A Statistical Calibration Framework for Improving Non-Reference Method Particulate Matter Reporting: A Focus on Community Air Monitoring Settings

Abstract

Recent advancement in lower-cost air monitoring technology has resulted in an increased interest in community-based air quality studies. However, non-reference monitoring (NRM; e.g., low-cost sensors) is imperfect and approaches that improve data quality are highly desired. Herein, we illustrate a framework for adjusting continuous NRM measures of particulate matter (PM) with field-based comparisons and non-linear statistical modeling as an example of instrument evaluation prior to exposure assessment. First, we collected continuous measurements of PM with a NRM technology collocated with a US EPA federal equivalent method (FEM). Next, we fit a generalized additive model (GAM) to establish a non-linear calibration curve that defines the relationship between the NRM and FEM data. Then, we used our fitted model to generate calibrated NRM PM data. Evaluation of raw NRM PM2.5 data revealed strong correlation with FEM (R = 0.9) but an average bias (AB) of −2.84 µg/m3 and a root mean square error (RMSE) of 2.85 µg/m3, with 406 h of data. Fitting of our GAM revealed that the correlation structure was maintained (r = 0.9) and that average bias (AB = 0) and error (RMSE = 0) were minimized. We conclude that field-based statistical calibration models can be used to reduce bias and improve NRM data used for community air monitoring studies.

Keywords

• air pollution
• citizen science
• community-based participatory research
• generalized additive models
• low-cost sensors