Evaluating performance of stormwater sampling approaches using a dynamic watershed model.

Accurate quantification of stormwater pollutant levels is essential for estimating overall contaminant discharge to receiving waters. Numerous sampling approaches exist that attempt to balance accuracy against the costs associated with the sampling method. This study employs a novel and practical approach of evaluating the accuracy of different stormwater monitoring methodologies using stormflows and constituent concentrations produced by a fully validated continuous simulation watershed model. A major advantage of using a watershed model to simulate pollutant concentrations is that a large number of storms representing a broad range of conditions can be applied in testing the various sampling approaches. Seventy-eight distinct methodologies were evaluated by "virtual samplings" of 166 simulated storms of varying size, intensity and duration, representing 14 years of storms in Ballona Creek near Los Angeles, California. The 78 methods can be grouped into four general strategies: volume-paced compositing, time-paced compositing, pollutograph sampling, and microsampling. The performances of each sampling strategy was evaluated by comparing the (1) median relative error between the virtually sampled and the true modeled event mean concentration (EMC) of each storm (accuracy), (2) median absolute deviation about the median or "MAD" of the relative error or (precision), and (3) the percentage of storms where sampling methods were within 10% of the true EMC (combined measures of accuracy and precision). Finally, costs associated with site setup, sampling, and laboratory analysis were estimated for each method. Pollutograph sampling consistently outperformed the other three methods both in terms of accuracy and precision, but was the most costly method evaluated. Time-paced sampling consistently underestimated while volume-paced sampling over estimated the storm EMCs. Microsampling performance approached that of pollutograph sampling at a substantial cost savings. The most efficient method for routine stormwater monitoring in terms of a balance between performance and cost was volume-paced microsampling, with variable sample pacing to ensure that the entirety of the storm was captured. Pollutograph sampling is recommended if the data are to be used for detailed analysis of runoff dynamics.

Calibration and evaluation of five indicators of benthic community condition in two California bay and estuary habitats.

Many types of indices have been developed to assess benthic invertebrate community condition, but there have been few studies evaluating the relative performance of different index approaches. Here we calibrate and compare the performance of five indices: the Benthic Response Index (BRI), Benthic Quality Index (BQI), Relative Benthic Index (RBI), River Invertebrate Prediction and Classification System (RIVPACS), and the Index of Biotic Integrity (IBI). We also examine whether index performance improves when the different indices, which rely on measurement of different properties, are used in combination. The five indices were calibrated for two geographies using 238 samples from southern California marine bays and 125 samples from polyhaline San Francisco Bay. Index performance was evaluated by comparing index assessments of 35 sites to the best professional judgment of nine benthic experts. None of the individual indices performed as well as the average expert in ranking sample condition or evaluating whether benthic assemblages exhibited evidence of disturbance. However, several index combinations outperformed the average expert. When results from both habitats were combined, two four-index combinations and a three-index combination performed best. However, performance differences among several combinations were small enough that factors such as logistics can also become a consideration in index selection.

Exposing water samples to ultraviolet light improves fluorometry for detecting human fecal contamination.

Fluorometry identifies human fecal contamination by detecting optical brighteners in environmental waters. Because optical brighteners are sensitive to sunlight, we determined if we could improve fluorometry by exposing water samples to ultraviolet (UV) light to differentiate between optical brighteners and other fluorescing organic compounds. Optical brighteners were likely present when the relative percentage difference in fluorometric value of the water before and after UV light exposure was >30% (glass cuvettes, 30 min exposure) or >15% (polymethacrylate cuvettes, 5 min exposure). In a blind study, we correctly identified the presence or absence of optical brighteners in 178 of 180 (99%) of the samples tested with a more expensive field fluorometer and in 175 of 180 (97%) of the samples tested with a less expensive handheld fluorometer. In the field, the method correctly identified two negative and three positive locations for human fecal contamination. When combined with counts of fecal bacteria, the new fluorometric method may be a simple, quick, and easy way to identify human fecal contamination in environmental waters.

A statistical appraisal of disproportional versus proportional microbial source tracking libraries.

Library-based microbial source tracking (MST) can assist in reducing or eliminating fecal pollution in waters by predicting sources of fecal-associated bacteria. Library-based MST relies on an assembly of genetic or phenotypic "fingerprints" from pollution-indicative bacteria cultivated from known sources to compare with and identify fingerprints of unknown origin. The success of the library-based approach depends on how well each source candidate is represented in the library and which statistical algorithm or matching criterion is used to match unknowns. Because known source libraries are often built based on convenience or cost, some library sources may contain more representation than others. Depending on the statistical algorithm or matching criteria, predictions may become severely biased toward classifying unknowns into the library's dominant source category. We examined prediction bias for four of the most commonly used statistical matching algorithms in library-based MST when applied to disproportionately-represented known source libraries; maximum similarity (MS), average similarity (AS), discriminant analyses (DA), and k-means nearest neighbor (k-NN). MS was particularly sensitive to disproportionate source representation. AS and DA were more robust. k-NN provided a compromise between correct prediction and sensitivity to disproportional libraries including increased matching success and stability that should be considered when matching to disproportionally-represented libraries.

Geographic sharing of ribotype patterns in Enterococcus faecalis for bacterial source tracking.

The limited host range of Enterococcus faecalis may reduce its clonal diversity and thereby increase its geographic sharing of ribotype patterns. Such sharing would be advantageous for bacterial source tracking (BST). We determined the geographic sharing of ribotype patterns in 752 Ent. faecalis isolates obtained primarily from wastewater treatment plants in Delaware (15 locations; 490 isolates), Georgia (2 locations; 48 isolates), Idaho (1 location; 118 isolates), New York (2 locations; 48 isolates), and Puerto Rico (2 locations; 48 isolates). Isolates were ribotyped with a RiboPrinter. When pooled across all locations and analyzed at a similarity index of 100% and a tolerance level of 1.00%, the 752 Ent. faecalis isolates yielded 652 different ribotypes, of which 429 (66%) were unshared. Even when the matching criterion was relaxed by decreasing the tolerance level from 1% to 10% or lowering the similarity cutoff from 100% to 90%, half or almost half of the ribotypes were unshared. A Mantel test of zero correlation showed no statistically significant correlation between ribotype patterns and geographic distance among the 32 samples (one location at one time) at either the 1.00% (P = 0.91) or 10.00% (P = 0.83) tolerance levels. Therefore, the percentage of ribotype patterns shared between two locations did not increase as the distance between locations decreased. In the case of BST, a permanent host origin database sufficiently large to encompass these ribotype patterns would be time-consuming and expensive to construct.

Assessment of statistical methods used in library-based approaches to microbial source tracking.

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.

Evaluation of polychlorinated biphenyl bioaccumulation patterns in white sea urchins (Lytechinus pictus) using multiple approaches.

The bioaccumulation of polychlorinated biphenyls (PCBs) from three amended field-contaminated sediments (with total PCB concentrations of approximately 4, 10, and 100 microg/g dry wt) by white sea urchins (Lytechinus pictus) was evaluated using multiple statistical and theoretical approaches. Similarity analysis of the PCB bioaccumulation patterns, based on the concept of ecological communities, showed that the PCB patterns in the sea urchins and source sediments were essentially identical for all three sediment concentrations. However, affinity analysis did show some preference for bioaccumulation of higher-molecular-weight and more hydrophobic congeners by the urchins. The affinity analysis also showed that within a homologous series, bioaccumulation increased with increasing hydrophobicity. The biota-sediment accumulation factor (BSAF) profiles for the two lower concentration sediments (A and B) were found to be statistically different from the high concentration sample (sediment C) by a multivariate analysis of variance (MANOVA). The relationship between the measured apparent organic carbon-normalized partition coefficients (K(OC)) and octanol-water partition coefficient (K(OW)) (log based) suggested a significant departure from thermodynamic equilibrium. A nonequilibrium, steady-state bioaccumulation model was found to correctly predict the observed experimental bioaccumulation patterns. To improve the model performance, a hydrophobic term was introduced to account for the drop-off in BSAF profiles with log K(OW) > or = 6.5. This study showed that nonequilibrium, steady-state models are far superior to equilibrium partitioning-based models for understanding the bioaccumulation of organic chemicals by sea urchins.

Comparison of national and regional sediment quality guidelines for classifying sediment toxicity in California.

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.

Development and evaluation of sediment quality guidelines based on benthic macrofauna responses.

Toxicity-based sediment quality guidelines (SQGs) are often used to assess the potential of sediment contamination to adversely affect benthic macrofauna, yet the correspondence of these guidelines to benthic community condition is poorly documented. This study compares the performance of 5 toxicity-based SQG approaches to a new benthos-based SQG approach relative to changes in benthic community condition. Four of the toxicity-based SQG approaches--effects range median, logistic regression modeling (LRM), sediment quality guideline quotient 1 (SQGQ1), and consensus--were derived in previous national studies in the United States, and one was developed as a regional variation of LRM calibrated to California data. The new benthos-based SQG approach, chemical score index, was derived from Southern California benthic community data. The chemical-specific guidelines for each approach were applied to matched chemical concentration, amphipod mortality, and benthic macrofauna abundance data for Southern California. Respective results for each SQG approach were then combined into a summary metric describing the overall contamination magnitude (e.g., mean quotient) and assessed in accordance with a set of thresholds in order to classify stations into 4 categories of expected biological effect. Results for each SQG approach were significantly correlated with changes in sediment toxicity and benthic community condition. Cumulative frequency plots and effect category thresholds for toxicity and benthic community condition were similar, indicating that both types of effect measures had similar sensitivity and specificity of response to contamination level. In terms of discriminating among multiple levels of benthic community condition, the toxicity-based SQG indices illustrated moderate capabilities, similar to those for multiple levels of toxicity. The National LRM, California LRM, and the chemical score index had the highest overall agreement with benthic categories. However, only the benthos-based chemical score index was consistently among the highest performing SQG indices for all measures of association (correlation, percent agreement, and weighted kappa) for both toxicity and benthos.

Distribution and sources of polybrominated diphenyl ethers in the Southern California Bight.

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.

Comparison and verification of bacterial water quality indicator measurement methods using ambient coastal water samples.

More than 30 laboratories routinely monitor water along southern California's beaches for bacterial indicators of fecal contamination. Data from these efforts frequently are combined and compared even though three different methods (membrane filtration (MF), multiple tube fermentation (MTF), and chromogenic substrate (CS) methods) are used. To assess data comparability and quantify variability within method and across laboratories, 26 laboratories participated in an intercalibration exercise. Each laboratory processed three replicates from eight ambient water samples employing the method or methods they routinely use for water quality monitoring. Verification analyses also were conducted on a subset of wells from the CS analysis to confirm or exclude the presence of the target organism. Enterococci results were generally comparable across methods. Confirmation revealed a 9% false positive rate and a 4% false negative rate in the CS method for enterococci, though these errors were small in the context of within- and among-laboratory variability. Fecal coliforms also were comparable across all methods, though CS underestimated the other methods by about 10%, probably because it measures only E. coli, rather than the larger fecal coliform group measured by MF and MTF. CS overestimated total coliforms relative to the other methods by several fold and was found to have a 40% false positive rate in verification. Across-laboratory variability was small relative to within- and among-method variability, but only after data entry errors were corrected. One fifth of the laboratories committed data entry errors that were much larger than any method-related errors. These errors are particularly significant because these data were submitted in a test situation where laboratories were aware they would be under increased scrutiny. Under normal circumstances, it is unlikely that these errors would have been detected and managers would have been obliged to issue beach water quality warnings.