GBD-MAPS – Global

Project Background

The 2019 Global Burden of Diseases, Injuries, and Risk Factors Study estimated that long-term exposure to ambient fine particulate air pollution (PM2.5) was the leading environmental risk factor for human health, contributing to over 4 million attributable deaths in 2019 (GBD Compare, 2021 (1)). While the GBD, in combination with other 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 global ambient PM2.5 pollution and its health impacts across multiple spatial scales.

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. 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 regional, national, and sub-national levels, for all 204 countries and territories currently included in the GBD project.

Table of Contents:

This page contains the following information:

1. Contributors
2. Approach/Methods
3. Source Code/Analysis Scripts
4. Input Datasets
5. Global Results

 

Project Contributors:

Project Lead:

Erin McDuffie (Washington University in St. Louis)

Principal Investigators:

Michael Brauer (University of British Columbia, University of Washington)
Randall Martin (Washington University in St. Louis)

Primary Contributors:

Joseph Spadaro (Spadaro Environmental Research Consultants)
Richard Burnett (University of Washington)
Steven Smith (Pacific Northwest National Laboratory)
Patrick O’Rourke (Pacific Northwest National Laboratory)
Melanie Hammer (Washington University in St. Louis, Dalhousie University)
Aaron van Donkelaar (Dalhousie University, Washington University in St. Louis)
Liam Bindle (Washington University in St. Louis)
Hao Lin (University of British Columbia) – data visualization site


Approach/Methods

GBD-MAPS projects use the following integrated modeling 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. Integrate GEOS-Chem sensitivity simulation results with high resolution PM2.5 exposure estimates, epidemiological concentration response relationships, and baseline mortality data from the GBD to estimate the source and fuel specific contributions to PM2.5 mass and the attributable mortality.

 


Source Code/Analysis Scripts

GEOS-Chem Model Source Code
Global simulations of PM2.5 mass use an updated version of the GEOS-Chem v12.1.0 model [Source Code DOI: 10.5281/zenodo.4718622] [Link]

CEDSGBD-MAPS Emissions Model Source Code
The anthropogenic emissions dataset was produced using an updated version of the Community Emissions Data System (CEDS) [Source code DOI: 10.5281/zenodo.3865670]  [Link] 

Analysis Scripts
The analysis scripts package for the GBD-MAPS Global study is written in MATLAB and uses the Input Data package described below. [Scripts package DOI: 10.5281/zenodo.4718618] [Link]


Input Datasets

1) Global Anthropogenic Emissions
Anthropogenic emissions are largely from the new CEDSGBD-MAPS global gridded emissions inventory, developed using an updated version of the Community Emissions Data System (CEDSGBD-MAPS)

CEDSGBD-MAPS annual global emissions for NOx (as NO2), CO, SO2, NH3, NMVOC, OC, and BC are provided as emission totals for each country as well as 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 11 source sectors and four fuel-categories (see Figures below).

The CEDSGBD-MAPS dataset is publicly available online with the following DOI [DOI: 10.5281/zenodo.3754964]. Further details are described in McDuffie, et al., 2020 (4) [Link]

Global Total Emissions by CEDS Sector:

(double click legend to isolate a single source)

Global Total Emissions by Fuel Category:
(double click legend to isolate a single source)

2) Additional Datasets

Additional input datasets [link] include:

  • global high resolution PM2.5 exposure estimates
  • disease-specific GBD concentration response relationships
  • national and disease specific GBD baseline burden data

 


Results

Study results are presented and discussed in McDuffie et al., Nature Communications (2021) [Link]. Annual fractional source contributions to surface PM2.5 mass and the associated disease burden are available in three datasets that are Supplementary to the manuscript. Gridded fractional source contribution results are also available [Link].

Figure 2 from McDuffie et al., Nature Communications (2021)

An interactive data visualization site was developed by the University of British Columbia [Link].


References

(1) GBD Compare Visualization Tool, 2021. [Link]. Data from: GBD 2019 Risk Factor Collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet 396, 1223-1249, https://doi.org/10.1016/S0140-6736(20)30752-2, 2020. [Link]

(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, https://doi.org/10.5194/essd-12-3413-2020, 2020 [Link]