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.
Highlights
Recent advancements in air monitoring have led to increased availability of ‘lower-cost’non-reference method (NRM) technologies that are readily adaptable to a variety of air pollution studies [1,2,3,4,5]
Exposure and Health (CAFEH) team collaborated with Boston neighborhoods near major highways to assess traffic-related air pollution using a variety of sensors [6,7]
We fit a generalized additive model (GAM) to establish a non-linear calibration curve that defines the observed relationship between the Non-Reference Method (NRM) and federal equivalent method (FEM) data
Summary
Non-reference method (NRM) technologies that are readily adaptable to a variety of air pollution studies [1,2,3,4,5] Such developments provide a tremendous opportunity to improve studies of air pollution, at smaller scales such as the community or neighborhood level, where collecting air monitoring data was previously unfeasible. A community with a heavy petrochemical industry presence in Richmond, CA, has the potential to alert the community during poor air quality episodes using their real time monitoring tools [9]. In their quest to collect their own community air monitoring data, have found surprising results such as the use of illegal truck routes and the contribution of meteorology and topography to local air quality [10,11,12,13]
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