In outdoor engineering control studies, measured analyte concentration levels change as environmental conditions change. Since the effectiveness of an engineering control varies with environmental conditions, comparisons of measurements taken with engineering controls operating versus those taken in an uncontrolled environment should adjust for these changes. However, environmental parameters are difficult to estimate. In this work, models based on factor analysis are used to account for the effect of environmental variables. These models also describe the phenomenon that greater control efficiency tends to occur at the highest levels of the uncontrolled environment. The approach is combined with the randomized pair (uncontrolled environment determination, engineering control determination) approach that is often used in these studies. Also, investigated are the benefits of taking samples at different locations and of different analytes. Results of the factor analysis models are compared with those from regressions of the log ratio (controlled/ uncontrolled) on the uncontrolled determination. Implications for statistical design are also discussed. Results from the example data set indicate that the factor analytic approach can identify a common factor, and thereby provide evidence that the common factor is due to environment. However, the simpler regression approach provides estimated reductions that are also statistically unbiased. Therefore, the factor analytic approach may be most useful in early stages of a study, to assess common environmental effects, after which the simpler regression approach may be used to obtain estimates of control effectiveness.
Engineering-controls; Outdoors; Environmental-engineering; Environmental-factors; Models; Sampling; Control-methods; Control-technology; Environmental-control; Environmental-engineering; Environmental-factors; Analytical-models; Sampling-methods; Statistical-analysis; Mathematical-models; Measurement-equipment