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Determining Hot Spots of Fecal Contamination in a Tropical Watershed by Combining Land-Use Information and Meteorological Data with Source-Specific Assays Justin R. Jent,† Hodon Ryu,‡ Carlos Toledo-Hernández,§ Jorge W. Santo Domingo,*,‡ and Lilit Yeghiazarian*,† †

School of Energy, Environmental, Biological & Medical Engineering, University of Cincinnati, Cincinnati, Ohio 45221, United States Office of Research and Development, U.S. Environmental Protection Agency, Cincinnati, Ohio 45248, United States § Department of Biology, University of Puerto Rico, San Juan, Puerto Rico ‡

S Supporting Information *

ABSTRACT: The objective of this study was to combine knowledge of environmental, topographical, meteorological, and anthropologic factors in the Rió Grande de Arecibo (RGA) watershed in Puerto Rico with information provided by microbial source tracking (MST) to map hot spots (i.e., likely sources) of fecal contamination. Water samples were tested for the presence of human and bovine fecal contamination in addition to fecal indicator bacteria and correlated against several land uses and the density of septic tanks, sewers, and latrines. Specifically, human sources were positively correlated with developed (r = 0.68), barren land uses (r = 0.84), density of septic tanks (r = 0.78), slope (r = 0.63), and the proximity to wastewater treatment plants (WWTPs) (r = 0.82). Agricultural land, the number of upstream National Pollution Discharge Elimination System (NPDES) facilities, and density of latrines were positively associated with the bovine marker (r = 0.71; r = 0.74; and r = 0.68, respectively). Using this information, we provided a hot spot map, which shows areas that should be closely monitored for fecal contamination in the RGA watershed. The results indicated that additional bovine assays are needed in tropical regions. We concluded that meteorological, topographical, anthropogenic, and land cover data are needed to evaluate and verify the performance of MST assays and, therefore, to identify important sources of fecal contamination in environmental waters.

1. INTRODUCTION Fecal contamination is one of the leading causes of surface water impairment in the United States.1 In order to establish adequate remediation practices it is necessary to identify (1) the primary sources of fecal contamination (e.g., human, cattle, swine, etc.) impacting the water body and (2) the geographic location of these sources. Answering either of these questions is not trivial. Answering the question of host identification requires the use of host-specific assays, a suite of techniques collectively known as microbial source tracking (MST).2−4 MST is poised to provide more information than the traditional methods that use fecal-indicator bacteria (FIB), such as fecal coliforms (in freshwater) and enterococci (in marine and freshwater).5 A major drawback of traditional methods is that they cannot be used to identify the specific hosts because these indicators are present in most host types.6 Identification of the exact location of the fecal source is difficult because sources are often geographically dispersed, such as for instance migrating wildlife, free-range (unfenced) livestock, and faulty septic tanks. In fact, nonpoint sources of fecal pollution are a major contributor to water quality impairement.2 In order to identify the location one has to include certain environmental factors that play a key role in © XXXX American Chemical Society

microbial transport such as topography, soil properties, and precipitation amount and intensity. Because fecal contamination of surface waters is a manifestation of the interplay between many environmental processes and factors, fecal contamination data alone are not sufficient, and a systemwide perspective is needed. Several studies have employed integrated data analysis to investigate the sources of fecal contamination.7−11 Most of these studies, however, were conducted in temperate climates, with results that may be hard to extrapolate to the tropics. Several studies4,12,13 have stressed the need of better evaluation of the performance of assays targeting bacterial indicators of fecal pollution and markers used in MST studies (e.g., CF128, HF183, PF163). For example, Fujioka et al.14 suggested that monitoring tropical streams for fecal coliform, E. coli, and enterococci may not result in an adequate assessment of human health risk since the soils in tropical environments (Hawaii and Guam) provide favorable growth conditions for Received: January 21, 2013 Revised: April 15, 2013 Accepted: April 16, 2013

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dx.doi.org/10.1021/es304066z | Environ. Sci. Technol. XXXX, XXX, XXX−XXX

Environmental Science & Technology

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in addition to the public health concerns, there is a significant negative economic impact due to water contamination. Water flow begins in the central mountainous region of the RGA watershed at an elevation of 1200 m (mean sea level) and continues northward to the Atlantic Ocean. Karst formations are present in the upper reaches, which allows surface water to seep underground through sinkholes and travel through underground fissures. Several lakes and reservoirs are scattered throughout the watershed; the largest ones being Lago Dos Bocas, Lago Guayo, and Lago Caonillas. Approximately 11 km upstream from the coast, the river spreads to a 4-km wide alluvial floodplain.21 Predominant land uses include forest reserves, coffee plantations, and minor crops such as beans, plantains, and citrus. Urban development is mainly confined to municipalities of Adjuntas, Utuado, and Jayuya. Most of the population in the RGA watershed is located in the coastal alluvial plain near the municipality of Arecibo.22 The upper watershed is mostly forested, undeveloped land. The climate in Puerto Rico varies due to its topography. Climate varies from subtropical at the higher elevations near the headwaters to tropical in the lower elevations. Due to its proximity to the equator, the temperature varies little throughout the year. Between the months of May and November rainfall can either be scarce, or flood-producing due to disturbances in the east-to-west trade winds.23 From November to April significant amounts of rainfall can occur with monthly averages ranging from 78 to 180.6 mm for Arecibo.24 Elevation also had a significant effect on the amount of precipitation. Daly et al.23 found a 140% increase in precipitation with every kilometer of elevation in Puerto Rico. Nine sites were sampled biweekly for 13 months. The sites were chosen based on the ease of access and for the presumed presence of human and cattle fecal sources (Figure S1of the Supporting Information). Three sites (4, 7, and 9) were located downstream of a wastewater treatment plant (WWTP) for the municipalities of Adjuntas, Utuado, and Jayuya, respectively. Sites 1 and 2 were located near the Guilarte National Forest and were considered low-impact sampling sites since no domesticated animals (e.g., cattle, swine, poultry) are located near these sites and very few humans populate the surrounding area. Site 3 was located on the Cidras river before the WWTP within the municipality of Adjuntas. Sampling site 5 was located before the WWTP at the start of the RGA. Site 6 was located within the municipality of Utuado. Sampling site 8 is located at the mouth of the watershed right before the river drains into the Atlantic Ocean. 2.2. Sample Collection and Analysis. Sample collection and data analyses are fully described in the work of ToledoHernández et al.15 Briefly, sampling began on October 30, 2009, and continued biweekly until December 22, 2010. A total of 54 samples were collected for each of the nine sampling sites throughout the initial study period. Samples were collected in the same day and were transported to the laboratory on ice within a 6-h time frame. All the samples were filtered onto polycarbonate membranes (0.4-μm pore size, 47-mm diameter; GE Water and Process Technologies, Trevose, PA) in the Microbiology Laboratory at the University of Puerto Rico−Rió Piedras Campus. Membranes were placed in microcentrifuge tubes and sent to the USEPA laboratory (Cincinnati, OH) for DNA extractions. DNA was extracted from each filter as described by Ryu et al.,25 and aliquots (2 μL) of the DNA extracts were used as a template in conventional and quantitative polymerase chain reaction (PCR) assays. To

FIB, creating secondary sources of these bacteria unrelated to fecal sources that can impact nearby waters. This makes the task of identifying fecal sources in the tropics challenging. Unfortunately, the performance of MST assays in tropical environments is not well-documented. Recently, ToledoHernández et al.15 collected water samples during a 13month period in the Rió Grande de Arecibo (RGA) watershed in Puerto Rico and used several source tracking markers to identify the primary sources of fecal pollution in the watershed. The results from this study indicated that humans and cattle were the most important sources of pollution, which is in agreement with the presence of wastewater treatment plants and cattle in the most polluted sites. However, the occurrence of the human and cattle markers was lower than expected, suggesting that additional assays may be needed to better determine fecal loads associated with each of these sources. Another recent study by Santiago-Rodriguez et al.16 incorporated rainfall events and MST data in the RGA watershed. While these studies were some of the first MST studies in a tropical region, important geographic and anthropologic variables such as topography, soils, land cover, and land management were not considered. There are many studies7,10,17−19 on the occurrence and dynamics of fecal bacteria and MST markers in temperate regions. However, to our knowledge there is scarce data that characterize the impact of tropical climate and landscape on the occurrence of MST bacterial genetic markers. As a result, it is difficult to assess when, and to what extent, factors such as landscape, precipitation, and solar irradiation have a different impact on the performance of MST assays and on the overall survival of targeted fecal bacteria in tropical settings than in temperate regions. To address these issues, we integrated meteorological, land cover, soil, and anthropological information with MST bacterial assays that were first evaluated by Toledo-Hernández et al.15 at different sites within the RGA watershed. The main objective of our study was to determine the importance of environmental, topographical, meteorological, and anthropologic factors on the occurrence of MST bacterial markers in tropical waters, using as a model the RGA watershed. While we do not consider fate and transport directly, we associated variables that produce runoff (e.g., precipitation, land cover, slope, and soil) with fecal pollution. We also compared descriptive statistics with other studies conducted in temperate regions and mapped the fecal contamination hot spots to show future locations for sampling. This strategy is likely to reduce the uncertainty associated with locating microbial contamination sources, which, in turn, leads to more effective development and implementation of management practices used to reduce microbial loads.

2. METHODS 2.1. Study Area and Sample Sites. The RGA watershed is located in the western-central part of Puerto Rico and has a catchment area of approximately 616 km2 (Figure S1 of the Supporting Information). We selected the RGA watershed since it was identified as impaired in a recent total maximum daily load (TMDL) study.20 In this watershed fecal pollution can result from multiple point sources, such as leaking septic and sewer systems, discharge from wastewater treatment plants (WWTPs), and nonpoint sources associated with agricultural activities. The microbial water quality of the RGA watershed is a major concern as it is an important drinking water reservoir and some of its sections are used in recreational activities. Thus, B



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Figure 1. Heatmap for human and bovine presence across time and sampling sites for the RGA watershed. This figure can be compared across both time and sampling site.

determine fecal pollution levels, we targeted Enterococcus spp. as the general FIB group using the Enterol assay.26,27 The HF183 assay was used to amplify human-specific Bacteroidetes, while the CF128 assay was used to selectively amplify ruminantspecific Bacteroidetes.12 These assays were chosen based on the knowledge of the area and presumed sources of contamination. The Enterol assay was used in quantitative PCR producing continuous data, while the HF183 and CF128 were used as conventional PCR assays producing presence/absence data. 2.3. Spatial Analysis. Slope, hydraulic conductivity, land use, and rainfall intensity play key roles in the fate and transport of bacteria in the environment since bacteria are transported in subsurface, overland, and open channel flow.28 In this analysis, we used GIS to calculate the land area contributing runoff to each sampling location, and associated the fecal contamination with slope, saturated hydraulic conductivity, land uses, and several other explanatory variables within the contributing area. A digital elevation model along with hydrography data was downloaded from the National Hydrography Data set Plus (NHDPlus) http://www.horizon-systems.com/nhdplus/. The NHDPlus is an integrated suite of application-ready geospatial data that incorporates the National Hydrography Data set with the National Elevation Data set and Watershed Boundary Data set. Following the procedure outlined in Kang et al.,8 sub-basins were delineated for each of the sampling sites. Each sampling site was used as an outlet point for the delineation. Sampling sites that were upstream of another sampling site were nested in the downstream sampling basin. Land cover information was downloaded from the United States Geological Survey (USGS) seamless server (http:// seamless.usgs.gov/) and included percent canopy cover, percent impervious area, and land use. The land use map was reclassified into seven categories: open water, developed, barren land, forested, grassland, agricultural, and wetlands. The SSURGO soils data for Puerto Rico was obtained from the National Resources Conservation Service http://soils.usda.gov/ survey/geography/ssurgo/. For precipitation estimates, hourly NEXRAD level III gridded data were obtained from the

National Weather Service. Solar irradiance was calculated using the GRASS GIS29 module r.sun.30 An estimate of the population using septic, sewer, and latrines within each sampling sub-basin was taken from a TMDL study performed by the USEPA.20 We then divided the population by the area of the sub-basin to obtain a septic, sewer, and latrine density. Additionally, the number of National Pollution Discharge Elimination System (NPDES) permitted facilities within each sampling sub-basin was obtained from the TMDL study.20 Each variable of interest, which included elevation, slope, land use, saturated hydraulic conductivity, precipitation, Shreve stream magnitude, the number of NPDES-permitted facilities, and the density of septic tanks, latrines, and sewers were allocated to each sampling site’s sub-basin (Tables S1 and S2 of the Supporting Information). The average Enterococcus spp. (Enterol) concentrations were calculated for each sampling sub-basin, as well as the percent positive for the human (HF183) and bovine (CF128) marker. 2.4. Separation of Wet and Dry Sampling Events. Since it is known that bacteria are transported with surface water in the rainfall-runoff process and that, during intense rainfall events and increased runoff, fecal contamination is often highest,28 it is necessary to separate the analysis into wet and dry sampling events.31 Without this separation, correlations would be misrepresented. This effect is common in data analysis and is known as Simpson’s paradox.31 The dry and wet sampling events were determined by summing the precipitation over a three-day period for each sampling site, as follows: hourly NEXRAD level III gridded precipitation data were aggregated to a daily rainfall total from 00 to 23 UTC for each sampling sub-basin. The time of day for sampling was not available; therefore, only a daily average could be used. If more than 2 mm of rain fell between the day of sampling and the previous 2-day period, the sampling date was classified as wet.8 There were a total of 41 wet sampling days and 13 dry sampling days. Table S3 (Supporting Information) provides a statistical summary for each of the sampling sub-basins for Enterol values, C



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Table 1. Correlations with Precipitation and Solar Irradiancea

site 1

site 2

site 3

site 4

site 5

site 6

site 7

site 8

site 9

a

Enterol HF183 CF128 Enterol HF183 CF128 Enterol HF183 CF128 Enterol HF183 CF128 Enterol HF183 CF128 Enterol HF183 CF128 Enterol HF183 CF128 Enterol HF183 CF128 Enterol HF183 CF128

24-h cumulative precipitation on day of sampling

24-h cumulative precipitation one day prior to sampling

24-h cumulative precipitation two days prior to sampling

72-h cumulative precipitation

solar irradiance

0.43c −0.09 −0.10 0.06 −0.10 −0.08 0.15 0.03 −0.11 −0.01 −0.08 −0.03 0.18 0.00 −0.01 0.29b 0.17 0.12 0.35b 0.00 0.22 0.22 0.05 0.48d 0.05 0.18 −0.06

0.12 0.11 −0.09 −0.05 0.32b −0.11 0.08 0.30b 0.10 0.01 −0.07 0.01 0.13 0.15 0.17 0.06 0.12 0.11 −0.05 0.04 0.19 0.02 −0.07 0.25a 0.01 0.29b −0.10

0.22 −0.04 0.02 −0.09 −0.01 0.36c −0.02 −0.04 −0.18 0.00 0.10 0.33b −0.33b −0.13 0.38c −0.19 −0.01 0.03 0.04 0.06 0.10 0.05 0.20 −0.12 0.10 0.01 −0.09

0.39c −0.06 −0.09 −0.04 0.07 0.10 0.27b 0.11 −0.12 0.10 −0.04 0.11 0.10 0.00 0.23b 0.16 0.17 0.14 0.28b 0.04 0.28b 0.22 0.10 0.37c 0.02 0.23a −0.11

−0.02 −0.13 0.06 −0.12 −0.14 0.12 −0.28b 0.02 0.14 −0.20 0.04 0.08 −0.22 −0.24a 0.15 −0.23a −0.10 0.23 −0.19 −0.20 0.28b −0.14 −0.13 0.15 0.08 0.34b 0.07

Significance level (α): a = 0.1; b = 0.05; c = 0.001; d ≤ 0.001.

Information). During dry weather events samples from site 4 (after the Adjuntas WWTP) had the highest enterococci mean concentration (1.596 pg/reaction,) while site 8 (mouth of Arecibo river) had the lowest (0.005 pg/reaction). Site 7 (after the Utuado WWTP) had the highest mean value (27.28 pg/ reaction), and site 3 (before the Adjuntas WWTP) had the lowest mean value (2.92 pg/reaction) during wet weather events, while site 9 (after the Jayuya WWTP) has the second lowest Enterococcus concentration (3.53 pg/reaction). Site 4 had the highest median value for both dry and wet weather events (0.015 and 0.014 pg/reaction, respectively). In addition to enterococci data, we determined the presence of human and bovine fecal contamination using the HF183 and CF128 markers, respectively (Figure 1). For the human marker, sites 4, 7, and 9 (all located after WWTPs) had the three highest occurrences during wet sampling events (53.8%; 47.6%; and 58.5%). Analysis shows that site 4 had the highest occurrence of the human marker for dry sampling while site 9 had the highest for wet weather. The sites with the least percent positive for the human marker were sites 1, 5, 6, and 8 for dry weather (0%) and site 2 (7.32%) during wet events. The bovine marker was highest at site 1 for dry weather (15.38%), while site 7 (28.6%) and site 8 (20.5%) were the highest for wet weather events. The lowest values for bovine marker occurred at sites 4, 6, and 8 all with 0% during dry events and at site 9 for wet events with 0%. 3.2. Correlation among Temporal Variables. Results from the temporal analysis of Enterol using Spearman-rankcorrelation analysis showed that sites 1, 6, and 7 were positively correlated with the 24-h cumulative precipitation on the day of

and Table S4 (Supporting Information) shows a summary for the human and bovine assays. 2.5. Correlation Analysis. To determine any effects of landscape and environmental factors on the occurrence of MST markers and enterococci concentrations, a correlation analysis was performed using Spearman’s rank-correlation and Pearson product−moment correlation for dichotomous (presence/ absence) data. The Spearman rank-correlation is a nonparametric measure of statistical dependence between two variables. The Pearson product−moment correlation measures the linear dependence of the variables. Two correlation analyses were performed. The first was with variables measured through time separated by site, including Enterol, CF128, HF183, 24-h cumulative precipitation for the day of sampling, one and two days prior to sampling, the 72-h cumulative precipitation, as well as solar irradiance. The second analysis included the average Enterol concentration, percent positive for HF183 and CF128, seven land use types, percent impervious area, percent canopy cover, saturated hydraulic conductivity, the Shreve stream magnitude, estimated density of septic, sewers, and latrines, elevation, and slope. Average Enterol and percent positive CF128 and HF183 were separated by wet and dry sampling events for the spatial correlation analysis.

3. RESULTS 3.1. Occurrence of Microbial Markers in the RGA Watershed. To gain an understanding of the overall occurrences of fecal pollution in the RGA watershed, we first used descriptive statistics for Enterol (enterococci) at each of the sampling sites (Tables S3 and S4 of the Supporting D



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Table 2. Spearman’s Rank-Correlation Results for Host Specific Markers Separated by Season with Variables of Interest wet sampling events

a

dry sampling events

variable

Enterol

CF128

HF183

Enterol

CF128

HF183

Shreve open water developed barren forested agricultural grassland wetland canopy saturated hydraulic conductivity elevation slope number of NPDES facilities density of sewers density of septic tanks density of latrines downstream of WWTP

0.54 0.13 −0.02 0.13 −0.27 0.71b 0.33 0.41 −0.30 −0.07 −0.55 −0.30 0.63a −0.38 −0.12 0.35 0.27

0.67b −0.12 0.08 0.23 −0.22 0.71b 0.22 0.41 −0.32 −0.13 −0.80b 0.08 0.74b −0.20 0.13 0.68b −0.09

0.31 −0.73b 0.68b 0.84c −0.92d 0.30 0.52 0.14 −0.70b 0.20 −0.52 0.63a 0.52 0.67b 0.78b 0.52 0.82c

−0.17 −0.13 0.12 −0.38 0.22 −0.18 −0.27 −0.55 0.22 −0.48 0.10 −0.18 −0.02 0.18 0.07 −0.15 0.46

−0.50 0.21 −0.31 −0.42 0.38 −0.21 −0.50 −0.42 0.55 0.18 0.60a −0.37 −0.67b 0.12 −0.08 −0.38 −0.14

−0.28 −0.53 0.24 0.14 −0.31 −0.01 0.00 −0.36 0.00 −0.09 −0.10 0.21 −0.08 0.46 0.42 0.02 0.76b

Significance level (α): a = 0.1; b = 0.05; c = 0.001; d ≤ 0.001.

with the CF128 marker. A negative relationship was observed between CF128 and the average elevation of the sampling subbasin (r = −0.80). 3.4. Correlation with Spatial Variables during Dry Sampling. Spatial analysis showed that dry sampling events did not correlate with Enterol. For the CF128 marker there was a negative correlation with the number of NPDES facilities (r = −0.67) and a positive relationship with elevation (r = 0.60). We also observed a positive correlation of HF183 and sampling sites directly downstream of WWTPs for dry events (r = 0.76).

sampling (r = 0.43, 0.29, and 0.35, respectively). The previous day 24-h cumulative rainfall had no significant impact on Enterol and the 24-h cumulative rainfall from two days prior to sampling had a negative effect on site 5 (r = −0.33). The 72-h cumulative rainfall positively impacted the levels of Enterol at sites 1, 3, and 7 (r = 0.39, 0.27, 0.28, respectively). Solar irradiance negatively correlated with sites 3 and 6 for Enterol (r = −0.28, −0.23, respectively). Pearson product−moment correlation analyses indicated that the previous day 24-h cumulative precipitation had a positive correlation with the HF183 marker for sites 2, 3, and 9 (r = 0.32, 0.30, 0.29, respectively), while only site 8 was statistically significant for CF128 (r = 0.25). The 24-h cumulative precipitation on the day of sampling only had a significant correlation with the cattle marker at site 8 (r = 0.25); no significant correlations were observed for the human marker. No significant correlation was also observed for the human marker and the 24-h cumulative precipitation two days prior to sampling, whereas the cattle marker showed significant positive correlations at sites 2, 4, and 5 (r = 0.36, 0.33, 0.38, respectively). The 72-h cumulative rainfall had a positive correlation at site 9 for HF183 (r = 0.23) and at sites 5, 7, and 8 for CF128 (r = 0.23, 0.28, 0.37). Solar irradiance was negatively correlated with the human marker at site 5 (r = −0.24). For sites 7 and 9 there was a positive correlation with the cattle (r = 0.28) and human marker (r = 0.34), respectively. 3.3. Correlation with Spatial Variables during Wet Sampling. Spatial analysis showed that wet sampling events correlated with the average of the Enterol, HF183, and CF128 assays (Table 2). In wet sampling events, Enterol results were significantly correlated with agricultural land use (r = 0.71) and the number of NPDES facilities (r = 0.63) within the sub-basin. HF183 correlated positively with developed (r = 0.68) and barren land (r = 0.84), sewer (r = 0.67) and septic density (r = 0.78), presence of WWTP (r = 0.82), and slope (r = 0.63). There was a significant negative correlation with open water (r = −0.73) and forested (r = −0.92) land uses, as well as percent canopy cover (r = −0.70). Agricultural land (r = 0.71), the Shreve stream magnitude (r = 0.67), latrine density (r = 0.68), and number of NPDES facilities (r = 0.74) positively correlated

4. DISCUSSION Our observation that enterococci concentrations correlate with precipitation in some of the RGA watershed sites is consistent with previous fecal bacterial studies conducted in both tropical and temperate regions.32,33 For example, Santiago-Rodriguez et al.16 observed that rainfall positively correlated with enterococci in most sites within the RGA watershed, except at the inland lake (site 1). In another study, Eleira and Vogel33 found that the previous day’s fecal coliform concentrations and 24 and 168 h antecedent rainfall amounts were the best predictors of fecal coliform levels in the Charles river (Massachusetts, USA). Similarly, in this study we found the 24 h cumulative precipitation on the day of sampling and the 72 h cumulative precipitation to be a good indicator of fecal contamination, particularly in sites 1, 3, 6, and 7. As soils become saturated, antecedent soil moisture conditions may contribute to this effect. The amount of rainfall prior to sampling combined with the saturated hydraulic conductivity and evapotranspiration will affect the amount of runoff. If rainfall prior to sampling has already saturated the soil, the time until runoff is produced will be shortened,34 increasing the microbial transport into the watershed via runoff. The fact that not all the sites were impacted by precipitation in a similar fashion suggests that there are several factors that could play important roles in the fate and transport of different fecal bacteria such as proximity of the source, saturated hydraulic conductivity, evapotranspiration, and landscape associated with the sources. The positive correlations with precipitation strongly suggest that the E



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The data also indicate that regardless of the precipitation (i.e., wet and dry sampling events), the proximity of sampling sites to WWTPs had a significant positive impact on the correlation with HF183. The human marker strongly correlated with barren and developed land. In addition, we found a positive correlation with the slope during wet events, although it was not as strong as with developed and barren land. A possible explanation for the positive correlation between barren land and human marker signals could be that there is little vegetation to abate erosion, and fecal bacteria attached to soil particles are easily transported to the watershed during rain events. Several studies have discussed the importance of bacterial attachment to different types of soil and bacterial transport.41−43 For example, Yeghiazarian et al.43 concluded that clay soils contribute more to contamination events than sandy soils due to microbial attachment to clay particles and subsequent transport with sediment. The association of the human marker with developed land might be due to a higher density of septic systems and sewer lines in the developed areas of the RGA watershed. Therefore, the association could be due to faulty septic tanks, leaking sewer lines, and combined sewer overflows.7,44 Aging infrastructure and combined sewer overflows are known to be important sources of fecal contamination in temperate regions.45,46 This could also be a significant problem in Puerto Rico as approximately 40% of the population is either connected to an individual sewage treatment system or not connected at all.44 The negative relationship of the enterococci densities with the percentage of canopy cover and forested land use may be associated with decrease in rainfall intensity due to interception. Rainfall interception varies depending upon rainfall intensity, forest maturation, and leaf area index.47 A study in Mexico found that the total apparent interception loss was 17% of annual rainfall for mature forests and 8% for secondary forests.48 With the reduction of rainfall intensity due to interception, the impact on manure and soil particles will also be reduced and fewer particles will be transported.49 Additionally, there are fewer livestock and human populations in the forested areas of the watershed. Therefore, we would expect to see less of an impact from heavily forested areas of the watershed. Our findings are supported by similar observations in other studies conducted in the tropics9 and in temperate regions.50 These data suggest that watershed segments nearby densely forested areas associated with relatively low numbers of wildlife (which is the case in Puerto Rico) might not need to be monitored as intensively as developed areas. However, future studies should be conducted in tropical settings with higher animal density to better understand the role of canopy cover on the fate and transport of source tracking bacterial populations. The negative relationship between the cattle marker and elevation in wet weather is compatible with the density of freerange livestock or animal farming operations in the mountainous areas of the watershed. Very few cattle were spotted at sites 1 and 2, which are the sampling sites with the highest elevations. However, there was also a positive correlation between the cattle assay and elevation during dry sampling events (r = 0.60), and unexpectedly, the cattle marker correlated with sites downstream of NPDES facilities (r = 0.74). While the impact of cattle having direct access to the water in some sites might explain such correlations, it should be noted that the cattle marker has been shown to exhibit some level of cross-reactivity with other sources.51−54 In the study by

resulting pollution was a direct result of stormwater runoff at certain sites (Table 1). It is reasonable to assume that solar irradiance would be harmful to fecal bacteria and display a negative correlation with in situ fecal bacterial levels. This would clearly be a significant factor in tropical watersheds. However, in our study solar irradiance was negatively correlated with Enterol at some sites but was positively correlated with the human and cattle marker at other sites (Table 1). Such negative correlation has been observed in several studies indicating that bacterial die off increases with an increase in solar irradiance.32,35 The positive correlation in dry sampling events could be linked to the occurrence of algal mats and its impact on bacterial persistence in surface waters. Indeed, the streams in this study are very shallow, allowing for significant sunlight penetration, resulting in algal growth stimulation. Such correlation has been observed in temperate waters. For instance, a recent study suggested that algal blooms in Lake Michigan could be an important source of Escherichia coli and enterococci.36 The Cladophora algal species is common in lakes and streams worldwide,37 and even dead algal mats have been shown to promote fecal bacterial growth.38 The fact that some of the sites exhibited inverse correlations with solar irradiance for different markers suggests that there are different mechanisms of survival rendering solar irradiance a variable with poor predictive power in the studied watershed. Our calculations of the average solar irradiance for the sampling site’s sub-basin using GIS suggest that there are several factors that cannot be accounted for when using this approach, such as stream depth, canopy cover, and cloud cover. The use of tools such as pyranometers should be investigated in the future to more accurately determine the importance of irradiance in survival of fecal bacteria in watersheds.32 Sites 7 and 8 are the only sub-basins within the RGA watershed associated with land predominantly used for agricultural activities (Supporting Information Table S2).39 It would be expected that site 8 would have the highest levels of fecal contamination in this watershed since agricultural and urban land dominate the northern section of the RGA watershed, and all flow terminates at this site. However, this was not the case based on enterococci and MST markers during dry sampling periods. It is plausible that salinity at the mouth of the river (site 8) might be contributing to the die-off of these bacterial groups. Indeed, salinity has been shown to have an inverse relationship with enterococci levels and fecal coliforms in tropical9 and temperate waters.40 In contrast, during wet weather events, agricultural land use seems to have a significant impact on fecal pollution in the RGA watershed. Although site 8 is associated with more agricultural activities than site 7, the latter site had a higher incidence of the bovine marker during wet weather. This suggests that salinity might also impact the survival of MST-targeted populations in tropical waters. The results indicate that a majority of the fecal pollution in this watershed is due to loadings from wastewater treatment plants. The three highest enterococci averages and the highest number of positive samples for the human assay were observed at the sampling sites just downstream of the wastewater treatment plants. The highest enterococci average in dry sampling events was the site just downstream of the Adjuntas WWTP (site 4). Moreover, this site had nearly the same percentage positive for dry (53.3%) and wet (53.8%) sampling events for the human marker, further suggesting that the fecal bacterial groups detected are coming from the treatment plants. F



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Figure 2. Map of hotspots during wet sampling events. Sources of fecal contamination are shown in color. The color-coding differentiates types of contamination sources and associated land uses.

Toledo-Hernández et al.,15 the CF128 marker had a 64% specificity when tested against 66 cattle fecal DNA extracts and that it cross-reacted with swine, turkey, and chicken. Shanks et al.54 reported that the CF128 marker cross-reacted with chicken, swine, dog, and duck fecal samples and had a 76% specificity when challenged against 175 fecal samples from 24 animal species. Where there is open water (i.e., reservoirs), there was a negative correlation with the human marker. A possible explanation is that the reservoirs (e.g., Dos Bocas) are acting as a sink for the bacteria. It is possible that the way in which these reservoirs are managed could have an influence on the fecal bacterial levels. If the water is held for an extended period, the bacteria will settle to the bottom of the reservoir. During intense or prolonged rain events, water will need to be released from the reservoir, carrying bacteria as well. It has been shown that stream and reservoir beds serve as sinks when microbes are settled, only to become sources when they are resuspended.55−57 By analyzing the positive correlations of the fecal markers with explanatory variables, we can create a hot spot map to visualize the areas that should be monitored for fecal contamination (Figure 2). Where there is a certain land use, or an anthropogenic source that correlated positively with the fecal markers, the stream segments downstream of this particular land use or source should be monitored more closely. Due to the data resolution for some specific variables, the entire watershed would be considered a continuous hot spot, or fecally impacted. This is the case with the septic, sewer, and latrine density variables. We were only able to obtain population estimates using each of these variables for the subbasins. Future studies need to determine the exact location of septic and latrines in order to better identify specific monitoring points. Due to this limitation, in this study we used fine resolution data, such as land uses, soils, and point source data to highlight the most impacted areas in this watershed. These

maps can help policy makers visualize impacted areas of streams that would require specific monitoring to help in the identification of primary fecal sources. In conclusion, to better understand the patterns of fecal contamination in the RGA watershed we integrated climatic, geographic, and anthropologic data such as rainfall amount and intensity, topography, soil properties, and land cover and use with data on occurrence of general, human, and cattle fecal markers. From this information, using GIS we generated hotspot maps of sources of fecal contamination. We suggest that hot-spot mapping is a powerful technique to more efficiently process a large amount of information and to present the results in a visually compelling form that can be used for watershed management and decision-making. Additionally, we probed the geographic stability of two host-specific assays developed for temperate regions. We found that these assays applied in the tropics produce results that are inconsistent with studies conducted in temperate regions. This suggests that assays need to be tested for host-specificity in a given region prior to being used in MST studies. The data also suggest that there is a need to develop additional assays that are compatible with host-specificity and host-distribution profiles of source tracking assays in order to accurately detect fecal sources in tropical waters. Environmental, landscape, land use, and hydrological factors associated with each watersheds need to be taken into consideration when using MST tools in environmental monitoring applications.

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ASSOCIATED CONTENT

S Supporting Information *

Four tables and one figure. This material is available free of charge via the Internet at http://pubs.acs.org. G



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Bacteroides-Prevotella Genes Encoding 16S rRNA. Appl. Environ. Microbiol. 2000, 66, 4571−4574. (13) Layton, A.; McKay, L.; Williams, D.; Garrett, V.; Gentry, R.; Sayler, G. Development of Bacteroides 16S rRNA Gene TaqMan-Based Real-Time PCR Assays for Estimation of Total, Human, and Bovine Fecal Pollution in Water. Appl. Environ. Microbiol. 2006, 72, 4214− 4224. (14) Fujioka, R.; Sian-Denton, C.; Borja, M.; Castro, J.; Morphew, K. Soil: the environmental source of Escherichia coli and Enterococci in Guam’s streams. J. Appl. Microbiol. 1998, 85 (Suppl 1), 83S−89S. (15) Toledo-Hernández, C.; Ryu, H.; Gonzalez-Nieves, J.; Huertas, E.; Toranzos, G. A.; Domingo, J. S. Tracking the primary sources of fecal pollution in a tropical watershed: a one year study. Appl. Environ. Microbiol. 2013, 79, 1689−1696. (16) Santiago-Rodriguez, T. M.; Tremblay, R. L.; Toledo-Hernández, C.; Gonzalez-Nieves, J. E.; Ryu, H.; Domingo, J. W. S.; Toranzos, G. A. Microbial Quality of Tropical Inland Waters and Effects of Rainfall Events. Appl. Environ. Microbiol. 2012, 78, 5160−5169. (17) Wilkes, G.; Edge, T. A.; Gannon, V. P. J.; Jokinen, C.; Lyautey, E.; Neumann, N. F.; Ruecker, N.; Scott, A.; Sunohara, M.; Topp, E.; Lapen, D. R. Associations among pathogenic bacteria, parasites, and environmental and land use factors in multiple mixed-use watersheds. Water Res. 2011, 45, 5807−5825. (18) Gentry, R. W.; McCarthy, J.; Layton, A.; McKay, L. D.; Williams, D.; Koirala, S. R.; Sayler, G. S. Escherichia coli loading at or near base flow in a mixed-use watershed. J. Environ. Qual. 2006, 35, 2244−2249. (19) Hampson, D.; Crowther, J.; Bateman, I.; Kay, D.; Posen, P.; Stapleton, C.; Wyer, M.; Fezzi, C.; Jones, P.; Tzanopoulos, J. Predicting microbial pollution concentrations in UK rivers in response to land use change. Water Res. 2010, 44, 4748−4759. (20) U.S. Environmental Protection Agency Total Maximum Daily Loads (TMDL) Rio Grande de Arecibo Watershed: Fecal Coliform http://www.epa.gov/waters/tmdldocs/Fecal Coliform TMDL Rio Grande de Arecibo.pdf (accessed Mar 13, 2012). (21) GLM Engineering Hydrologic-Hydraulic Study of Rio Grande de Arecibo, Renewable Power Generation and Resource Recovery Facility, Arecibo, Puerto Rico. http://www2.pr.gov/AGENCIAS/jca/ Documents/Documentos Ambientales/2010/Preliminar/Borrador Energy Answers - Arecibo - JCA-10-0018/ApendiceB_EstudioHH_ Parte1.pdf (accessed Apr 5, 2012). (22) Martinuzzi, S.; Gould, W. A.; Ramos González, O. M. Land development, land use, and urban sprawl in Puerto Rico integrating remote sensing and population census data. Landscape Urban Plan. 2007, 79, 288−297. (23) Daly, C.; Helmer, E. H.; Quiñones, M. Mapping the climate of Puerto Rico, Vieques and Culebra. Int. J. Climatol. 2003, 23, 1359− 1381. (24) The Weather Channel Monthly Weather for Arecibo, PR. http://www.weather.com/weather/wxclimatology/monthly/graph/ USPR0009 (accessed Apr 1, 2013). (25) Ryu, H.; Griffith, J. F.; Khan, I. U. H.; Hill, S.; Edge, T. A.; Toledo-Hernández, C.; Gonzalez-Nieves, J.; Santo Domingo, J. Comparison of gull feces-specific assays targeting the 16S rRNA genes of Catellicoccus marimammalium and Streptococcus spp. Appl. Environ. Microbiol. 2012, 78, 1909−1916. (26) Haugland, R. A.; Siefring, S. C.; Wymer, L. J.; Brenner, K. P.; Dufour, A. P. Comparison of Enterococcus measurements in freshwater at two recreational beaches by quantitative polymerase chain reaction and membrane filter culture analysis. Water Res. 2005, 39, 559−568. (27) Ludwig, W.; Schleifer, K.-H. How Quantitative is Quantitative PCR with Respect to Cell Counts? Syst. Appl. Microbiol. 2000, 23, 556−562. (28) Pachepsky, Y. A.; Sadeghi, A. M.; Bradford, S. A.; Shelton, D. R.; Guber, A. K.; Dao, T. Transport and fate of manure-borne pathogens: Modeling perspective. Agr. Water Manage. 2006, 86, 81−92. (29) Neteler, M.; Bowman, M. H.; Landa, M.; Metz, M. GRASS GIS: A multi-purpose open source GIS. Environ. Modell. Softw. 2012, 31, 124−130.

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected] (J.W.S.D.); yeghialt@ ucmail.uc.edu (L.Y.). Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The U.S. Environmental Protection Agency, through its Office of Research and Development, partially funded and collaborated in the research described herein. It has been subjected to the Agency’s administrative review and has been approved for external publication. Any opinions expressed in this paper are those of the author(s) and do not necessarily reflect the views of the Agency; therefore, no official endorsement should be inferred. Any mention of trade names or commercial products does not constitute endorsement or recommendation for use. We thank Thomas Adams of the National Weather Service for providing the NEXRAD precipitation data. H.R. was the recipient of a National Research Council Senior Research Fellowship.

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REFERENCES

(1) U.S. Environmental Protection Agency National Summary of Impaired Waters and TMDL Information. http://ofmpub.epa.gov/ tmdl_waters10/attains_nation_cy.control?p_report_type=T#causes_ 303d (accessed Aug 21, 2012). (2) Santo Domingo, J. W.; Bambic, D. G.; Edge, T. A.; Wuertz, S. Quo vadis source tracking? Towards a strategic framework for environmental monitoring of fecal pollution. Water Res. 2007, 41, 3539−3552. (3) Scott, T. M.; Rose, J. B.; Jenkins, T. M.; Farrah, S. R.; Lukasik, J. Microbial Source Tracking: Current Methodology and Future Directions. Appl. Environ. Microbiol. 2002, 68, 5796−5803. (4) Simpson, J. M.; Santo Domingo, J. W.; Reasoner, D. J. Microbial Source Tracking: State of the Science. Environ. Sci. Technol. 2002, 36, 5279−5288. (5) U.S. Environmental Protection Agency Improved enumeration methods for the recreational water quality indicators: Enterococci and Escherichia coli.; Office of Science and Technology: Washington, D.C., 2000; p 55. (6) Wheeler, A. L.; Hartel, P. G.; Godfrey, D. G.; Hill, J. L.; Segars, W. I. Potential of Enterococcus faecalis as a human fecal indicator for microbial source tracking. J. Environ. Qual. 2002, 31, 1286−1293. (7) Peed, L. A.; Nietch, C. T.; Kelty, C. A.; Meckes, M.; Mooney, T.; Sivaganesan, M.; Shanks, O. C. Combining Land Use Information and Small Stream Sampling with PCR-Based Methods for Better Characterization of Diffuse Sources of Human Fecal Pollution. Environ. Sci. Technol. 2011, 45, 5652−5659. (8) Kang, J.-H.; Lee, S. W.; Cho, K. H.; Ki, S. J.; Cha, S. M.; Kim, J. H. Linking land-use type and stream water quality using spatial data of fecal indicator bacteria and heavy metals in the Yeongsan river basin. Water Res. 2010, 44, 4143−4157. (9) Ragosta, G.; Evensen, C.; Atwill, E.; Walker, M.; Ticktin, T.; Asquith, A.; Tate, K. W. Causal connections between water quality and land use in a rural tropical island watershed. EcoHealth 2010, 7, 105− 113. (10) Crowther, J.; Wyer, M. D.; Bradford, M.; Kay, D.; Francis, C. A. Modelling faecal indicator concentrations in large rural catchments using land use and topographic data. J. Appl. Microbiol. 2003, 94, 962− 973. (11) Goto, D. K.; Yan, T. Effects of land uses on fecal indicator bacteria in the water and soil of a tropical watershed. Microbes Environ. 2011, 26, 254−260. (12) Bernhard, A. E.; Field, K. G. A PCR Assay To Discriminate Human and Ruminant Feces on the Basis of Host Differences in H



Article

(30) Hofierka, J.; Suri, M. The solar radiation model for Open source GIS: implementation and applications. International GRASS users conference; Trento, Italy, Sep 11−13, 2002. (31) Simpson, E. H. The Interpretation of Interaction in Contingency Tables. J. R. Stat. Soc. B Met. 1951, 13, 238−241. (32) Cho, K. H.; Cha, S. M.; Kang, J.-H.; Lee, S. W.; Park, Y.; Kim, J.W.; Kim, J. H. Meteorological effects on the levels of fecal indicator bacteria in an urban stream: A modeling approach. Water Res. 2010, 44, 2189−2202. (33) Eleria, A.; Vogel, R. M. Predicting Fecal Coliform Bacteria Levels in the Charles River, Massachusetts, USA. J. Am. Water Resour. As. 2005, 41, 1195−1209. (34) Akan, A. O.; Houghtalen, R. J. Urban Hydrology, Hydraulics, and Stormwater Quality - Engineering Applications and Computer Modeling, 1st ed.; John Wiley & Sons, Inc.: Hoboken, NJ, 2003. (35) Auer, M. T.; Niehaus, S. L. Modeling fecal coliform bacteria−I. Field and laboratory determination of loss kinetics. Water Res. 1993, 27, 693−701. (36) Whitman, R. L.; Shively, D. A.; Pawlik, H.; Nevers, M. B.; Byappanahalli, M. N. Occurrence of Escherichia Coli and Enterococci in Cladophora (Chlorophyta) in Nearshore Water and Beach Sand of Lake Michigan. Appl. Environ. Microbiol. 2003, 69, 4714−4719. (37) Byappanahalli, M. N.; Shively, D. A.; Nevers, M. B.; Sadowsky, M. J.; Whitman, R. L. Growth and survival of Escherichia coli and enterococci populations in the macro-alga Cladophora (Chlorophyta). FEMS Microbiol. Ecol. 2003, 46, 203−211. (38) Yu., I.; Gubelit, M. B. V. Growth of Enterobacteria on Algal Mats in the Eastern Part of the Gulf of Finland. Inland Water Biol. 2011, 4, 132−136. (39) Homer, C.; Huang, C.; Yang, L.; Wylie, B.; Coan, M. Development of a 2001 National Landcover Database for the United States. Photogramm. Eng. Rem. S. 2004, 70, 829−840. (40) Mallin, M. A.; Williams, K. E.; Esham, E. C.; Lowe, R. P. Effect of Human Development on Bacteriological Water Quality in Coastal Watersheds. Ecol. Appl. 2000, 10, 1047−1056. (41) Dorner, S. M.; Anderson, W. B.; Slawson, R. M.; Kouwen, N.; Huck, P. M. Hydrologic Modeling of Pathogen Fate and Transport. Environ. Sci. Technol. 2006, 40, 4746−4753. (42) Fries, J. S.; Characklis, G. W.; Noble, R. T. Attachment of Fecal Indicator Bacteria to Particles in the Neuse River Estuary, N.C. J. Environ. Engr. ASCE 2006, 132, 1338−1345. (43) Yeghiazarian, L. L.; Walker, M. J.; Binning, P.; Parlange, J.-Y.; Montemagno, C. D. A combined microscopic and macroscopic approach to modeling the transport of pathogenic microorganisms from nonpoint sources of pollution. Water Resour. Res. 2006, 42, W09406. (44) Laughlin, J. EPA Regional Focus: Region 2 Focuses on CSOs, Energy Management, Green Infrastructure. http://www.waterworld. com/articles/print/volume-27/issue-8/editorial-features/epa-regionalfocus-region-2-focuses-on-csos-energy-management-greeninfrastructure.html (accessed Oct 29, 2012). (45) Passerat, J.; Ouattara, N. K.; Mouchel, J.-M.; Rocher, V.; Servais, P. Impact of an intense combined sewer overflow event on the microbiological water quality of the Seine River. Water Res. 2011, 45, 893−903. (46) US EPA Microbial Source Tracking Guide Document; Office of Research and Development: Washington, DC, 2005; p 131. (47) Crockford, R. H.; Richardson, D. P. Partitioning of rainfall into throughfall, stemflow and interception: effect of forest type, ground cover and climate. Hydrol. Process. 2000, 14, 2903−2920. (48) Holwerda, F.; Bruijnzeel, L. A.; Muñoz-Villers, L. E.; Equihua, M.; Asbjornsen, H. Rainfall and cloud water interception in mature and secondary lower montane cloud forests of central Veracruz, Mexico. J. Hydrol. 2010, 384, 84−96. (49) Peña-Arancibia, J. L.; van Dijk, A. I. J. M.; Guerschman, J. P.; Mulligan, M.; (Sampurno) Bruijnzeel, L. A.; McVicar, T. R. Detecting changes in streamflow after partial woodland clearing in two large catchments in the seasonal tropics. J. Hydrol. 2012, 416−417, 60−71.

(50) Schoonover, J. E.; Lockaby, B. G. Land cover impacts on stream nutrients and fecal coliform in the lower Piedmont of West Georgia. J. Hydrol. 2006, 331, 371−382. (51) Gawler, A. H.; Beecher, J. E.; Brandão, J.; Carroll, N. M.; Falcão, L.; Gourmelon, M.; Masterson, B.; Nunes, B.; Porter, J.; Rincé, A.; Rodrigues, R.; Thorp, M.; Walters, J. M.; Meijer, W. G. Validation of host-specific Bacteriodales 16S rRNA genes as markers to determine the origin of faecal pollution in Atlantic Rim countries of the European Union. Water Res. 2007, 41, 3780−3784. (52) Gourmelon, M.; Caprais, M. P.; Ségura, R.; Le Mennec, C.; Lozach, S.; Piriou, J. Y.; Rincé, A. Evaluation of two libraryindependent microbial source tracking methods to identify sources of fecal contamination in French estuaries. Appl. Environ. Microbiol. 2007, 73, 4857−4866. (53) Liu, R.; Chiang, M. H. Y.; Lun, C. H. I.; Qian, P.-Y.; Lau, S. C. K. Evaluation of host-specific Bacteroidales 16S rRNA gene markers as a complementary tool for detecting fecal pollution in a prairie watershed. Water Res. 2010, 43, 6164−6174. (54) Shanks, O. C.; White, K.; Kelty, C. A.; Hayes, S.; Sivaganesan, M.; Jenkins, M.; Varma, M.; Haugland, R. A. Performance Assessment PCR-Based Assays Targeting Bacteroidales Genetic Markers of Bovine Fecal Pollution. Appl. Environ. Microbiol. 2010, 76, 1359−1366. (55) Bai, S.; Lung, W.-S. Modeling sediment impact on the transport of fecal bacteria. Water Res. 2005, 39, 5232−5240. (56) Burton, G. A.; Gunnison, D.; Lanza, G. R. Survival of pathogenic bacteria in various freshwater sediments. Appl. Environ. Microbiol. 1987, 53, 633−638. (57) Jamieson, R. C.; Joy, D. M.; Lee, H.; Kostaschuk, R.; Gordon, R. J. Resuspension of sediment-associated Escherichia coli in a natural stream. J. Environ. Qual. 2005, 34, 581−589.

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