Effect of Model Spatial Resolution on Estimates of Fine Particulate Matter Exposure and Exposure Disparities in the United States

David A. Paolella, Christopher W. Tessum, Peter J. Adams, Joshua S. Apte, Sarah Chambliss, Jason Hill, Nicholas Z. Muller, Julian D. Marshall

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

To the extent that pollution and population are spatially correlated, air quality modeling with coarse-resolution horizontal grids may systematically underpredict exposures and disparities in exposure among demographic groups (i.e., environmental injustice). We use InMAP, a reduced-complexity air pollution model, to quantify how estimates of year-2014 fine particulate matter (PM2.5) exposure in the United States vary with model spatial resolution, for a variable-resolution grid. We test five grids, with population-weighted average grid cell edge lengths ranging from 5.9 to 69 km. We find that model-estimated PM2.5 exposure, and exposure disparities among racial-ethnic groups, are lower with coarse grids than with fine grids: switching from our coarsest- to finest-resolution grid increases the calculated population-weighted average exposure by 27% (from 6.6 to 8.3 μg m-3) and causes the estimated difference in average exposure between minorities and whites to increase substantially (from 0.4 to 1.6 μg m-3). Across all grid resolutions, exposure disparities by race-ethnicity can be detected in every income category. Exposure disparities by income alone remain small relative to disparities by race-ethnicity, irrespective of resolution. These results demonstrate the importance of fine model spatial resolution for identifying and quantifying exposure disparity.

Original languageEnglish (US)
Pages (from-to)436-441
Number of pages6
JournalEnvironmental Science and Technology Letters
Volume5
Issue number7
DOIs
StatePublished - Jul 10 2018

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