Identifying Pollutant Sources in Tidally Mixed Systems: Case Study of

Oct 20, 2005 - California, Irvine, California, Santa Ana Regional Water. Quality Control Board ... regionally important tidal embayment in southern Ca...
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Environ. Sci. Technol. 2005, 39, 9083-9093

Identifying Pollutant Sources in Tidally Mixed Systems: Case Study of Fecal Indicator Bacteria from Marinas in Newport Bay, Southern California YOUNGSUL JEONG,† S T A N L E Y B . G R A N T , * ,† S C O T T R I T T E R , † ABHISHEK PEDNEKAR,† LINDA CANDELARIA,‡ AND CLINTON WINANT§ Henry Samueli School of Engineering, University of California, Irvine, California, Santa Ana Regional Water Quality Control Board, Riverside, California, and Scripps Institution of Oceanography, University of California, San Diego, California

This study investigates the contribution of several marinas to fecal indicator bacteria impairment in Newport Bay, a regionally important tidal embayment in southern California. Three different fecal indicator bacteria groups were assayed, including total coliform, Escherichia coli, and enterococci bacteria, all measured using the IDEXX Colilert and Enterolert system. To document temporal variability in the fecal indicator bacteria signal, water column samples (n ) 4132) were collected from two marinas over time scales ranging from hours to months. To document spatial variability of the fecal indicator bacteria signal, water column and sediment samples were collected from a number of sites (n ) 11 to 36, depending on the study) in and around the two marinas, over spatial scales ranging from meters to kilometers. To identify the dominant temporal and spatial patterns in these data a statistical approachs Empirical Orthogonal Function analysisswas utilized. Finally, to clarify the transport pathways responsible for the observed temporal and spatial patterns, fecal indicator bacteria data were compared to simultaneous measurements of tidal flow, temperature, and salinity. The results of this field effort collectively implicate runoffsboth dry weather runoff at sampling sites located near some storm drains and wet weather runoff at all sitessas a primary source of fecal indicator bacteria in the water column and subtidal sediments. The results and analysis presented here reinforce the growing body of evidence that management of fecal indicator bacteria impairment in the coastal waters of southern California will require developing long-term strategies for treating nonpoint sources of both dry weather and stormwater runoff.

* Corresponding author phone: (949) 824-7320; fax: (949) 8242541; e-mail: [email protected]. † Henry Samueli School of Engineering, University of California, Irvine. ‡ Santa Ana Regional Water Quality Control Board. § Scripps Institution of Oceanography, University of California, San Diego. 10.1021/es0482684 CCC: $30.25 Published on Web 10/20/2005

 2005 American Chemical Society

1. Introduction The development of total maximum daily load (TMDL) management plans for impaired receiving waters requires, at a minimum, knowledge of the location and magnitude of pollutant sources potentially responsible for the impairment. While this information may be obvious in cases where pollutant point sources are clearly delineated (eg., sewer outfalls), in many cases the causes of impairment, and associated pollutant transport pathways, are either unknown or poorly constrained. In this study we develop and test a field-based approach for addressing the latter situation in tidally mixed systems such as marinas, embayments, estuaries, and tidal saltwater marshes. Identification of pollution sources is particularly challenging in tidally mixed systems because the flow field is constantly changing both direction and magnitude with the tides. For such systems coastal managers are often confronted with the following fundamental but difficult-to-answer question: Is the majority of pollution coming from a specific component of the system (e.g., a particular geographical region)? At a more fundamental level, this question can be restated as follows: What are the dominant spatial and temporal patterns associated with the pollutant of concern, and how are these patterns related to specific pollutant sources and transport pathways? Here we show that these questions can be answered by implementing a carefully designed field monitoring program, and analyzing the resulting multidimensional data set using a statistical technique called Empirical Orthogonal Function (EOF) analysis. We use this approach to determine if nonpoint sources of fecal pollution originating in two recreational marinas (e.g., from illicit vessel discharges, urban runoff drains, vessel pump-out facilities) are the primary source of fecal indicator bacteria impairment in Newport Bay, a regionally important tidal embayment in southern California. The EOF approach described here is complementary to other statistical methods for analyzing coastal water quality time series data, such as analysis of variance, or ANOVAs; see Boehm and Weisberg (1) for an excellent example of the latter relative to fecal indicator bacteria measurements at beaches in southern California. ANOVA is a statistical approach for quantitatively comparing the mean and variance of different groups of data (e.g., fecal indicator bacteria measurements collected from the surf zone during spring tides versus those collected during neap tides in the example presented by Boehm and Weisberg). The EOF approach described here, on the other hand, identifies the dominant temporal patterns (or “modes”) in a spatially distributed data set, and provides a measure (or “loading”) of a mode’s strength at each sampling site. Put another way, EOF analysis is an unbiased statistical approach for identifying the dominant temporal patterns in a time series data set, and how these temporal patterns are distributed spatiallysprecisely the information needed to answer the questions posed above. The data collection and analysis described in this paper is complementary to other published methods for identifying sources of fecal pollution in coastal systems, including sourcetracking methodologies that aim to identify the primary host (e.g., human, bovine, avian, etc.) of fecal indicator pollution (2), the identification of high-risk and low-risk sites based on the co-variation of multiple fecal indicator organisms (3-5), and process studies that couple fate and transport studies with fecal indicator occurrence patterns (6-14). A theme that emerges from many of these studies is that fecal indicator VOL. 39, NO. 23, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 1. Map of Newport Bay showing the location of the Dunes and Balboa Yacht Basin (BYB) marinas. Marina, storm drain, channel, and lagoon sampling locations are designated with triangles, circles, squares, and diamonds, respectively. These stations were sampled during the studies indicated by the color key (see text for details). Abbreviations refer to the locations of a current meter (S4), the Pacific Coast Highway Bridge (PCH), and the University of California, Irvine (UCI). bacteria often survive, and in some cases regrow, in marine and freshwater sediments (8, 9, 15-20). To address the potential role that sediment regrowth and/or sequestering might play in the fecal indicator pollution patterns observed in Newport Bay, the present study includes measurements of fecal indicator bacteria in both water and subtidal (i.e., perpetually submerged) sediment samples. The paper is organized as follows. The field site and study design are described (Section 2), followed by a summary of the current velocity measurements (Section 3), fecal indicator bacteria measurements in the water column (Section 4), and the EOF analysis of the water column data (Section 5). The paper concludes with a description of the sediment data (Section 6) and a discussion of the scientific and management implications of the data and analysis presented in the paper (Section 7). This article is complementary to a companion paper in which a 32-year history of coliform measurements in Newport Bay and the neighboring surf zone are compiled and analyzed (23). This companion article examined coliform occurrence patterns over spatial scales of 1 to 10 km, and time scales of months to decades. The focus of the present article, on the other hand, is on the contribution of a few marinas to the fecal indicator problem in Newport Bay over spatial scales of 1 m to 1 km and time scales ranging from hours to months. In addition, the present article combines information on fecal indicator bacteria occurrence patterns with measurements of heat and mass transport in the Newport Bay. 9084

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2. Field Site and Study Design Newport Bay (hereafter referred to as the Bay) is the second largest estuarine embayment in southern California (Figure 1). The Bay provides a critical habitat for terrestrial and aquatic species, and is a spawning and nursery habitat for commercial and noncommercial fish species (21). The lower portion of the Bay (referred to here as Lower Bay) is a popular recreational area, and one of the largest pleasure craft harbors in the United States. The upper region (Upper Bay) is a state ecological reserve and provides refuge, foraging areas, and breeding grounds for a number of threatened and endangered species. The Pacific Coast Highway bridge (denoted PCH in Figure 1) roughly demarcates the boundary between Upper Bay and Lower Bay. Beneficial uses of the Bay are threatened by numerous sources of pollutant loading that discharge into the Bay either directly or via tributaries of its watershed. The U.S. Environmental Protection Agency and Santa Ana Regional Water Quality Control Board have adopted for the Bay TMDLs for fecal coliform, nutrients, sediment, and toxic pollutants (22). The practical objective of the present study is to assess the contribution of marinas to fecal indicator bacteria impairment in the Bay. To achieve this objective, five separate field experiments were carried out at two different marinas: the Balboa Yacht Basin (henceforth referred to as BYB) owned by the City of Newport Beach, and the Dunes Marina (henceforth referred to as Dunes) owned by the County of Orange and privately operated (Figure 1). The two marinas differ with respect to their size (BYB has

TABLE 1. Timing and General Features of the Five Marina Studies studya

timing

BYB I Dunes I BYB II Dunes II Dunes III

07/26 16:00 to 07/29 04:00, 2002 09/20 16:00 to 09/23 04:00, 2002 11/22 16:00 to 11/25 04:00, 2002 04/18 16:00 to 04/21 01:00, 2003 08/29 16:00 to 09/01 01:00, 2003

a

antecedent dry weather number period (days) of sites 67 123 6 2 29

11 11 27 36 24

BYB, Balboa Yacht Basin marina; Dunes, Dunes marina.

174 boat slips, Dunes has 450 boat slips), shape, and, presumably, tidal flushing characteristics (see Figure S1 for aerial photos of the BYB and Dunes marinas). During the set of field experiments described here the tide range (i.e., the difference between the daily high-high and low-low tides) varied from 1.7 to 2.3 m. Estimates for the flushing time scales of Upper and Lower Newport Bay vary from days to months, depending on the region of interest (e.g., Upper Bay vs Lower Bay) and assumptions employed in the flushing calculation (23, 24). Water Column Sampling and Analysis. Table 1 summarizes key features of the experimental design employed in the five studies. Altogether, 4132 water samples were collected from the BYB and Dunes marinas over a 14-month period of time, from July 2002 through September 2003. All five studies were conducted during dry weather periods; however, the elapsed time from the last storm (or antecedent dry period) varied from 2 days (for the Dunes II study) to 123 days (for the Dunes I study). All five studies had similar experimental designs, although the number of samples collected, and the spatial distribution of sampling sites, varied. Sampling sites were divided into four categories based on their location: (1) marina samples, collected from the marina being studied; (2) channel samples, collected from the channel adjacent to the marina; (3) storm drain impacted samples, collected from the marina or channel near storm drain outlets; and (4) lagoon samples, collected during the Dunes II study from a lagoon near the Dunes marina (see Figure 1). The number of sites sampled, and the sampling frequency, were arrived at after weighing several different objectives including the following. (1) To assess the reproducibility of the sample collection and analysis methods, duplicates were collected for 10% of all samples. (2) To capture tidal and day/night (diurnal) changes in the fecal indicator bacteria signal, water samples were collected every 3 h, 24 h per day, for the duration of each 2.5 day study period. (3) To assess vertical stratification of fecal indicator bacteria in the water column, paired water samples were collected from each site, one from the surface of the water column and another from 1 m below the surface. (4) To maximize the possibility of detecting illicit discharges from vessels inside the marinas, studies were conducted over the course of a weekend (from Friday afternoon through Monday morning) when boat usage was significant. Several of the studies were conducted during holiday periods (the Dunes III study was conducted over Labor Day weekend, and the Dunes II study was conducted over Easter Sunday). All samples were transported a short distance to UCI (denoted by the anteater in Figure 1) where they were analyzed for fecal indicator bacteriasincluding total coliform (TC), Escherichia coli (EC, a sub-group of fecal coliform, FC), and enterococci bacteria (ENT)susing defined substrate tests known commercially as Colilert and Enterolert, implemented in a 97-well quantitray format. This particular test was utilized because it is quantitative, relatively inexpensive, and not labor intensive. The last feature made possible the processing of

a large number of samples on a 24 h per day basis. The Colilert method, when implemented for marine waters, can yield false TC positives (25-27). However, the frequency of false TC positives does not appear to be significant relative to the background noise associated with fecal indicator assays (27). Because we utilize only the Colilert and Enterolert methods in this study (i.e., we are not mixing results from different assay methods) the results reported here are internally comparable. All water samples were also analyzed for salinity, turbidity, and pH using methods described elsewhere (28). Details of the water column sampling, including the logic used in selecting the sampling sites, can be found in the Supporting Information for this paper. Sediment Sampling and Analysis. During the Dunes II and Dunes III studies, two subtidal sediment samples were collected once daily from the same set of sites where water samples were collected every 3 h. In all, 115 sediment samples were collected and analyzed for fecal indicator bacteria, including 55 and 60 during the Dunes II and Dunes III studies, respectively. Immediately prior to sampling the sediment, a 500-mL water sample was collected from the bottom of the water column (just above the bed) using a custom ball-valve sampling system affixed to a telescoping pole. The water samples were handled and analyzed using the procedures described above. Sediment was collected in two 50-mL conical tubes (Fischer Scientific, Pittsburgh, PA) affixed to the end of a telescoping pole. The pole was lowered over the side of a small boat, and the conical tube was forced into the bottom sediments to a depth of approximately 10 cm. The pole was then raised, and if the conical tube contained sediments it was capped and immediately placed on ice. If the conical tube did not contain sediments (e.g., due to wash-out of the sediments as the tube was raised through the water column), the entire procedure was repeated. This method worked well at most sites because of the soft (i.e., fine grained and organic rich) nature of bottom sediments in the Bay. Sediment samples were analyzed at UCI using a procedure similar to the one described by Craig et al. (29). Specifically, sediment (25 g wet weight) was added to a 500-mL centrifuge bottle (Kendro Laboratories, Asheville, NC), resuspended in 75 mL of 0.1% Peptone (Difco, Sparks, MD), hand shaken for 1 min, and then centrifuged at 500 rpm for 10 min at 4-10 °C using a GS 3 rotor in a Sorvall RC28S Hybrid centrifuge (Dupont, Willmington, DE). The centrifuge bottles were recovered, and 10 mL of the supernatant was collected and analyzed for fecal indicator bacteria using the Colilert and Enterolert defined substrate tests, as described above. The sediment fecal indicator bacteria concentration Cs was calculated from the following formula:

Cs )

Ct75mL × 100 (units of most probable number Wsr (MPN)/100 g dry sediment) (1)

where Ct is the concentration of fecal indicator bacteria measured in the 10 mL of supernatant, Ws is the wet weight (in grams) of sediment resuspended in the 0.1% peptone, and r is the dry-to-wet weight ratio for the sediment. To compute r, approximately 2 g of wet sediment from each sediment sample was weighed out, dried overnight in an oven at 110 °C, and re-weighed. Current Meter Deployment. To characterize the predominant tidal flow in the BYB and Dunes marina study areas, a multidirectional current meter with an integrated pressure transducer, thermister, and conductivity meter (S4, InterOcean Scientific) was deployed coincident with the BYB and Dunes sampling periods, with the exception of the Dunes III study for which the S4 instrument was not available. The S4 was anchored by Orange County Sheriff Harbor Patrol VOL. 39, NO. 23, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 2. Time series plots of water level (top curve in each panel), salinity (second curve), temperature (third curve), easterly component of the current velocity (fourth curve), and the vertical velocity (fifth curve) measured during the BYB I, Dunes I, BYB II, and Dunes II studies. MSL and PSU are abbreviations for mean sea level and practical salinity units. S4 data were not collected during the Dunes III study. divers to a bottom-mounted mooring which positioned the instrument roughly 50 cm above the channel bottom. During the BYB studies, the mooring was placed along the main channel east of BYB marina where the bed elevation was roughly -3.8 m MSL; during the Dunes studies the mooring was placed along the main channel west of Dunes marina where the bed elevation is roughly -4.4 m MSL. Deployment locations are noted in Figure 1 (designated as “S4”). During each deployment, the S4 was programmed to sample every 10 min for the duration of the study (57-60 h). EOF Analysis of the Water Column Data. EOF analysis of the water quality data involved the following steps (30-34). (1) Organization of the data into a data matrix, Cij, with i and j corresponding to sampling sites and sampling times. Entries in the data matrix represent the concentrations of a particular analyte (i.e., salinity, pH, turbidity, or log-transformed concentrations of fecal indicator bacteria); all concentration values in the data matrix are the average of measurements on samples collected from the surface and 1 m below the surface. (2) Preparation of a de-meaned data matrix, D ) [dij] ) (Cij - C h i)/ri, where C h i represents the mean of all concentration measurements at the ith station, and ri () σi) is the standard deviation of all concentration measurements at the ith station. (3) Decomposition of the demeaned data matrix into a series of EOF modes and associated loadings. The modes are ordered such that the first mode captures the most variance in the de-meaned data set, the 9086

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second captures the next most data variance, and so on. The magnitude of the eigenvalue λk denotes the fraction of variance captured by the kth mode. To make our analysis tractable, we adopted the following rules: (1) we studied in detail the first EOF mode, provided that it captured approximately 50% (or more) of the variance in the de-meaned data set; and (2) we studied in detail the second EOF mode provided that it captured approximately 50% (or more) of the remaining variance (i.e., 50% of the variance not captured by the first mode). The choice of a 50% cutoff is somewhat arbitrary, although experience with other applications of EOF has shown that it is a reasonable starting point for the physical interpretation of EOF modes (32). See Supporting Information for the mathematical details of the EOF mode calculation.

3. Observations of Tidal Currents, Temperature, and Salinity Time series plots of water level, salinity, temperature, easterly component of the current velocity, and the vertical velocity (computed by taking the time derivative of the water level measurements) are presented in Figure 2. These data were recorded by an S4 instrument deployed in the channel outside of the BYB marina (during the BYB I and BYB II studies), and in the channel outside of the Dunes marina (during the Dunes I and Dunes II studies) (see Section 2). Tidal exchange of water between the ocean and the Bay causes the water level at the two study sites to rise and fall with the tides. In general,

FIGURE 3. Geometric mean of fecal indicator bacteria and mean salinity measured during the five marina studies. the water level data measured by the S4 instrument have a mixed-tide character; i.e., there are four distinct high and low tides per day, including higher-high, lower-high, higherlow, and lower-low tides. During the Dunes I study, the higher-high and lower-high tides are about the same, as are the higher-low and lower-low tides. Currents recorded by the S4 instrument are strongly forced by the tides, with inland-directed flow coincident with rising tides (positive vertical velocity) and oceanward-directed flow coincident with falling tides (negative vertical velocity). Because of the sinuous geography of Newport Bay, inlanddirected flow manifests as westward flow at the BYB site and eastward flow at the Dunes site. The peak velocities recorded by the S4 are approximately 10 and 50 cm/s in the channels outside of the BYB and Dunes marinas, respectively. When averaged over all flood and ebb tides, the absolute magnitude of the tidal velocities are in the range of 4-5 and 17-22 cm/s for the BYB and Dunes studies, respectively. Temperature and salinity recorded by the S4 instrument in the channel outside of the Dunes marina exhibit tidal cycling, with salinity increasing (decreasing) and temperature decreasing (increasing) during rising (falling) tides (upper right and lower right panels, Figure 2). Tidal cycling of

temperature and salinity is not evident in the channel outside of the BYB marina during the BYB I study (upper left panel, Figure 2), although temperature and salinity do exhibit some tidal (diurnal) cycling in the BYB II study (lower-left panel). During the BYB I study, temperature and salinity changed slowly over the two-day sampling period, perhaps reflecting the slow intrusion of warmer and lower salinity water from Upper Bay. Overall, these data reveal that tidal currents strongly influence heat and mass transport in the channel outside of the Dunes marina; the influence of tidal currents is less obvious in the channel outside of the BYB marina. More generally, the oscillating nature of the currents measured here, in which the direction of flow reverses every ca. 6 h, implies that water quality impairment at the two marinas can be affected by a myriad of potential pollutant sources located within, inland, and oceanward of the BYB and Dunes sites.

4. Observations of Fecal Indicator Pollution in the Water Column Vertical Stratification. For all practical purposes, fecal indicator bacteria pollution in Newport Bay is mixed down to a depth of at least 1 m (Table S1). In the two cases where VOL. 39, NO. 23, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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TABLE 2. Eigenvalues Computed from an EOF Analysis of Physical and Fecal Indicator Bacteria Dataa TC

EC

ENT

salinity

pH

TUBb

λ1 λ2 λ3 ∑iN)4λi

77 7 (28) 4 (19) 12 (53)

BYB I (N ) 11) 47 42 90 15 (28) 18 (31) 3 (29) 13 (24) 13 (22) 2 (22) 25 (48) 27 (47) 5 (51)

85 7 (47) 3 (19) 5 (34)

40 33 (55) 8 (13) 19 (32)

λ1 λ2 λ3 ∑iN)4λi

56 18 (40) 8 (17) 18 (43)

Dunes I (N ) 11) 20 41 58 19 (23) 16 (27) 17 (40) 15 (19) 13 (22) 9 (21) 46 (58) 30 (51) 16 (39)

64 17 (47) 9 (23) 10 (30)

27 22 (30) 13 (17) 38 (53)

λ1 λ2 λ3 ∑iN)4λi

26 23 (31) 11 (14) 40 (55)

BYB II (N ) 27) 17 26 42 14 (17) 16 (22) 17 (30) 11 (13) 13 (18) 11 (18) 58 (70) 45 (60) 30 (52)

50 15 (31) 9 (17) 26 (52)

35 18 (27) 9 (14) 38 (59)

λ1 λ2 λ3 ∑iN)4λi

65 9 (25) 6 (17) 20 (58)

Dunes II (N ) 36) 32 61 84 11 (16) 8 (19) 7 (47) 8 (12) 7 (18) 2 (15) 49 (72) 24 (63) 7 (38)

75 8 (3l) 4 (15) 13 (54)

30 16 (22) 12 (17) 42 (61)

λ1 λ2 λ3 ∑iN)4λi

83 5 (28) 3 (16) 9 (56)

Dunes III (N ) 24) 78 21 49 4 (18) 15 (19) 18 (36) 4 (16) 12 (14) 11 (21) 14 (66) 52 (67) 22 (43)

61 9 (22) 7 (18) 23 (60)

29 18 (25) 13 (18) 40 (57)

a Entries represent the percentage of data variance captured by the first and higher-order EOF modes. Entries in parentheses represent the percentage of the remaining variance not captured by the first EOF mode. Entries in bold indicate eigenvalues that capture over 40% of the de-meaned data variance. b TUB, turbidity.

FIGURE 4. Spearman Rank correlation coefficients between fecal indicator bacteria and salinity. Sampling stations with statistically significant correlations (p e 0.05) are designated with a cross (+). a significant difference is evident (based on a KrusakalWallis test, p < 0.01) between the median concentration of fecal indicator bacteria at the surface and 1 m below the surface, the difference is small (factor of two in the case of TC measured during the BYB I study, and approximately 10% in the case of EC measured during the BYB II study). Site-to-Site and Study-to-Study Variability. The singlesample exceedences and geometric means of fecal indicator bacteria in the water column are nearly the same across site categories (i.e., Marina, Channel, Storm Drain Impacted, and Lagoon) (Figure 3 and Table S2). Exceptions include the following: (1) during the BYB II study TC and ENT were elevated in samples collected adjacent to storm drains, particularly along the west wall of the marina (third row of color panels in Figure 3), and (2) during the Dunes II study ENT was elevated in samples collected at some channel sites and along the eastern edge of the lagoon (fourth row of color panels in Figure 3). If the marinas were the primary source of contamination in Newport Bay, the concentration of fecal indicator bacteria should be higher in the marinas and lower in the channels, contrary to the trends reported here. While the geometric mean of fecal indicator bacteria generally exhibits little site-to-site variability, significant study-to-study variability is evident. The highest singlesample exceedence rates and geometric means occurred during the Dunes II study; 10% of all samples exceeded the single-sample standard for ENT during this particular study (Table S2). The Dunes II study had the shortest antecedent 9088

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dry period (2 days, Table 1), and the lowest recorded salinity and pH values (Table S2 and Figure 3). Collectively, these observations suggest a potential connection between surface water runoff (which has lower salinity and pH, compared to ocean water) and fecal indicator bacteria impairment in Newport Bay. Correlations between Fecal Indicator Bacteria, Salinity, and Turbidity. To explore the potential link between runoff and fecal indicator bacteria, Spearman Rank correlation coefficients (a nonparametric measure of correlation (35), abbreviated here as “Sp”) were computed between fecal indicator bacteria and salinity (Figure 4). Fecal indicator bacteria concentrations in the water column were strongly negatively correlated with salinity during the Dunes II study (fourth row of panels, Figure 4). An examination of the raw data collected during the Dunes II study (see Figures S4-S7, Supporting Information) reveals that salinity decreases, and fecal indicator bacteria concentration increases, during ebb tides, when water from Upper Bay flows past the Dunes marina on its way to Lower Bay. This observation is consistent with the idea that, during the Dunes II study, most of the fecal indicator pollution originated from sources of freshwater runoff flowing into Upper Bay, probably from the San Diego Creek and/or Santa Ana Delhi Channel (Figure 1). The observed tidal cycling of fecal indicator bacteria concentrations during the Dunes II study also suggest that a relatively small fraction of water flowing from Upper Bay to Lower Bay during ebb tides is returned to Upper Bay during the following flood tide. If fecal indicator bacteria were sloshing back and forth past the Dunes site with the tides, then the concentration of fecal indicator bacteria should have been high during both the ebb and flood phases of the tide, contrary to observations. Weaker negative correlations between fecal indicator bacteria and salinity are also evident in several other cases, including the following: (1) storm drain sites during the BYB

FIGURE 5. Time series plots of EOF modes calculated from physical (pH, salinity) and fecal indicator bacteria data; only EOF modes for which the first eigenvalue is approximately 50% (or more) are shown in the figure. The black curves represent measured (or computed in the case of Dunes III study) water level. The red, blue, and green curves represent the raw pH, salinity, and log-transformed fecal indicator bacteria data (designated pH, SAL, TC, EC, and ENT). The purple line represent the first EOF mode computed from the de-meaned pH (pH_mode1), salinity (SAL_mode1), and log-transformed TC (TC_mode1), EC (EC_mode1), and ENT (ENT_mode1) data. II study (third row of panels, Figure 4), and (2) TC concentrations measured at all sites during the BYB I and Dunes III studies (first and last row of panels, Figure 4). While the negative correlation between fecal indicator bacteria and salinitysobserved at many sites in both the BYB and Dunes field areassis consistent with a runoff source for these organisms, this negative correlation could also be attributed to more rapid die-off of fecal indicator bacteria with increasing salinity (36). Indeed, across all five studies (i.e., BYB I, Dunes I, BYB II, Dunes II, and Dunes III) the negative correlation between salinity and fecal indicator bacteria is most pronounced for TC (-0.87 < Sp < 0.20), intermediate for ENT (-0.69 < Sp < 0.35), and weakest for EC (-0.53 < Sp < -0.33) (p e 0.05). The pronounced negative correlation between salinity and TC is consistent with the known sensitivity of this fecal indicator bacteria group to high (i.e., near ocean) salinity (37). Finally, we note that there were no consistent relationships between the concentration of fecal indicator bacteria and turbidity (Figure S3, Supporting Information). During the Dunes II study, TC and turbidity were positively correlated (0.33 < Sp < 0.63, p e 0.05) at many sites (n ) 23), whereas EC and ENT were positively correlated with turbidity (0.35 < Sp < 0.69, p e 0.05) at fewer sites (n ) 8 and 10 for EC and ENT, respectively). During the other studies (i.e., BYB I, BYB

II, Dunes I, and Dunes III) relatively few sites exhibited a significant (at the p ) 0.05 level) correlation between turbidity and the concentration of fecal indicator bacteria.

5. EOF Analysis of Water Column Data EOF Modes. As mentioned in the Introduction, the goal of the EOF approach is to identify the dominant temporal patterns (referred to here as “modes”) in time series data, and then to quantitatively determine how these modes are distributed spatially, by examining the spatial distribution of “loadings” associated with each mode. EOF analyses was carried out for the complete set of analytes measured in the five different water column studies (i.e., BYB I, Dunes I, BYB II, Dunes II, and Dunes III). In general, there are as many modes as there are sampling stations, but most of the higher-order modes capture very little variance in the original data set. The modes are ordered according to the percentage of variance captured by each. The first mode captures the most data variance, the second mode captures the next most variance, and so on. Importantly, the magnitude of the eigenvalue associated with each mode, when multiplied by a factor of 100, indicates the percentage of data variance captured by each mode. The EOF eigenvalues computed from the time series measurements of pH, salinity, turbidity, and log-transformed fecal indicator bacteria conVOL. 39, NO. 23, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 6. Color contour plots of EOF loadings calculated from physical (pH, salinity) and log-transformed fecal indicator bacteria data; only EOF modes for which the first eigenvalue is approximately 50% (or more) are shown in this figure. centrations are presented in Table 2. In many cases the first mode accounts for a very large percentage of the data variance (i.e., λ1 ranges up to 90%). For the purposes of this study, we chose to focus only on those EOF modes that captured approximately 50% (or more) of the data variance, indicated in Table 2 with bold type (see Section 2 and Supporting Information for more details). The temporal patterns associated with these specific EOF modes are presented in Figure 5, and their associated spatial loadings are presented in Figure 6. Time series plots of the first EOF mode are organized by study (columns in Figure 5) and analyte (rows in Figure 5). Two plots are included for each analyte. The first is a time series plot of the raw data, where the colored curves denote station categories (see color key in the figure). The second is a time series plot of the first EOF mode (purple curve). Comparing these two plots, we find that the first EOF mode generally captures the trend evident in the raw time series data. During the three Dunes studies, for example, all of the first EOF modes plotted in Figure 5 exhibit significant diurnal (once per day) or semi-diurnal (twice per day) cycling, although the cycling is not necessarily consistent across the different analytes for a fixed study, or across all studies for a fixed analyte. For example, during the Dunes I study, the first pH mode exhibits a diurnal pattern (i.e., it peaks once per day), whereas the first salinity mode exhibits a semidiurnal pattern (i.e., it peaks twice per day). Table 3 summarizes the different cycling patterns observed for the first EOF modes. On the basis of the results presented in Figure 5 and Table 3, fecal indicator bacteria measurements at the Dunes site often exhibit diurnal or semidiurnal variability. In contrast, fecal indicator bacteria measurements at the BYB site either exhibit no pattern (NA 9090

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TABLE 3. Patterns Revealed by First EOF Modea h (MSL) pH SAL TC EC ENT

BYB I

Dunes I

BYB II

Dunes II

Dunes III

WSD WSD LF LF WD WD

SD D, LF SD D NA D

WSD

WSD WSD, LF D WSD, LF NA D

SD D, LF SD SD, LF SD, LF NA

D NA NA NA

a NA, not applicable because λ < 50% (see Table S3); LF, low 1 frequency; WSD, weak semi-diurnal; SD, semi-diurnal; D, diurnal; WD, weak diurnal.

in Table 3) or a low frequency pattern (LF in Table 3), where the latter refers to an increase or decrease in concentration over a multi-day period. The semi-diurnal and diurnal cycling observed at the Dunes site is most likely driven by tidal transport processes, given the earlier observation that both heat and mass transport are strongly forced by tides at this site (Section 3). Sunlight- and/or salinity-induced mortality of bacteria in the water column may also contribute to the diurnal cycling of fecal indicator bacteria concentrations (12). Finally, it should be noted that the first EOF modes calculated from the fecal indicator bacteria data are generally negatively correlated with the first EOF modes calculated from pH and salinity (Sp ) -0.71 to -0.15). This last result is consistent with the idea that runoff is a significant source of these contaminants (see discussion in Section 4, and Figure 4). EOF Loadings. The EOF loadings are relatively uniform (i.e., constant) across all of the sampling sites (Figure 6). This result implies that the temporal variability captured by the

FIGURE 7. Spatial distribution of fecal indicator bacteria in the sediment during Dunes II (first column) and Dunes III (second column) studies, and in the water just above the bed sediment during the Dunes II (third column) and Dunes III (fourth column) studies. first EOF mode is relatively homogeneous across all sampling sites; i.e., if the concentrations are rising in one part of the sampling grid, they are rising in the rest of the grid as well.

6. Observations of Fecal Indicator Bacteria in the Sediment The concentration of fecal indicator bacteria in the sediment exhibits significant study-to-study variability (at least for the two studies represented here), and relatively less site-to-site variability (Figure 7 and Table S3; see also Figure S8). Concentrations of fecal indicator bacteria in the sediment were at least 1 order of magnitude higher during the Dunes II study (geometric means in the range 103-104 for TC, 101102 for EC, and 102-103 for ENT, all in MPN/100 g of dry sediment), compared to the Dunes III study (geometric means in the range 100-101 for TC and EC, and 101-102 for ENT, all in MPN/100 g dry sediment) (Figure 7 and Table S3). In contrast, the concentration of fecal indicator bacteria in the sediment is relatively constant across sites, with slightly higher concentrations of TC, and lower concentrations of EC, in the lagoon sediments during the Dunes II study (first column of panels in Figure 7, Table S3). There are no obvious relationships between the concentration of fecal indicator bacteria measured in the sediments and the concentration of fecal indicator bacteria measured in the water column just above the sediment bed (third and fourth columns of panels in Figure 7, Table S3). In particular, there are many

examples where the concentrations of fecal indicator bacteria in the sediment are relatively high, but the concentrations of fecal indicator bacteria in the water above the sediment bed are relatively low; the opposite pattern (i.e., sediment concentrations low, above-bed concentrations high) is also apparent in Figure 7.

7. Data Synthesis and Practical Implications This paper sheds light on the spatial and temporal patterns of fecal indicator bacteria concentrations in the water column and sub-tidal sediments of Newport Bay; and on the sources and transport pathways that give rise to these patterns. Water column concentrations of fecal indicator bacteria exhibit significant temporal variability at multiple time scales, including the following: (1) significant studyto-study variability, with the highest concentrations observed for studies with the shortest antecedent dry period (e.g., Dunes II study); (2) semi-diurnal and diurnal variability, to a greater extent at the Dunes site and to a lesser extent at the BYB site; and (3) low-frequency variability as evidenced by an increase or decrease of fecal indicator bacteria concentrations over the course of multi-day field experiments. The EOF analysis indicates that the variability captured by (2) and (3) is fairly homogeneous across the sampling grids; in other words, if the concentration of fecal indicator bacteria is rising at one site in the grid, it tends to rise at all other sites as well. Sediment concentrations VOL. 39, NO. 23, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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also exhibited modest site-to-site variability but significant study-to-study variability, consistent with the water column results. Collectively, these results are not consistent with the ideas which motivated this studysthat fecal indicator bacteria originate primarily from sources (e.g., illicit vessel discharges, runoff, leaking pump-out facilities, etc.) located within the BYB and Dunes marinas. Instead, the data point to runoff (from local storm drains and from creeks draining into Upper Bay), and perhaps regrowth of environmentally adapted strains of fecal indicator bacteria (38), as sources of fecal indicator bacteria in both the water column and sediments. Once fecal indicator bacteria enter the water column, they are transported laterally by the tides (to a greater or lesser extent, depending on location), and transferred vertically in to and out of the sediments. To the extent that fecal indicator bacteria in the sediments survive, and perhaps replicate, contaminated sediment could serve as a long-term nonpoint source of water column contamination by, for example, tidal resuspension (11). However, the fact that the concentration of fecal indicator bacteria in the sediments was significantly lower during the Dunes III study (antecedent dry period 29 days) compared to the Dunes II study (antecedent dry period 2 days), suggests that sub-tidal sediments are not a permanent high-level source of fecal indicator bacteria pollution in Newport Bay. An issue not explored in this study, but of significant interest, is the possibility that sediments in the intertidal zone support the regrowth of fecal indicator bacteria, as has been suggested for other geographical settings (8, 9).

Acknowledgments This research was supported by a grant from the University of California Marine Council grant (UCMarine-32114), the Santa Ana Regional Water Quality Control Board, and the City of Newport Beach. Special thanks to Ryan Reeves and Harmony Gates who coordinated the complex field data collection effort, and to Brett Sanders at UCI who collected the current meter data included in this study. We also thank the many undergraduate and graduate students who participated in the collection and analysis of the samples.

Supporting Information Available Sampling and analysis procedures, empirical orthogonal function calculation procedures, and additional tables and figures. This material is available free of charge via the Internet at http://pubs.acs.org.

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Received for review November 5, 2004. Revised manuscript received August 26, 2005. Accepted August 26, 2005. ES0482684

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