Distribution and sources of PCBs (Aroclor 1268) in the Sapelo Island National estuarine research reserve.

Aroclor 1268 is a highly chlorinated PCB mixture that was released into the aquatic environment near Brunswick, GA (BR), as a result of decades of local industrial activity. This extensive contamination has led to US EPA Superfund designation in estuarine areas in and around Purvis Creek, GA. Roughly 50 km to the northeast is the Sapelo Island National Estuarine Research Reserve (SI) where previous studies have documented unexpectedly high Aroclor 1268-like PCB levels in blubber and plasma samples of resident bottlenose dolphins. This result led to a collaborative effort to assess the PCB patterns and concentrations in SI sediment and fish (as potential vectors for PCB transfer to SI resident dolphins). Thirty SI randomly assigned stations were sampled for sediment PCB levels. Additionally, fish were collected and analyzed from SI (n = 31) and BR (n = 33). Results were pooled with regional assessments of PCB concentrations from South Carolina and North Carolina in an effort to determine the association of Aroclor 1268 levels in SI samples. Results indicated that PCB levels in sediment and fish are much lower in the SI estuary compared to BR sediment and fish concentrations. However, PCB congener profiles for both sediments and fish were similar between the two locations and consistent with the Aroclor 1268 signature, indicating possible transport from the Brunswick area. A likely source of Aroclor 1268 in dolphins from SI is contaminated fish prey.

Random number generation from right-skewed, symmetric, and left-skewed distributions.

Monte Carlo simulations have become a mainstream technique for environmental and technical risk assessments. Because their results are dependent on the quality of the involved input distributions, it is important to identify distributions that are flexible enough to model all relevant data yet efficient enough to allow thousands of evaluations necessary in a typical simulation analysis. It has been shown in recent years that the S-distribution provides accurate representations for frequency data that are symmetric or skewed to either side. This flexibility makes the S-distribution an ideal candidate for Monte Carlo analyses. To use the distribution effectively, methods must be available for drawing S-distributed random numbers. Such a method is proposed here. It is shown that S-distributed random numbers can be efficiently generated from a simple algebraic formula whose coefficients are tabulated. The method is shown step by step and illustrated with a detailed example. (The tables are accessible in electronic form in the FTP parent directory at http:@www.musc.edu/voiteo/ftp/.)