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Several commonly used statistical methods for fingerprint identification in microbial source tracking (MST) were examined to assess the effectiveness of pattern-matching algorithms to correctly identify sources. Although numerous statistical methods have been employed for source identification, no widespread consensus exists as to which is most appropriate. A large-scale comparison of several MST methods, using identical fecal sources, presented a unique opportunity to assess the utility of several popular statistical methods. These included discriminant analysis, nearest neighbour analysis, maximum similarity and average similarity, along with several measures of distance or similarity. Threshold criteria for excluding uncertain or poorly matched isolates from final analysis were also examined for their ability to reduce false positives and increase prediction success. Six independent libraries used in the study were constructed from indicator bacteria isolated from fecal materials of humans, seagulls, cows and dogs. Three of these libraries were constructed using the rep-PCR technique and three relied on antibiotic resistance analysis (ARA). Five of the libraries were constructed using Escherichia coli and one using Enterococcus spp. (ARA). Overall, the outcome of this study suggests a high degree of variability across statistical methods. Despite large differences in correct classification rates among the statistical methods, no single statistical approach emerged as superior. Thresholds failed to consistently increase rates of correct classification and improvement was often associated with substantial effective sample size reduction. Recommendations are provided to aid in selecting appropriate analyses for these types of data.

A number of sediment quality guidelines (SQGs) have been developed for relating chemical concentrations in sediment to their potential for effects on benthic macroinvertebrates, but there have been few studies evaluating the relative effectiveness of different SQG approaches. Here we apply 6 empirical SQG approaches to assess how well they predict toxicity in California sediments. Four of the SQG approaches were nationally derived indices that were established in previous studies: effects range median (ERM), logistic regression model (LRM), sediment quality guideline quotient 1 (SQGQ1), and Consensus. Two approaches were variations of nationally derived approaches that were recalibrated to California-specific data (CA LRM and CA ERM). Each SQG approach was applied to a standardized set of matched chemistry and toxicity data for California and an index of the aggregate magnitude of contamination (e.g., mean SQG quotient or maximum probability of toxicity) was calculated. A set of 3 thresholds for classification of the results into 4 categories of predicted toxicity was established for each SQG approach using a statistical optimization procedure. The performance of each SQG approach was evaluated in terms of correlation and categorical classification accuracy. Each SQG index had a significant, but low, correlation with toxicity and was able to correctly classify the level of toxicity for up to 40% of samples. The CA LRM had the best overall performance, but the magnitude of differences in classification accuracy among the SQG approaches was relatively small. Recalibration of the indices using California data improved performance of the LRM, but not the ERM. The LRM approach is more amenable to revision than other national SQGs, which is a desirable attribute for use in programs where the ability to incorporate new information or chemicals of concern is important. The use of a consistent threshold development approach appeared to be a more important factor than type of SQG approach in determining SQG performance. The relatively small change in classification accuracy obtained with regional calibration of these SQG approaches suggests that further calibration and normalization efforts are likely to have limited success in improving classification accuracy associated with biological effects. Fundamental changes to both SQG components and conceptual approach are needed to obtain substantial improvements in performance. These changes include updating the guideline values to include current use pesticides, as well as developing improved approaches that account for changes in contaminant bioavailability.

Polybrominated diphenyl ethers (PBDEs) were measured in surface sediments from 121 locations within the Southern California Bight. Site selection was based on a probabilistic approach to determine the spatial extent and magnitude of PBDE concentrations with known confidence intervals. Coastal embayments (including estuaries, marinas, ports, and bays) and the continental shelf out to the lower slope were sampled. Thirteen PBDEs were detected at 92 of the sites, with a geometric mean and maximum of 4.7 and 560 ng/g dry weight (sum of 13 congeners), respectively. The PBDE concentrations were higher in coastal embayments than in offshore locations. Embayments had an area-weighted geometric mean total PBDE concentration of 12 (95% confidence interval, 8.0-17) ng/g dry weight and a total PBDE mass of 110 (77-160) kg. The offshore stratum, which is 99% of the total area, had an area-weighted geometric mean total PBDE concentration of 2.0 (1.6-2.5) ng/g dry weight and a total PBDE mass of 860 (700-1,100) kg. The five highest PBDE concentrations were associated with the mouths of urban rivers, indicating that urban runoff is likely a major input of PBDEs to these coastal marine waters. The outfalls of wastewater treatment plants were not observed to be major sources.