GBD-MAPS-Global: Gridded Fractional Source Contribution Results
Bibliographic record
Abstract
Overview This dataset contains the modeled gridded fractional source contribution results for the 2021 Global Burden of Disease - Major Air Pollution Sources (GBD-MAPS) - Global study. Fractional results aggregated to the regional, national, and sub-national scales are available as Supplementary Datasets in McDuffie et al., Nature Communications, 2021. This GBD-MAPS-Global methodology and results are described in the following article: McDuffie, E. E., Martin, R. V., Spadaro, J. V., Burnett, R., Smith, S. J., O'Rourke, P., Hammer, M., van Donkelaar, A., Bindle, L., Shah, V., Jaegle, L., Luo, G., Yu, F., Adeniran, J., Lin, J., Brauer, M. Source Sector and Fuel Contributions to Ambient PM2.5 Attributable Mortality Across Multiple Spatial Scales, Nature Communications Notes: All results provide the modeled fractional contribution of each designated source category to total surface PM2.5 mass and attributable disease burden. Fractional source contribution results are derived from emission sensitivity simulations using the GEOS-Chem 3D global chemical transport model (DOI: 10.5281/zenodo.4718622) and emissions from the Community Emissions Data System (GBD-MAPS update) (DOI: 10.5281/zenodo.3754964), unless otherwise noted. Results are provided at the 0.01°x0.01° (~1 km x 1 km) spatial resolution and are based on original simulations conducted at 2°x2.5° (global) and 0.5°x0.625° (Europe, North America, and Asia regions) degree resolutions. All units are in fractional percentages (e.g., 0.0245 is 2.45%). To calculate gridded absolute contributions of each source to PM2.5 mass (used in the GBD-MAPS analysis): 1) Multiply the 0.01°x0.01° fractional source contributions by downscaled 0.01°x0.01° resolution PM2.5 mass estimates, available in the DownscaledPM.zip folder as part of the GBD-MAPS-Global: Analysis Input Dataset (DOI: 10.5281/zenodo.4642700) Data File Descriptions: LatLon - The latitude and longitude values that correspond to each data point in the source-specific files (0.01°x0.01° resolution) Sector Files (sum to 100%): AFCID - Anthropogenic Fugitive, Combustion, and Industrial Dust AGR - Agriculture - includes manure management, soil fertilizer emissions, rice cultivation, enteric fermentation, and other agriculture ENEcoal - Energy Production (coal combustion only) - Includes electricity and heat production, fuel production and transformation, oil and gas fugitive/flaring, and fossil fuel fires 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 GFEDagburn - Agricultural Waste Burning - Includes solid waste disposal, waste incineration, waste-water handling, and other waste handling (from the GFED fires inventory) GFEDoburn - Other Open Fires - Includes deforestation, boreal forest, peat, savannah, and temperate forest fires (from the GFED fires inventory) 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) 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) NRTR - non-road/ off-road transportation - Includes Rail, Domestic navigation, Other transportation 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) RCOC - Commercial Combustion - Includes commercial and institutional combustion RCOO - Other Combustion - Includes combustion from agriculture, forestry, and fishing RCORbiofuel - Residential combustion (solid biofuel combustion only) - includes residential heating and cooking RCORcoal - Residential combustion (coal combustion only) - includes residential heating and cooking RCORother - Residential Combustion (all non-coal and non-solid biofuel) - includes residential heating and cooking ROAD - Road Transportation - includes cars, motorcycles, heavy and light duty trucks and buses SHP - International Shipping - Includes international shipping and tanker loading SLV - Solvents - Includes solvents production and application (degreasing and cleaning, paint application, chemical products manufacturing and processing, and other product use) WDUST - Windblown Dust - (from the DEAD dust model) WST - Waste - Includes solid waste disposal, waste incineration, waste-water handling, and other waste handling Fuel Categories (do not sum to 100% for each grid cell as these only include combustion sources of PM2.5): BIOFUEL - Solid Biofuel (or biomass) Combustion- Includes solid biofuel COAL - Total Coal Combustion - Includes hard coal, brown coal, coal coke OILGAS- Liquid Oil and Natural Gas Combustion - Includes light and heavy oil, diesel oil, and natural gas
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.023 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".