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247 results for “PM2.5”
Dataset for Exploring Co-benefits Between PM2.5 Control and Carbon Reduction
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Surface Ozone, NO2, and PM2.5 Concentrations Estimated by the Deep Learning model (Air Transformer) based on Satellite data.
<p>Surface ozone, NO2, and PM2.5 concentrations Estimated by the deep learning model (Air Transformer) based on massive ground-level monitoring, satellite observations, meteorological conditions, dynamic industrial emissions, and other ancillary data from May 2018 to June 2021.</p>
Wildfire, prescribed burn, and agricultural burn smoke PM2.5 estimates for CA, WA, and OR 2014-2020
<p>Wildfires, prescribed burns, and agricultural burns all impact ambient air quality across the Western U.S.; however, little is known about how communities across the region are differentially exposed to smoke from each of these fire types. To address this gap, we quantify smoke exposure stemming from wildfire, prescribed, and agricultural burns across Washington, Oregon, and California from 2014-2020 using a fire type-specific biomass burning emissions inventory and the GEOS-Chem chemical transport model. We examine fire type-specific PM<sub>2.5</sub> concentration by race/ethnicity, socioeconomic status, and in relation to the Center for Disease Control's Social Vulnerability Index. Overall, population average PM<sub>2.5</sub> concentrations are greater from wildfires than from prescribed and agricultural burns. While we found limited evidence of exposure disparities among sub-groups across the full study area, we did observe disproportionately higher exposures to wildfire-specific PM<sub>2.5</sub> exposures among Native communities in all three states and, in California, higher agricultural burn-specific PM<sub>2.5</sub> exposures among lower socioeconomic groups. We also identified, for all three states, areas of significant spatial clustering of smoke exposures from all fire types and increased social vulnerability. These results provide a first look at the differential contributions of smoke from wildfires, prescribed burns, and agricultural burns to PM<sub>2.5</sub> exposures among demographic subgroups, which can be used to inform more tailored exposure reduction strategies across sources.</p>
Data used in "Chemically specific sampling bias: the ratio of PM2.5 to surface AOD on average and peak days in the U.S."
<p>This dataset contains all relevant data used in the manuscript "Chemically specific sampling bias: the ratio of PM2.5 to surface AOD on average and peak days in the U.S." published in Environmental Science: Atmospheres.</p>
High-Quality Daily PM2.5 Datasets for India at 10 km Resolution (Version 2)
<div> <div> <div> <div> <div> </div> </div> </div> </div> <div> <div> <div> <div> <div> <div> <p>If you use this dataset in your research/work, please cite the following paper:</p> <p><strong>Kawano, Ayako, et al. "Improved daily PM2.5 estimates in India reveal inequalities in recent enhancement of air quality." <em>Science Advances</em> 11.4 (2025): eadq1071. <a href="https://doi.org/10.1126/sciadv.adq1071">DOI: 10.1126/sciadv.adq1071</a></strong></p> <p>Thank you for acknowledging our work!</p> </div> </div> </div> </div> </div> </div> </div> <div>----------------------------------------------</div> <div> </div> <div>Open-source daily fine particulate matter (PM2.5) datasets at a 10 km resolution for India from 2005 to 2023, using a region-specific two-stage machine learning model carefully validated on held-out monitor data that it was not trained on. Our model demonstrates robust out-of-sample performance, substantially outperforming existing publicly-available monthly PM2.5 datasets.</div> <div> </div> <div>To take advantage of both the longer available time series of Aerosol Optical Depth (AOD) data and information from newer sensors such as TROPOspheric Monitoring Instrument (TROPOMI), we developed two separate machine learning models - the "Full model" and the "AOD model".</div> <div> </div> <div><strong>Full model:</strong></div> <div> <ul> <li>Predictive performance (spatial cross-validation): R2 value of 0.67, RMSE of 27.79 μg/m3</li> <li>Input features: Moderate Resolution Imaging Spectroradiometer (MODIS) AOD and TROPOMI satellite inputs along with other remote sensing data</li> <li>Daily PM2.5 predictions for: July 10, 2018 - September 30, 2023</li> </ul> </div> <div><strong>AOD model:</strong> </div> <div> <div> <ul> <li>Predictive performance (spatial cross-validation): R2 value of 0.64, RMSE of 32.08 μg/m3</li> <li>Input features: all inputs except TROPOMI used for the Full model</li> <li>Daily PM2.5 predictions for: January 1, 2005 - September 30, 2023</li> </ul> </div> </div> <div> </div> <div>Please note that we employed spatial cross-validation (CV) rather than more conventional random CV to be responsible for predicting daily PM2.5 concentrations for locations without air quality monitors across India. When the above Full model was evaluated using 10-fold random CV, it showed notably higher performance (<strong>R2 of 0.85 and RMSE of 18.48 μg/m3</strong>). This highlights the potential of random CV to overstate model performance on critical real-world applications.</div> <div> </div> <div>Code and source data needed to replicate the results have been also deposited. </div>
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>
AirGAM 2022r1 PM2.5 results for all stations 2005-2019
<p>Contains all trend, cross-validation and evaluation results for PM2.5.</p>
The hourly PM2.5 observation data over the Pearl River Delta region
<p>This dataset contains the hourly PM<sub>2.5</sub> observation data over the PRD region used in the JGR-Atmospheres paper.</p>
GEOS-Chem 2015 Speciated PM2.5 Outputs for "Impact of circular- and single-sector waste-heat reuse pathways on PM2.5-air quality, CO2 emissions, and human health in India; material exchanges more viable to achieve sustainability targets"
<p>Daily PM2.5 and species outputs from GEOS-Chem Modeling over India (0.5° x 0.625°) </p>
Chemical composition of PM2.5 for Deyang campaign
<p>The file contains the concentrations of different PM2.5 compositions measured by a ToF-ACSM, including organics, nitrate, sulphate, ammonium and chloride (μg/m3). The interval of the campaign was from 18 December 2021 to 22 January 2022, adn the temporal resolution of the data is 10 min.</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>
Variations of the urban PM2.5 chemical components and corresponding light extinction for three heating seasons in the Guanzhong Plain, China
<p>The basic data of the thesis “Variations of the urban PM2.5 chemical components and corresponding light extinction for three heating seasons in the Guanzhong Plain, China”.</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>
Wildland fire PM2.5 modeled estimates for the US from 2008-2018
<p>This dataset includes daily modeled wildland fire PM<sub>2.5 </sub>concentrations for 2008-2018 for the state of California at a 12-km grid spatial resolution, estimated using the U.S. EPA's Community Multiscale Air Quality (CMAQ) (v. 5.0.1- 5.3) modeling system. These wildland fire emissions estimates (which include wildfires and prescribed burns [but exclude agricultural burns]) incorporate multiple sources of fire activity. SMARTFIRE2 was used to reconcile the sources of fire activity data. Fuel consumption was calculated using the U.S. Forest Service's CONSUME ver. 3.0 fuel consumption model and the Fuel Characteristic Classification System (FCCS) fuel-loading database in the BlueSky Framework. Emission factors were taken from the Fire Emission Production Simulator (FEPS) model. Non-fire emissions sources are from the National Emissions Inventory (NEI). The model was run with all emissions (fire and non-fire sources) and again without fires. The calculated difference between these simulations ('all sources PM<sub>2.5</sub>'<sub> </sub>and 'non-fire PM<sub>2.5</sub>') isolates the fire contribution, or 'fire-only PM<sub>2.5</sub>', which is the dataset provided here. </p>
Numerical model-informed testbed for surface PM2.5 concentration over China and its estimates during 2013-2021
<p>This is an updated version of https://doi.org/10.5281/zenodo.11122294, the data provided in NetCDF format.</p> <p>In addition to the long-term PM2.5 dataset created from Li et al (2024), which can be used for health assessments and studying air pollution influences, here we also provide testbed data crucial for evaluating machine learning-based retrieval methods, especially in scenarios where no ground-truth data is available.<br>The testbed dataset includes all inputs and outputs following the physical model simulation, which naturally correlates with physical laws such as emissions, diffusion, advection, and deposition, representing typical conditions that any prediction method should meet. This data can be used to evaluate and compare methods using the same dataset, allowing for continuous improvement. Besides traditional cross-validation, the proposed testbed validation is highly recommended to examine a method’s predictive ability. We will continue updating the testbed data for other pollutants and with different resolutions and regions in future studies.</p>
Attributing human mortality from fire PM2.5 to climate change
<p>The dataset and scripts are for reanalysis the paper: Attributing human mortality from fire PM2.5 to climate change. </p> <p>The script is written in MATLAB. see Readme.txt and FireMort_SI_re.mlx for detail. </p> <p>Unzip files and make the folder (/Input, /Output, /Major_input, /Minor_input). </p> <p>Some original dataset (which can be directly driven from ISIMIP repository) were not included. </p> <p>If you need more information, please contact Chaeyeon Park (email- park.chaeyeon@aist.go.jp, or chaeyeon528@gmail.com)</p>
Prescribed Burn Related Increases of Population Exposure to PM2.5 and O3 Pollution in the Southeastern US over 2013–2020
<p>Daily prescribed burn PM2.5 and MDA8-O3</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>
A Modeling Framework for Near-Road Population Exposure to Traffic-Related PM2.5 and Environmental Equity Analysis: A Case Study in Atlanta, Georgia
<p>This is the dataset for the NCST project <em>"A Modeling Framework for Near-Road Population Exposure to Traffic-Related PM2.5 and Environmental Equity Analysis: A Case Study in Atlanta, Georgia"</em> by the Georgia Tech research team.</p> <p> </p> <p>Here is the abstract of the research: </p> <p>In this study, a modeling framework for population exposure to traffic-related PM2.5 with high spatiotemporal resolution is proposed and applied to the I-575/I-75 Northwest Corridor (NWC) in Atlanta, GA, for environmental equity analysis. The analyses retrieved trip data from the Atlanta Regional Commission’s (ARC) Activity-Based Model 2020 (ABM2020), after implementing path retention algorithms (Zhao, et al., 2019) to generate individual travel paths for more than 20 million predicted vehicle trips. Emission rates for each link were retrieved from MOVES-Matrix given the ABM link speed and facility type, the ARC’s county-level fleet composition data, and regional fuel properties and I&M program parameters. High-resolution downwind concentration profiles were predicted using EPA’s AERMOD microscale dispersion model with AERMET meteorology profiles for a huge array of receptors. Trip-end locations were derived from the ABM trip data, and the on-road trajectories for each person-trip (vehicle trace data) were derived from the travel paths through network. ABM synthetic household and person data were used in demographic assessment, and linked to representative household latitude and longitude locations in the Epsilon 2019 household demographic dataset. Individual exposure to traffic-related PM2.5 in time and space (average hourly concentration) was assessed by overlaying the second-by-second person location profiles (for 24 hours) against the hourly predicted PM2.5 concentration profiles. The analyses summarize the results across 16 demographic groups and the aggregate population exposure are compared to assess potential impact differences across demographics. High-income households in the corridor were exposed to less traffic-related air pollution as they tended to live further from the freeways. The analyses did not reveal large disproportionate negative impacts on low income groups along this specific corridor, but lager disproportionate negative impacts are expected elsewhere in the metro area due to the spatial clustering of income groups along other corridors. Overall, the research demonstrates the applicability of the modeling framework and describes how the various elements (e.g., link screening, dispersion modeling, path tracing, etc.) are optimized on the supercomputing cluster.</p>
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Allen Brain Atlas
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OpenNeuro
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