Comparison of measured and predicted environmental PCB concentrations using simple compartmental models.

Abstract

The use of models to represent biological, chemical, and physical processes that govern the fate and transport of environmental contaminants is an enduring feature of risk assessments. Data collection is costly and time-consuming. Measuring future conditions is impossible regardless of the resources available. For these reasons, rarely do analysts have sufficient empirical data for estimating risks in a population of interest across the desired dimensions of space and time. The appropriate level of complexity, detail, and resource investment in a modeling exercise should be established by the intended use of the results generated and by the expected performance of the available modeling options. At one end of the spectrum, the concentration of a contaminant in an environmental medium may be estimated quickly and inexpensively using an intermedia partition coefficient or simple steady state compartmental model. In contrast, a complex dynamic model requiring vast stores of input data, computer power, and run time may be used to estimate concentration. However, because models are generally used when data are scarce or nonexistent, our ability to assess the accuracy and precision of various model options is often limited. The research presented below exploits a relatively unusual opportunity to compare concentration measurements of polychlorinated biphenyls (PCBs) for multiple environmental media with predictions from simple compartmental fate and transport models using two-dimensional Monte Carlo analysis. Simple compartmental models are found to predict measurements quite well overall, although decisions about the treatment of variability and autocorrelation in the airborne load of contaminant with season, weather system, and location influence their performance. The models are assessed at two sites near New Bedford Harbor in Massachusetts, one characterized by higher and more variable contaminant concentrations, while the other is a comparison or "background" site. The difference between model performance in the two locations illustrates some characteristics of situations in which simple models are most appropriate. Under background conditions of relatively low and consistent contaminant levels, a two compartment model generates excellent predictions of the sum of PCB congener concentration in soil based on air concentration. Under more contaminated or more variable conditions, model results are less predictive; however, most fall within an order of magnitude of the data. A comparison of model performance for predicting concentration of the sum of PCB congeners vs for predictions of individual PCB congeners is also pursued. Individual congener concentrations in soil tend to be slightly overpredicted for lighter weight congeners, i.e., those more characteristic of the New Bedford Harbor region, while levels of heavier congeners tend to be underpredicted in circumstances where air concentrations are relatively low. A three compartment model representing air, soil, and plant matter is found to predict levels of PCBs in edible produce within about an order of magnitude of those measured, under the conditions considered. This work demonstrates the relevance and usefulness of results from simple and easily implemented models for fate and transport predictions.