title: 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.
authors.affiliation: School of Public Health, University of Illinois, Chicago, IL, USA. roxolana.kashuba@duke.edu
authors.forename: Roxolana
authors.initials: R
authors.lastname: Kashuba
authors.forename: Peter A
authors.initials: PA
authors.lastname: Scheff
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D004784:Environmental Monitoring / Q000706:statistics & numerical data*; D006813:Humidity; D017711:Nonlinear Dynamics; D052638:Particulate Matter / Q000032:analysis*; D011786:Quality Control; D012044:Regression Analysis; D013696:Temperature; D014481:United States; D014484:United States Environmental Protection Agency
chemical_list: D000393:Air Pollutants; D052638:Particulate Matter