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The influence of air quality model resolution on health impact assessment for fine particulate matter and its components

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Abstract

Health impact assessments for fine particulate matter (PM2.5) often rely on simulated concentrations generated from air quality models. However, at the global level, these models often run at coarse resolutions, resulting in underestimates of peak concentrations in populated areas. This study aims to quantitatively examine the influence of model resolution on the estimates of mortality attributable to PM2.5 and its species in the USA. We use GEOS-Chem, a global 3-D model of atmospheric composition, to simulate the 2008 annual average concentrations of PM2.5 and its six species over North America. The model was run at a fine resolution of 0.5 × 0.66° and a coarse resolution of 2 × 2.5°, and mortality was calculated using output at the two resolutions. Using the fine-modeled concentrations, we estimate that 142,000 PM2.5-related deaths occurred in the USA in 2008, and the coarse resolution produces a national mortality estimate that is 8 % lower than the fine-model estimate. Our spatial analysis of mortality shows that coarse resolutions tend to substantially underestimate mortality in large urban centers. We also re-grid the fine-modeled concentrations to several coarser resolutions and repeat mortality calculation at these resolutions. We found that model resolution tends to have the greatest influence on mortality estimates associated with primary species and the least impact on dust-related mortality. Our findings provide evidence of possible biases in quantitative PM2.5 health impact assessments in applications of global atmospheric models at coarse spatial resolutions.

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Acknowledgments

This research is supported through NASA Applied Sciences Program grant NNX09AN77G. Dr. Ying Li was partially supported by a postdoctoral fellowship from the Earth Institute at Columbia University.

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Authors and Affiliations

  1. Department of Environmental Health, College of Public Health, East Tennessee State University, PO Box 70682, Johnson City, TN, USA

    Ying Li

  2. Department of Mechanical Engineering, University of Colorado at Boulder, 1111 Engineering Drive UCB 427, Boulder, CO, USA

    Daven K. Henze

  3. Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, 722 West 168th Street, New York, NY, USA

    Darby Jack & Patrick L. Kinney

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  1. Ying Li
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  2. Daven K. Henze
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  3. Darby Jack
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Correspondence to Patrick L. Kinney.

Appendix

Appendix

Table 4 Comparing national mortality estimates attributable to each PM2.5 species based on modeled 0.5 × 0.66° concentrations using two approaches: (1) directly applying the simulated annual average concentrations of each species (approach used in this study) and (2) calculating mortality for the total PM2.5 concentration relative to the total PM2.5 minus one species (Punger and West 2013)
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Fig. 8
figure 8figure 8figure 8

Modeled annual average PM2.5 species concentrations in 2008 using GEOS-Chem output at a fine (0.5 × 0.66°) resolution and b coarse (2 × 2.5°) resolution (unit, μg/m3)

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Fig. 9
figure 9

Correlation analysis of the dust concentration and population using fine (0.5 × 0.66°) model results

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Fig. 10
figure 10

The difference in estimated mortality by county attributed to total PM2.5 between the fine-model resolution (0.5 × 0.66°) and the coarse resolution (2 × 2.5°)

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Fig. 11
figure 11

a Estimates of national all-cause mortality due to PM2.5 species at various grid resolutions, including the fine-model resolution (0.5 × 0.66°) and three re-gridded resolution (1 × 1.25, 2 × 2.5, and 4 × 5)

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Li, Y., Henze, D.K., Jack, D. et al. The influence of air quality model resolution on health impact assessment for fine particulate matter and its components. Air Qual Atmos Health 9, 51–68 (2016). https://doi.org/10.1007/s11869-015-0321-z

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