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34 results for “Global PM2.5”
LGHAP v2: Global annual mean 1-km gap-free PM2.5 grids (2000-2021)
<p>A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.</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>
Long-term (2003-2020) hourly 0.25° global PM2.5 dataset (DeepCAMS) Part-1: 2003-2011
<p>This is part-I (2003-2011) of our DeepCAMS.</p> <p>Part-II can be found at https://doi.org/10.5281/zenodo.6969598</p> <p>Usage: The raw data -- (scaling factor: 0.1) --> the true PM2.5 concentration</p> <p>Paper title: Generating a Long-term (2003-2020) hourly 0.25° global PM2.5 dataset via spatiotemporal downscaling of CAMS with deep learning (DeepCAMS)</p> <p>Paper doi: <a href="https://doi.org/10.1016/j.scitotenv.2022.157747">https://doi.org/10.1016/j.scitotenv.2022.157747</a></p> <p>If you find our work helpful, please cite it, thank you very much!</p>
Evolution of India's PM2.5 Pollution Between 1998 and 2020 Using Global Reanalysis Fields
<p>These datasets are part of Supplementary information for the journal article<br> "<a href="https://doi.org/10.1039/D2EA00027J">Evolution of India’s PM2.5 Pollution Between 1998 and 2020 Using Global Reanalysis Fields Coupled with Satellite Observations and Fuel Consumption Patterns</a>"</p> <p>Fuel consumption patterns linked to the evolution of PM2.5 pollution data is available <a href="https://doi.org/10.5281/zenodo.7156314">here</a><br> <a href="https://doi.org/10.5281/zenodo.7156314">https://doi.org/10.5281/zenodo.7156314</a></p> <p><br> Data period - 1998 to 2020<br> PM2.5 units - micro-gm/m3<br> State and District GIS Shapefiles are available here - <a href="http://projects.datameet.org/maps">https://projects.datameet.org/maps</a></p> <p><strong>List of files available for download</strong></p> <p>india_wustl_extracts_pm25_bygrid_annual.xlsx</p> <ul> <li>PM2.5 concentrations data resolution is 0.1 degrees</li> <li>Covers India and the remaining countries in the domain covering 67E to 99E in longitudes and 7N to 39 N in latitudes (Pakistan, Bangladesh, Nepal and partially Sri Lanka and Afghanistan)</li> </ul> <p>india_wustl_extracts_pm25_bystate.xlsx</p> <ul> <li>PM2.5 concentrations data aggregated at the state level</li> <li>States are as designated under Census 2011 + Telangana</li> <li>Total states = 30</li> <li>Total Union Territories (UT) = 6</li> <li>JK as State includes new UT - Ladakh</li> </ul> <p>india_wustl_extracts_pm25_bydistrict.xlsx</p> <ul> <li>PM2.5 concentrations data aggregated at the district level</li> <li>Districts are as designated under Census 2011</li> <li>Total districts = 640</li> </ul> <p>india_wustl_extracts_pmsa.xlsx</p> <ul> <li>PM2.5 source apportionment concentrations aggregated at state and district level </li> <li>36 states and UTs</li> <li>640 districts</li> <li><a href="https://sites.wustl.edu/acag/datasets/gbd-maps/">GBDMAPS source classification</a> <ul> <li>1. AFCID = Anthropogenic Fugitive, Combustion, and Industrial Dust</li> <li>2. AGR = Agriculture - includes manure management, soil fertilizer emissions, rice cultivation, enteric fermentation, and other agriculture</li> <li>3. ENEcoal = Energy Production (coal combustion only) - Includes electricity and heat production, fuel production and transformation, oil and gas fugitive/flaring, and fossil fuel fires</li> <li>4. ENEother = Energy Production (all non-coal combustion) - Includes electricity and heat production, fuel production and transformation, oil and gas fugitive/flaring, and fossil fuel fires</li> <li>5. GFEDagburn = Agricultural Waste Burning - Includes solid waste disposal, waste incineration, waste-water handling, and other waste handling (from the GFED fires inventory)</li> <li>6. GFEDoburn = Other Open Fires - Includes deforestation, boreal forest, peat, savannah, and temperate forest fires (from the GFED fires inventory)</li> <li>7. INDcoal = Industry (coal combustion only) - Includes Industrial combustion (iron and steel, non-ferrous metals, chemicals, pulp and paper, food and tobacco, non-metallic minerals, construction, transportation equipment, machinery, mining and quarrying, wood products, textile and leather, and other industry combustion) and non-combustion industrial processes and product use (cement production, lime production, other minerals, chemical industry, metal production, food, beverage, wood, pulp, and paper, and other non-combustion industrial emissions)</li> <li>8. INDother = Industry (all non-coal combustion) - Includes Industrial combustion (iron and steel, non-ferrous metals, chemicals, pulp and paper, food and tobacco, non-metallic minerals, construction, transportation equipment, machinery, mining and quarrying, wood products, textile and leather, and other industry combustion) and non-combustion industrial processes and product use (cement production, lime production, other minerals, chemical industry, metal production, food, beverage, wood, pulp, and paper, and other non-combustion industrial emissions)</li> <li>9. NRTR = non-road/ off-road transportation - Includes Rail, Domestic navigation, Other transportation</li> <li>10. OTHER = all remaining sources, including: volcanic SO2, lightning NOx, biogenic soil NO, ocean emissions, biogenic emissions, very short lived iodine and bromine species, decaying plants (misc. inventories)</li> <li>11. RCOC = Commercial Combustion - Includes commercial and institutional combustion</li> <li>12. RCOO = Other Combustion - Includes combustion from agriculture, forestry, and fishing</li> <li>13. RCORbiofuel = Residential combustion (solid biofuel combustion only) - includes residential heating and cooking</li> <li>14. RCORcoal = Residential combustion (coal combustion only) - includes residential heating and cooking</li> <li>15. RCORother = Residential Combustion (all non-coal and non-solid biofuel) - includes residential heating and cooking</li> <li>16. ROAD = Road Transportation - includes cars, motorcycles, heavy and light duty trucks and buses</li> <li>17. SHP = International Shipping - Includes international shipping and tanker loading</li> <li>18. SLV = Solvents - Includes solvents production and application (degreasing and cleaning, paint application, chemical products manufacturing and processing, and other product use)</li> <li>19. WDUST = Windblown Dust - (from the DEAD dust model)</li> <li>20. WST = Waste - Includes solid waste disposal, waste incineration, waste-water handling, and other waste handling</li> </ul> </li> <li>Aggregated Source definitions used in this presentation <ul> <li>1. DUST = Anthropogenic dust = AFCID</li> <li>2. WINDUST = Wind erosion (dust storms) = WDUST</li> <li>3. WASTE = Waste burning = WST</li> <li>4. RESI = All commercial and residential cooking, lighting, and heating = RCOC + RCOO + RCORbiofuel + RCORcoal + RCORother</li> <li>5. TRANS = All transport (excluding aviation) = ROAD + NRTR + SHP</li> <li>6. POWER = Energy generation = ENEcoal + ENEother</li> <li>7. INDUS = All industries and product use = INDcoal + INDother + SLV</li> <li>8. BIOB = Biomass burning, including forest fires and agricultural waste burning = GFEDoburn + GFEDagburn</li> <li>9. AGR = Agricultural activities (excluding agricultural waste burning) = AGR</li> <li>10. OTHER = All others = OTHER</li> </ul> </li> </ul> <p>India_PMSA_APnA_50airsheds_CAMxOutputs.csv</p> <ul> <li> <p>Summary of estimated source contributions to ambient PM2.5 concentrations, including the contribution of sources outside the city airsheds. These results are explained and discussed in journal articles.<br> 1. Air pollution knowledge assessments (APnA) for 20 Indian cities [<a href="https://doi.org/10.1016/j.uclim.2018.11.005">link</a>]<br> 2. National Clean Air Programme (NCAP) for Indian cities: Review and outlook of clean air action plans [<a href="https://doi.org/10.1016/j.aeaoa.2020.100096">link</a>]<br> 3. Also explained here <a href="https://zenodo.org/record/6919069#.YzKHIHZBxPY">https://zenodo.org/record/6919069#.YzKHIHZBxPY</a> </p> </li> </ul> <p> </p> <p>Original data source at 0.01 degree resolution: <a href="https://sites.wustl.edu/acag/datasets/surface-pm2-5">https://sites.wustl.edu/acag/datasets/surface-pm2-5</a><br> </p>
Global PM2.5 Dataset: Hybrid Calibration of CAMS and MERRA-2 PM2.5 Reanalysis Products during 2017-2019
<p>By integrating two reanalysis PM<sub>2.5</sub> products (CAMSRA, MERRA-2), a <strong>global daily hybrid-calibrated PM<sub>2.5</sub> concentration dataset</strong> is generated through the proposed CM-HC scheme with ERT model. This products include <strong>1095</strong> global PM<sub>2.5</sub> GeoTIFF files, starting from Jan 01, 2017 to Dec 31, 2019 (about <strong>0.95GB</strong> memory after uncompressing this zip file).</p> <p>To prove the superiority of this product, some experiments are implemented as follows: comparing with 1) two original products; 2) results of two separate calibration schemes with ERT; 3) results of CM-HC with other three ML models (RF, GBDT, XGBoost). Above analyses include two aspects, that is, the accuracy results and mapping effects. More details can be viewed at the thesis.</p> <p>One <strong>GeoTIFF</strong> file includes one-day calibrated PM<sub>2.5</sub> data. It is worth noting that the files contain the data of ocean area, however, our thesis only shows the results of land area in order to visually display the mapping effect before and after calibrating. Also, there is no site on the sea to validate in our study.</p>
Global PM2.5 Dataset: Hybrid Calibration of CAMS and MERRA-2 PM2.5 Reanalysis Products during 2017-2019
<p>By integrating two reanalysis PM<sub>2.5</sub> products (CAMSRA, MERRA-2), a <strong>global daily hybrid-calibrated PM<sub>2.5</sub> concentration dataset</strong> is generated through the proposed CM-HC scheme with ERT model. This products include <strong>1095</strong> global PM<sub>2.5</sub> GeoTIFF files, starting from Jan 01, 2017 to Dec 31, 2019 (about <strong>0.95GB</strong> memory after uncompressing this zip file).</p> <p>To prove the superiority of this product, some experiments are implemented as follows: comparing with 1) two original products; 2) results of two separate calibration schemes with ERT; 3) results of CM-HC with other three ML models (RF, GBDT, XGBoost). Above analyses include two aspects, that is, the accuracy results and mapping effects. More details can be viewed at the thesis.</p> <p>One <strong>GeoTIFF</strong> file includes one-day calibrated PM<sub>2.5</sub> data. It is worth noting that the files contain the data of ocean area, however, our thesis only shows the results of land area in order to visually display the mapping effect before and after calibrating. Also, there is no site on the sea to validate in our study.</p>
LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2001)
<p>A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.</p>
LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2013)
<p>A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.</p>
LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2015)
<p>A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.</p>
Long-term (2003-2020) hourly 0.25° global PM2.5 dataset (DeepCAMS) Part-2: 2012-2020
<p>This is part II (2012-2020) of our DeepCAMS.</p> <p>Part I (2003-2011) can be found at: https://doi.org/10.5281/zenodo.6967082</p> <p>Usage: The raw data -- (scaling factor: 0.1) --> the true PM2.5 concentration</p> <p>Paper title: Generating a Long-term (2003-2020) hourly 0.25° global PM2.5 dataset via spatiotemporal downscaling of CAMS with deep learning (DeepCAMS)</p> <p>Paper doi: <a href="https://doi.org/10.1016/j.scitotenv.2022.157747">https://doi.org/10.1016/j.scitotenv.2022.157747</a></p> <pre>If you find our work helpful, please cite it. Thank you very much!</pre>
Global surface O3, NO2, HCHO, and PM2.5 concentrations estimated from deep learning from 2019 to 2023
Open the record for dataset details and reuse information.
Global Annual PM2.5 Grids from MODIS, MISR, SeaWiFS and VIIRS Aerosol Optical Depth (AOD), 1998-2022, V5.GL.04
The Global Annual PM2.5 Grids from MODIS, MISR, SeaWiFS and VIIRS Aerosol Optical Depth (AOD), 1998-2022, V5.GL.04 consists of annual concentrations (micrograms per cubic meter) of all composition (i.e. total) ground-level fine particulate matter (PM2.5). This data set combines AOD retrievals from multiple satellite algorithms including the NASA MODerate resolution Imaging Spectroradiometer Collection 6.1 (MODIS C6.1), Multi-angle Imaging SpectroRadiometer Version 23 (MISRv23), MODIS Multi-Angle Implementation of Atmospheric Correction Collection 6 (MAIAC C6), the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) Deep Blue Version 4, along with the Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS). The GEOS-Chem chemical transport model is used to initially relate this total column measure of aerosol to near-surface PM2.5 concentration. Geographically Weighted Regression (GWR) is used with global ground-based measurements from the World Health Organization (WHO) database and available regional networks to predict and adjust for the residual PM2.5 bias per grid cell in the initial satellite-derived values. These estimates are primarily intended to aid in large-scale studies. Gridded data sets are provided at a resolution of 0.01 degrees to allow users to agglomerate data as best meets their particular needs. Data sets are gridded at the finest resolution of the information sources that were incorporated, but do not fully resolve PM2.5 gradients at the gridded resolution due to influence by information sources at coarser resolution. The data are distributed as GeoTIFF and netCDF files and are in WGS84 projection.
Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD), 1998-2019, V4.GL.03
The Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD), 1998-2019, V4.GL.03 consists of annual concentrations (micrograms per cubic meter) of all composition ground-level fine particulate matter (PM2.5). This data set combines AOD retrievals from multiple satellite algorithms including the NASA MODerate resolution Imaging Spectroradiometer Collection 6.1 (MODIS C6.1), Multi-angle Imaging SpectroRadiometer Version 23 (MISRv23), MODIS Multi-Angle Implementation of Atmospheric Correction Collection 6 (MAIAC C6), and the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) Deep Blue Version 4. The GEOS-Chem chemical transport model is used to relate this total column measure of aerosol to near-surface PM2.5 concentration. Geographically Weighted Regression (GWR) is used with global ground-based measurements from the World Health Organization (WHO) database to predict and adjust for the residual PM2.5 bias per grid cell in the initial satellite-derived values. These estimates are primarily intended to aid in large-scale studies. Gridded data sets are provided at a resolution of 0.01 degrees to allow users to agglomerate data as best meets their particular needs. Data sets are gridded at the finest resolution of the information sources that were incorporated, but do not fully resolve PM2.5 gradients at the gridded resolution due to influence by information sources at coarser resolution. The data are distributed as GeoTIFF files and are in WGS84 projection.
MERRA2_CNN_HAQAST bias corrected global hourly surface total PM2.5 mass concentration, V1 (MERRA2_CNN_HAQAST_PM25) at GES DISC
This product provides MERRA-2 bias-corrected global hourly surface total PM2.5 mass concentration with the same horizontal spatial resolution as MERRA-2, covering a temporal range from 2000 to 2024. It is derived using a machine learning (ML) approach with a convolutional neural network (CNN) method and is specifically developed for the NASA Health and Air Quality Applied Sciences Team (HAQAST).The dataset consists of two parameters: MERRA2_CNN_Surface_PM25 and QFLAG. MERRA2_CNN_Surface_PM25, a 3-dimensional variable (time, latitude, longitude), represents the surface PM2.5 concentrations in µg/m³. QFLAG denotes the quality of data at each grid point, where 4 indicates the highest quality and 1 indicates the lowest quality. It is recommended to use QFLAG values of 3 and 4 for quantitative analysis.
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