Findings (Nov 2023)

Intercomparison of Six National Empirical Models for PM2.5 Air Pollution in the Contiguous US

  • Matthew J. Bechle,
  • Michelle L. Bell,
  • Daniel L. Goldberg,
  • Steve Hankey,
  • Tianjun Lu,
  • Albert A. Presto,
  • Allen L. Robinson,
  • Joel Schwartz,
  • Liuhua Shi,
  • Yang Zhang,
  • Julian D. Marshall

Abstract

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Empirical models aim to predict spatial variability in concentrations of outdoor air pollution. For year-2010 concentrations of PM~2.5~ in the US, we intercompared six national-scale empirical models, each generated by a different research group. Despite differences in methods and independent variables for the models, we find a relatively high degree of agreement among model predictions (e.g., correlations of 0.84 to 0.92, RMSD (root-mean-square-difference; units: μg/m^3^) of 0.8 to 1.4, or on average \~12% of the average concentration; many best-fit lines are near the 1:1 line).