials</li> <li><strong>Figure</strong>: figures in the paper and supplementary materials</li> <li><strong>Data</strong>: USREP-ReEDS results and data from other sources <ul> <li>'rrpt_subset.csv' contains the portions of the ReEDS output from February 26, 2021 that are necessary to create the figures in the paper.</li> <li>'urpt_subset.csv' contains the portions of the USREP output from February 26, 2021 that are necessary to create the figures in the paper.</li> <li>'urpt_welfare_subset.csv' contains more detailed USREP welfare output from February 26, 2021.</li> <li>'cooper_pop_proj.csv' contains U.S. population projections from the University of Virginia Weldon Cooper Center for Public Service published in 2018.</li> <li>'carbon_price_comparison.csv' contains data from other recent carbon pricing studies, as described in supplementary materials G.</li> </ul> </li> </ul> <p><strong>Health Impacts</strong></p> <ul> <li><strong>analysis</strong>: <ul> <li><strong>lib</strong>: annotated code library, which loads raw data from the root data folder and conducts health impacts analysis</li> <li><strong>data</strong>: outputs <ul> <li><strong>inmap</strong>: spatial inputs/outputs for inmap</li> <li><strong>working</strong>: intermediate procssed output files</li> <li><strong>final</strong>: final health impacts results</li> </ul> </li> </ul> </li> <li><strong>data</strong>: raw data used in analysis <ul> <li><strong>working</strong>: processed intermediate raw data for faster loading in R</li> </ul> </li> </ul>
Ground obeservation data (meteorological factors and air pollution) in Greater Bay Area, 2015 - 2021
<p>Ground observation data used in the paper <em>Development of an LSTM-Broadcasting deep-learning framework for regional air pollution forecast improvement</em>.</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 Multi-Pollutant Emissions Inventory for Air Pollution Modeling and Supporting Information for Kampala
<p>This paper is under review</p> <p>Abstract:</p> <p>Kampala, the political and economic capital of Uganda and one of the fastest urbanising cities in sub-Saharan Africa, is experiencing a deteriorating trend in air quality with emissions from multiple diffused local sources like transportation, domestic and outdoor cooking, and industries, and sources outside the city airshed like seasonal open fires in the region. PM2.5 (particulate matter under 2.5um size) is the key pollutant of concern in the city with monthly spatial heterogeneity of 60-100 ug/m3. Outdoor air pollution is distinctly pronounced in the global south cities and lack the necessary capacity and resources to develop integrated air quality management programmes including ambient monitoring, emissions and pollution analysis, source apportionment, and preparation of clean air action plans. This paper presents an integrated assessment of air quality in Kampala drawing from ground measurements (from a hybrid network of stations), satellite observations (from NASA’s MODIS and OMI), global reanalysis fields (from GEOS-chem and CAMS simulations), high resolution (~1km) multi-pollutant emissions inventory for the airshed, WRF-CAMx based PM2.5 pollution analysis, and a qualitative review of institutional and policy environment for air quality management in Kampala. The proposed clean air action plans aim for better air quality in the region using a combination of short-, medium-, and long-term emission control measures for all the dominate sources and institutionalize pollution tracking mechanisms (like emissions and pollution monitoring and reporting) for effective management of air pollution.</p> <p>This data archive serves as a supplemenary to the journal article and with a short description of the files below:</p> <ul> <li>File: AQ-Kampala-Analysis-Summary.pptx (Caution: large 60MB) <br>A composite presentation including the following<br> <ul> <li>Grid summaries</li> <li>Snapshots of airshed GIS files, emission activities</li> <li>Summary of meteorology from WRF simulations and historical synoptics</li> <li>Summaries of ambient monitoring data</li> <li>CAMS reanalysis summary</li> <li>Summaries of Emission inventory and WRF-CAMx modelling (annual and monthly)</li> <li>Summaries of PM2.5 Source apportionment (annual and monthly)</li> </ul> </li> <li>File: grids_kampala.rar<br>Grid file (KML and ESRI shapefiles format) for the airshed spanning 0.0N to 0.6N and 32.3E to 32.9E with a spatial resolution of 0.01deg (~1km)</li> <li>File: gis_roads_from_opensteetmaps.rar<br>ESRI shapefiles of primary roads and all roads, extracted from the openstreetmaps<br>Raw data archive @ https://download.geofabrik.de/index.html</li> <li>File: gis-scanned2021image-quarries.kml<br>KML file of quarries scanned using the imagery on Google Earth platform</li> <li>File: population_kampala_2000-2022.csv<br>Gridded population data 2000 to 2022<br>Raw data archive is from LANDSCAN - https://landscan.ornl.gov</li> <li>File: Monitoring-Kampala_USEmbassy_2017-2024.xlsx<br>Summary of monitoring data collected at the US Embassy in Kampala<br>Raw data archive is @ https://www.airnow.gov/international/us-embassies-and-consulates/#Uganda$Kampala</li> <li>File: meteo_wrf_stats.xlsm<br>Summary of output of WRF simulations for the Kampala region. Have to activiate macros to summarize the results by month and update the charts. The tool can be used to other cities also by changing the input data.</li> <li>File: meteo_precip-era5-reanalysis.csv<br>Summary of monthly precipitation date (mm/day) from ERA5 reanalysis fields<br>Raw data archive is @ https://psl.noaa.gov/data/atmoswrit/timeseries</li> <li>File: TROPOMI_EastAfrica_NO2_Maps.zip<br>Images of monthly average TROPOMI NO2 extracts covering East Africa (Uganda and Ethiopia)<br>Extracted from Google Earth Engine, using 10% cloud fraction</li> <li>File: TROPOMI_EastAfrica_CSVs.zip<br>CSV files of gridded monthly average NO2, SO2, HCHO, and Ozone columnar densities<br>Extracted from Google Earth Engine, using 10% cloud fraction<br>Read the data descriptions and applicability of the data for analysis before using (for example, negative numbers in the SO2 file). <br>NO2 - https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_NO2 <br>SO2 - https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_SO2<br>HCHO - https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_HCHO<br>O3 - https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_O3</li> <li>File: composite_emisson_factors_gains.xlsx<br>A composite library of emission factors for reference</li> <li>File: kampala_gridded_emissions_2018.rar<br>Gridded emissions inventory for Kampala - PM25, PM10, SO2, NO, NO2, and CO<br>PM25 is speciated into FPRM, BC, and OC (sum all for PM25)<br>PM10 is speciated into FPRM, CPRM, BC, OC (sum all for PM10)<br>All emissions in tons/year/grid<br>Emissions are seggragted into sectors and fuels - included in the filenames</li> <li>File: kampala_gridded_modelled_monthavgp25.csv<br>Gridded PM2.5 concentrations for 2018, from WRF-CAMx modelling system<br>Monthly averages in ug/m3</li> </ul>
A Multi-Pollutant Emissions Inventory for Air Pollution Modeling and Supporting Information for Addis Ababa
<p>This paper is under review. For additional information or queries, send email to sguttikunda@urbanemissions.info</p> <p>****</p> <p>ABSTRACT: Ground measurements and satellite observations over Addis Ababa airshed show a deteriorating trend of air pollution, especially for PM2.5 (all particulate matter under 2.5um). In this paper, we present a review of available monitoring data; a model-ready multi-pollutant (PM10, PM2.5, SO2, NOx, CO, non-methane VOCs, and CO2) emissions inventory at 0.01º resolution for the designated airshed; a heatmap of PM2.5 concentrations and an estimate of source contributions constructed using WRF-CAMx chemical transport modelling system; and a discussion on proposed actions towards establishing an air quality management plan for the city. Emissions from road transport; residential and commercial cooking; resuspended dust on roads and from construction activities; residential and industrial heating; lighting; open waste burning; and other industrial activities contributed the most to ambient PM2.5 pollution. Particularly, vehicle exhaust is estimated to contribute up to 29% of total PM2.5, followed by biomass combustion in the residential and industrial sectors.</p> <p>File included in this dataset:</p> <ol> <li>Composite presentation of supporting information and analysis results<br>File: Report-Addis-Data-Summary.pptx<br>This presentation includes summary images of<br> <ul> <li>monitoring data</li> <li>the GIS fields</li> <li>google earth scans </li> <li>annual and monthly emissions</li> <li>annual and monthly PM2.5 concentrations</li> <li>annual and monthly source apportionment</li> </ul> </li> <li>Ambient monitoring data<br>File: AddisAbaba_AllEmbassy_Data.xlsx (summary of data till May2024)<br>File: monitoring_addisair_cleaned.xlsx (summary of sensor data)</li> <li>Gridded emissions inventory<br>File: addis_gridded_emissions.rar<br>Format: ix,iy,midlong,midlat,FPRM,CPRM,BC,OC,NO,NO2,CO,SO2<br>Units: tons/grid/year<br>PM2.5 emissions = FPRM + BC + OC<br>PM10 emissions = FRPM + BC + OC + CPRM<br>midlong and midlat are midpoints of grids - see gis_addis_grids.rar</li> <li>TROPOMI UVAI and MODIS AOD<br>Data files extracted from google earth engine as TIF and CSV, for the designated airshed<br>File: ADDISABABA_yearly_tifs.zip</li> <li>Meteorlogical data summary for the airshed, extracted from the WRF sumulations<br>File: meteo_addis.houravg_summary.csv</li> <li>Reference reports<br>File: Report-C40-2016-Addis-Ababa-GHG-Emssions.pdf<br>File: Report-CSE-Ethiopia-Urban-AQM-Guidance.pdf<br>File: Report-WB-Ethiopia-motorization-management.pdf<br>File: Report-UNEP-Addis-AQM-Plan-Draft.pdf</li> <li>GIS files<br>File: gis_addis_grids.rar (shapefile and KML file for the airshed grid)<br>File: gis_addis_osm_roads.rar (shapefiles extracted from openstreetmaps)<br>File: pop_extracts_4selection_domain.xlsm (gridded population data, with macros can be used to extract population totals around a monitoring station)<br>File: gis_addis_multiple_layers.rar (as KML files - districts, townships, main_roads, water_bodies, quarries, landfill, industrial_areas)</li> </ol>
Fig. 3 in The Relation Of Forest And Air Pollution With Human Health In Urban Territories Of Lithuania
Fig. 3. The correlation between the forest coverage and air pollution indicators.
Fig. 2 in The Relation Of Forest And Air Pollution With Human Health In Urban Territories Of Lithuania
Fig. 2. The correlation between the forest and human health indicators.
A Multi-Pollutant Emissions Inventory for Air Pollution Modeling and Supporting Information for Bishkek
<p>Full paper is published here<br><a href="https://doi.org/10.3390/air2040021">https://doi.org/10.3390/air2040021</a><br>Mapping PM2.5 Sources and Emission Management Options for Bishkek, Kyrgyzstan<br><br></p> <p>****</p> <p>Harsh winters, aging infrastructure and control technologies, and the increasing demand for urbanization and modernization of amenities are major factors contributing to the deteriorating air quality in Bishkek, the capital city of Kyrgyzstan and a burgeoning economic hub in Central Asia. The heating energy needs are met via combustion of coal at the central heating plant, heat only boilers, and in-situ heating equipment and the mobility needs via combustion of diesel and petrol. Other mapped sources contributing to daily air pollution levels in Bishkek’s airshed include 30 km2 of industrial area, 16 large open combustion brick kilns, a vehicle fleet with average age more than 10 years, 7.5 km2 of quarries, and one landfill. Annual PM2.5 emission load for the airshed is approximately 5,500 tons, resulting in an annual average concentration of 48 ug/m3, which is 9-10 times higher than the World Health Organization (WHO) guideline of 5 ug/m3. Wintertime daily averages range from 200-300 ug/m3. Proposed emissions management policies for the city include shift to clean fuels like gas and electricity at the heating plants and households, restricting the secondhand vehicle imports and incentivizing newer standard vehicles, promotion of public transport system with newer buses, at least doubling of the waste collection efficiency and landfill management capacity and encouraging greening and maintaining road infrastructure to control dust emissions. PM2.5 levels from mid- to long-term implementation of these options is expected to drop by 50-70%. A long-term plan for Bishkek must include an expansion of the ambient monitoring network using a combination of reference-grade and low-cost sensors to track progress of air quality management efforts and to support information dissemination for public awareness.</p> <p>Files included here:</p> <ol> <li>Composite presentation of supporting information and analysis results<br>File: Bishkek_AQ_Analysis_Composite.pptx<br>This presentation includes summary images of<br> <ul> <li>monitoring data</li> <li>the GIS fields</li> <li>google earth scans </li> <li>annual and monthly emissions</li> <li>annual and monthly PM2.5 concentrations</li> <li>annual and monthly source apportionment</li> </ul> </li> <li>Meteorological data summary for the airshed, extracted from the WRF sumulations<br>File: Bishkek_Met_Summary.pptx<br>File: bishkek_meteorology_stats.xlsm (activate macros for stats and making images for final use)</li> <li>GIS files<br>File: gis_bishkek-grids-pop.rar (shapefile and KML file for the airshed grid, csv file for gridded population 20 years, and images)<br>File: gis_bishkek_roads.rar (shapefiles extracted from openstreetmaps)<br>File: gis_bishkek_adm0.rar (shapefiles of administrative boundaries)<br>File: gis_bishkek_multiplelayers.rar (as KML files - districts, townships, main_roads, water_bodies, quarries, landfill, industrial_areas)</li> <li>CAMx output (units is ug/m3)<br>File: bishkek_camx_pm25_monthlyavg.csv (see the grid file for mapping)</li> <li>Gridded model-ready emissions inventory<br>File: bishkek_gridded_emissions_2018.rar (see the grid file for mapping)<br>Format: ix,iy,midlong,midlat,FPRM,CPRM,BC,OC,NO,NO2,CO,SO2<br>Units: tons/grid/year<br>PM2.5 emissions = FPRM + BC + OC<br>PM10 emissions = FRPM + BC + OC + CPRM<br>midlong and midlat are midpoints of grids - see gis_bishkek-grids-pop.rar</li> <li>Ambient monitoring data<br>File: bishkek_monitoring_claritysensor_2021.xlsx<br>File: bishkek_monitoring_us.embassy2019-2024.rar</li> </ol>
High resolution annual average air pollution concentration maps for the Netherlands
<p>Raster-based air pollution concentration maps for the Netherlands. The dataset consists of air pollution concentration maps for six pollutants (NO2, NO2background, NOx, PM2.5, PM2.5absorbance, PM10), covering the land mass of the Netherlands at 5m spatial resolution. The maps were calculated using the Land Use Regression models from the European Study of Cohorts for Air Pollution Effects (ESCAPE) project. Several Python scripts used for data preparation and the model scripts creating the datasets are included. Use the free 7-Zip to uncompress. Uncompressed size: 78 GiB.</p> <p>A description of concepts, datasets and scripts is given in the manuscript "High resolution annual average air pollution concentration maps for the Netherlands" by Oliver Schmitz, Rob Beelen, Maciej Strak, Gerard Hoek, Ivan Soenario, Bert Brunekreef, Ilonca Vaartjes, Martin J. Dijst, Diederick E. Grobbee, and Derek Karssenberg. <em>Scientific Data</em> 6:190035 (2019). <a href="https://doi.org/10.1038/sdata.2019.35">https://doi.org/10.1038/sdata.2019.35</a></p> <p>The datasets are licensed under a Creative Commons license (CC-BY 4.0). The Python scripts are licensed under the MIT License.</p> <p>Contact: o.schmitz@uu.nl</p>
Evaluation data for the Intervention Model for Air Pollution (InMAP) version 1.6.1
<p>This directory contains data for performing 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>- annual_all_2005.csv: U.S. EPA annual average measured air pollution data for year 2005, downloaded from EPA AirData at http://aqsdr1.epa.gov/aqsweb/aqstmp/airdata/download_files.html. </p> <p>- census2013blckgrp.shp and associated files: US Census Bureau estimates of average population counts for several demographic groups for years 2011-2015. Descriptive and license information regarding these files can be found in census2013blckgrp_README.txt</p> <p>- InMAPData_v1.6.1.gob: InMAP variable grid resolution input data for the continental U.S. year 2005 for use as the "VariableGridData" variable in the InMAP configuration file. It was created with the 'inmap grid' command.</p> <p>- InMAPData_v1.2.0.ncf: Regular-grid InMAP input data for the continental U.S. year 2005 for use as the "InMAPData" variable in the InMAP configuration file. It was created from WRF-Chem simulation outputs with the 'inmap preproc' command. This file has not changed since version 1.2. Information regarding the WRF-Chem simulations is available in: Tessum, C. W., Hill, J. D., & Marshall, J. D. (2015). Twelve-month, 12 km resolution North American WRF-Chem v3.4 air quality simulation: performance evaluation. Geosci. Model Dev., 8(4), 957–973. http://doi.org/10.5194/gmd-8-957-2015</p> <p>- mortalityRates2013.shp and associated files: Year 2013 all-population, all-cause mortality rate data from the CDC WONDER database (http://wonder.cdc.gov). Descriptive information regarding these files can be found in mortalityRates2013_README.txt</p> <p>- states.shp and related files: U.S. state boundaries for making evaluation maps.</p> <p>- la_test directory: Files with the area surrounding the city of Los Angeles extracted for faster-running tests.</p> <p>- 2005_emissions directory: Anthropogenic emissions from the 2005 U.S. EPA National Emissions Inventory (NEI), processed using the AEP model (https://github.com/ctessum/aep) (files Elevated.shp and GroundLevel.shp), and biogenic and wilfire emissions (file bioFireEmis.shp) calculated following the methods in Tessum, C. W., Hill, J. D., & Marshall, J. D. (2015). Twelve-month, 12 km resolution North American WRF-Chem v3.4 air quality simulation: performance evaluation. Geosci. Model Dev., 8(4), 957–973. http://doi.org/10.5194/gmd-8-957-2015</p> <p>- singleSource directory: Regular-grid InMAP input data for los Angeles for year 2005 for use as the "InMAPData" variable in the InMAP configuration file. It was created from WRF-Chem simulation outputs with the 'inmap preproc' command, based on a 9 - 3 - 1 km nested WRF-Chem simulation which only included emissions from a single source in downtown Los Angeles. The directory includes four different InMAPData_*.ncf files, three corresponding to the three WRF-Chem domains and one for a nested InMAP simulation. The directory also contains an inmapEmis.shp shapefile and supporting files that contain emissions matching the emissions used in the WRF-Chem simulation. More information about this simulation can be found in the InMAP model description article.</p> <p>- FuelScenarios directory: This directory contains two subdirectories. "Emissions" contains spatial emissions information corresponding to the emissions scenarios described at doi: 10.1073/pnas.1406853111. "Concentrations" contains changes in pollutant concentrations resulting from WRF-Chem simulations of the emissions scenarios, also described and discussed at doi: 10.1073/pnas.1406853111.</p>
Current Opinion in Psychiatry - Air pollution and Neurodevelopment Paper Method and Dataset
<p>The dataset for this research was compiled through an advanced PubMed search targeting publications from a one-year period. Keywords focused on air pollution, neurodevelopment, and associated disorders. From an initial pool of 5,000 publications, filtering based on co-occurrence of relevant keywords reduced this to approximately 80 papers. VOSviewer was employed to analyze co-occurrences and generate a visual map of relationships between air pollution and child neurodevelopment. The thesaurus was applied to standardize terminology, refining the final network for detailed analysis of keyword clusters and their interactions.</p>
MULTIPLIERS_WP3/4_Science learning project on Air Pollution_UAB_Public data
<p><span>This dataset contains the following data related to</span><span> the science learning project on <em>Air pollution</em></span><span>:</span></p> <ul> <li><span>Summary of transcripts from interviews with teachers, OSC members, and students (Pseudo-/Anonymised)</span></li> </ul> <p> </p>
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>
Eindhoven - Carbon sequestration and air pollutants capture
<p>Results of the i-Tree simulations of carbon sequestration, total amount of carbon stored in vegetation and air pollutants capture in three NBS locations in Eindhoven.</p>
Data for "biochar-composting substantially reduces methane and air pollutant emissions from dairy manure"
<p>Dataset showing:</p> <p>1) Weekly compost NH4 concentrations</p> <p>2) Weekly compost NO3 concentrations</p> <p>3) Weekly compost C, N, O, H</p> <p>4) Weekly compost pH and EC</p> <p>5) Daily compost temperature</p> <p>6) Weekly compost moisture</p> <p>7) Weekly compost porosity</p> <p>8) Final compost CEC</p> <p>9) Final compost germination indexes</p> <p>10) AP3 integrated assessment model results</p> <p>11) Daily compost gas fluxes</p>
ScienceDex guides
Understand access before you commit
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.