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Façade-Level Monitoring of CO2 Variability under Urban Heat Island Conditions using Low-Cost Sensor Data Loggers

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Comparison of Policy-Relevant Air Quality Metrics Calculated With Sparse In Situ Monitoring and Contiguous

Summer Acker¹, Tracey Holloway^1,2, Kevin M Stewart³

¹Nelson Institute Center for Sustainability and the Global Environment University of Wisconsin-Madison Madison WI USA.

|July 8, 2026

Summary

Satellite data closely matches ground monitor data for assessing U.S. air quality standards (NAAQS). Differences in fine particulate matter (PM2.5) assessments between methods are linked to specific county characteristics and risk factors.

Keywords:

NAAQS monitor‐satellite alignment risk assessment satellite‐derived PM2.5

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Area of Science:

Environmental Science
Atmospheric Science
Public Health

Background:

National Ambient Air Quality Standards (NAAQS) compliance traditionally uses sparse ground-based monitors.
Assessing fine particulate matter (PM2.5) levels across counties presents challenges due to monitor network limitations.

Purpose of the Study:

To compare monitor-based county design values (CDVs) with satellite-derived county design value equivalents (CDVEs) for PM2.5 NAAQS compliance.
To identify factors influencing discrepancies between monitor-based and satellite-based PM2.5 assessments.

Main Methods:

Calculated CDVs using EPA's standard methodology.
Computed CDVEs using Washington University's global satellite-derived PM2.5 data, focusing on the 90th percentile grid value within each county.
Classified counties into aligned and non-aligned groups based on agreement between CDVs and CDVEs regarding NAAQS status.

Main Results:

Satellite-derived CDVEs showed strong agreement with monitor-based CDVs across 536 U.S. counties (r=0.76, rs=0.74).
Discrepancies in non-aligned counties were associated with factors like limited monitors, extreme PM2.5 levels, low monitor coverage, large county size, wildfires, mountainous terrain, deserts, and low urbanization.
The number of risk factors correlated with the magnitude of differences between CDVEs and CDVs, with seven-risk-factor counties exhibiting 5x higher median differences.

Conclusions:

Satellite-derived PM2.5 data offers a valuable, contiguous data source complementing sparse ground monitor networks for regulatory assessments.
Understanding risk factors associated with discrepancies is crucial for integrating satellite data into air quality policy frameworks.
High error-risk counties are concentrated in the Western U.S., while low error-risk counties are prevalent in the Midwest and East.