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

Year: 2023
Type: article
Abstract: 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).
Source: Findings
DOI: 10.32866/001c.89423
Authors: 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
Institutions: University of Washington, Yale University, George Washington University, Virginia Tech, California State University, Dominguez Hills, Carnegie Mellon University, Harvard University, Emory University, Northeastern University
Language: English
Open Access status: gold
Estimated APC paid: $100
OpenAlex ID: W4388721666
FWCI: 0.249
Cites: 53
Cited by: 3
Related to: 10