Comparison of Policy‐Relevant Air Quality Metrics Calculated With Sparse In Situ Monitoring and Contiguous Satellite‐Derived Data

Abstract Compliance with the U.S. National Ambient Air Quality Standards (NAAQS) traditionally relies on a sparse network of ground‐based monitors. We compare two methods for assessing whether U.S. counties are above or below the annual fine particulate matter (PM 2.5 ) NAAQS of 9.0 μg/m 3 : the monitor‐based methodology used to calculate county design values (CDVs; an EPA metric representing the maximum, in‐county PM 2.5 monitor value over a 3‐year annual average), and a satellite‐based methodology that computes county design value equivalents (CDVEs) from Washington University's global satellite‐derived PM 2.5 data product. The satellite‐based approach uses the 90th percentile PM 2.5 grid value in a county and shows strong overall agreement with monitor‐based CDVs across 536 monitored U.S. counties ( r = 0.76; r s = 0.74). Counties were classified as aligned positives (APs), aligned negatives (ANs), non‐aligned positives (NPs), or non‐aligned negatives (NNs) based on whether CDVEs align or don't align with CDVs on NAAQS status. Risk factors likely contributing to larger differences between CDVEs and CDVs within non‐aligned counties include ≤2 monitors, low (10 μg/m 3 ) CDVs, low (0.1%) monitor coverage, large county size, wildfires, mountains, deserts, and low urbanization. In non‐aligned counties, differences grow with the number of risk factors, with seven‐risk‐factor counties showing 5x higher median differences than those with one. High error‐risk counties cluster in the Western U.S.; low error‐risk counties are in the Midwest and East. These findings highlight trade‐offs between sparse and contiguous data for regulatory assessments and support integration of satellite‐derived data into policy frameworks.