Nonlinear Regression Adjustments of Multiple Continuous Monitoring Methods Produce Effective Characterization of Short-Term Fine Particulate Matter

Abstract

This study comprehensively characterizes hourly fine particulate matter (PM(2.5)) concentrations measured via a tapered element oscillating microbalance (TEOM), beta-gauge, and nephelometer from four different monitoring sites in U.S. Environment Protection Agency (EPA) Region 5 (in U.S. states Illinois, Michigan, and Wisconsin) and compares them to the Federal Reference Method (FRM). Hourly characterization uses time series and autocorrelation. Hourly data are compared with FRM by averaging across 24-hr sampling periods and modeling against respective daily FRM concentrations. Modeling uses traditional two-variable linear least-squares regression as well as innovative nonlinear regression involving additional meteorological variables such as temperature and humidity. The TEOM shows a relationship with season and temperature, linear correlation as low as 0.7924 and nonlinear model correlation as high as 0.9370 when modeled with temperature. The beta-gauge shows no relationship with season or meteorological variables. It exhibits a linear correlation as low as 0.8505 with the FRM and a nonlinear model correlation as high as 0.9339 when modeled with humidity. The nephelometer shows no relationship with season or temperature but a strong relationship with humidity is observed. A linear correlation as low as 0.3050 and a nonlinear model correlation as high as 0.9508 is observed when modeled with humidity. Nonlinear models have higher correlation than linear models applied to the same dataset. This correlation difference is not always substantial, which may introduce a tradeoff between simplicity of model and degree of statistical association. This project shows that continuous monitor technology produces valid PM(2.5) characterization, with at least partial accounting for variations in concentration from gravimetric reference monitors once appropriate nonlinear adjustments are applied. Although only one regression technically meets new EPA National Ambient Air Quality Standards (NAAQS) Federal Equivalent Method (FEM) correlation coefficient criteria, several others are extremely close, showing optimistic potential for use of this nonlinear adjustment model in garnering EPA NAAQS FEM approval for continuous PM(2.5) sampling methods.

Using structural equation modeling to construct calibration equations relating PM2.5 mass concentration samplers to the federal reference method sampler

“…7, the regression of TEOM on FRM produces a curve that is not too far from the actual calibration curve, because the FRM (used as the independent variable) has substantially smaller imprecision than the TEOM at this temperature. The typical approach used by the EPA and others Bortnick et al, 2002;Vega et al, 2003;Kashuba and Scheff, 2008), however, is to use the regression of the FRM on the TEOM. This regression curve is substantially different from the actual calibration curve (and the other regression curve).…”

“…In these cases, ordinary regression produces two contradictory curves, depending on which device is used as the independent variable in the model, both of which are biased away from the correct calibration curve (Bollen, 1989). Nevertheless, numerous PM 2.5 or PM 10 method comparison studies (e.g., Allen et al, 1997;Ayers et al, 1999;Chung et al, 2001;Charron et al, 2004;Schwab et al, 2006;Zhu et al, 2007) have been based on ordinary regression, and a number of studies (e.g., Bortnick et al, 2002;Vega et al, 2003;Kashuba and Scheff, 2008) comparing TEOM and filter-based (e.g., FRM) measurements have used the less-precise TEOM as the independent variable, exacerbating the problem. In fact, the U.S. Environmental Protection Agency (EPA) guidelines for relating FRM and continuous PM 2.5 measurements specify the use of ordinary linear regression with the presumably less-precise continuous measurement device used as the independent variable.…”

A two-step approach for relating tapered element oscillating microbalance and dichotomous air sampler PM2.5 measurements

“…Several studies have examined the use of a correction model to adjust the TEOM measurements to meet the DQO set out by regulatory agencies (Rizzo et al, 2003;Kashuba and Scheff, 2008). This is of particular interest for jurisdictions in a northern climate, since colder winter months bring the highest discrepancies between the TEOM and dichot.…”