Project Background

The 2017 Global Burden of Diseases, Injuries, and Risk Factors Study (GBD, 2017 (1)) estimated that exposure to ambient fine particulate air pollution (PM2.5) was the leading environmental risk factor for human health, contributing to over 5% of all premature deaths in 2017. While the GBD, in combination with other studies and reporting projects, such as the State of Global Air (SoGA) have put air pollution on the global health agenda, a logical next step towards addressing this health risk is to identify the dominant sources contributing to ambient PM2.5 pollution and its health impacts at the national level for all countries.

The Global Burden of Disease-Major Air Pollution Sources (GBD-MAPS) – Global project, funded by the Health Effects Institute, is a joint collaboration led by researchers at Washington University in St. Louis, the University of British Columbia, the University of Washington, and Dalhousie University. Expanding upon a similar approach used in two previous GBD-MAPS studies (2,3) for India and China, the GBD-MAPS-Global project employs an integrated modeling approach to identify dominant sources of ambient PM2.5 pollution and to quantify the associated health impacts at the national level, for all 195 countries and territories currently included in the GBD project.   

Table of Contents:

This page contains the following information:

1. Approach/Methods
2. Source Code
3. Input Emission Datasets
4. Global Results


Project Leader

Erin McDuffie

Visiting Research Associate

Project Contributors

Melanie Hammer

Postdoctoral Fellow

Liam Bindle

Scientific Applications Software Engineer


GBD-MAPS projects use the following approach:

1. Conduct emission sensitivity simulations with the global GEOS-Chem 3D chemical transport model to identify dominant source contributors to ambient PM2.5 mass.

2. Use GEOS-Chem PM2.5 source sensitivity simulation results with contemporary epidemiological concentration response functions to estimate the source- and fuel-specific disease burden attributable to ambient PM2.5 in each country.


Source Code

GEOS-Chem Code
Global simulations of PM2.5 mass use an updated version of the GEOS-Chem v12.1.0 model [Link]

Analysis Scripts


Input Emission Datasets

Global Anthropogenic Emissions
Emissions in GEOS-Chem simulations are primarily from the new CEDS_GBD-MAPS global gridded emissions inventory, developed using an recent version of the Community Emissions Data System (CEDS_GBD-MAPS) [DOI: 10.5281/zenodo.3865670]

CEDS_GBD-MAPS annual emissions for NOx (as NO2), CO, SO2, NH3, NMVOC, OC, and BC are provided as both country total emissions and global gridded emission fluxes (0.5 x 0.5 degree resolution) for each year from 1970 to 2017. Both datasets are reported as a function of the following 11 source sectors and four fuel-categories.

CEDS_GBD-MAPS Data from 1970 – 2017 are publicly available online with the following DOI [DOI: 10.5281/zenodo.3754964]. Further details in McDuffie, et al., 2020 (4) [Link]

Global Total Emissions by Sector:

(double click legend to isolate a single source)

Global Total Emissions by Fuel Category:

(double click legend to isolate a single source)

Additional Inventories

  • Global Fire Emissions Database (GFED) – open burning, including wildfires and agricultural burning  [Link] (van der Werf, et al., 2017 (5))
  • AFCID – anthropogenic dust from fugitive, combustion, and industrial sources (Philip et al., 2017 (6))
  • Windblown dust
  • MEGAN – biogenic emissions from terrestrial ecosystems [Link](Guenther et al., 2012 (7))



*Further details and results from the GBD-MAPS-Global project will be provided here and in an upcoming manuscript.*




(1) Stanaway, J. D., et al., Global regional, and national comparative risk assessment of 84 behavioral, environmental, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990-2017, Lancet, 392, 1923-1994, doi:10.1016/S0140-6736(18)322225-6, 2018.

(2) GBD MAPS Working Group, Burden of Disease Attributable to Coal-Burning and Other Major Sources of Air Pollution in China, Health Effects Institute, Special Report 20 [Link].

(3) GBD MAPS Working Group, Burden of Disease Attributable to Major Air Pollution Sources in India, Health Effects Institute, Special Report 21 [Link].

(4) McDuffie, E. E., S. J. Smith, P. O’Rourke, K. Tibrewal, C. Venkataraman, E. A. Marais, B. Zheng, M. Crippa, M. Brauer, R. V. Martin, A global anthropogenic emission inventory of atmospheric pollutants from sector- and fuel- specific sources (1970- 2017): An application of the Community Emissions Data System (CEDS), Earth Syst. Sci. Data Discuss.,, in review, 2020 [Link]

(5) van der Werf, G. R., J. T. Randerson, L. Giglio, T. T. van Leeuwen, Y. Chen, B. M. Rogers, M. Mu, M. J. E. van Marle, D. C. Morton, G. J. Collatz, R. J. Yokelson, and P. S. Kasibhatla. Global fire emissions estimates during 1997–2016, Earth Syst. Sci. Data, 9, 697-720, 10.5194/essd-9-697-2017, 2017.

(6) Philip, S., R. V. Martin, G. Snider, C. L. Weagle, A. van Donkelaar, M. Brauer, D. K. Henze, Z. Klimont, C. Venkataraman, S. K. Guttikunda, and Q. Zhang. Anthropogenic fugitive, combustion and industrial dust is a significant, underrepresented fine particulate matter source in global atmospheric models, Environmental Research Letters, 12, 044018, 10.1088/1748-9326/aa65a4, 2017.

(7) Guenther, A. B., X. Jiang, C. L. Heald, T. Sakulyanontvittaya, T. Duhl, L. K. Emmons, and X. Wang, The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions, Geosci. Model Dev., 5, 1471-1492, 10.5194/gmd-5-1471-2012, 2012.