Determining Hot Spots of Fecal Contamination in a TropicalWatershed by Combining Land-Use Information and MeteorologicalData with Source-Specific AssaysJustin R. Jent,† Hodon Ryu,‡ Carlos Toledo-Hernandez,§ 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 ofenvironmental, topographical, meteorological, and anthropologic factors in the RioGrande de Arecibo (RGA) watershed in Puerto Rico with information provided bymicrobial source tracking (MST) to map hot spots (i.e., likely sources) of fecalcontamination. Water samples were tested for the presence of human and bovinefecal contamination in addition to fecal indicator bacteria and correlated againstseveral land uses and the density of septic tanks, sewers, and latrines. Specifically,human sources were positively correlated with developed (r = 0.68), barren landuses (r = 0.84), density of septic tanks (r = 0.78), slope (r = 0.63), and the proximityto wastewater treatment plants (WWTPs) (r = 0.82). Agricultural land, the numberof 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 spotmap, which shows areas that should be closely monitored for fecal contamination in the RGA watershed. The results indicatedthat 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 importantsources of fecal contamination in environmental waters.

1. INTRODUCTION

Fecal contamination is one of the leading causes of surfacewater impairment in the United States.1 In order to establishadequate 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 geographiclocation of these sources. Answering either of these questions isnot trivial. Answering the question of host identificationrequires the use of host-specific assays, a suite of techniquescollectively known as microbial source tracking (MST).2−4

MST is poised to provide more information than the traditionalmethods that use fecal-indicator bacteria (FIB), such as fecalcoliforms (in freshwater) and enterococci (in marine andfreshwater).5 A major drawback of traditional methods is thatthey cannot be used to identify the specific hosts because theseindicators are present in most host types.6

Identification of the exact location of the fecal source isdifficult because sources are often geographically dispersed,such as for instance migrating wildlife, free-range (unfenced)livestock, and faulty septic tanks. In fact, nonpoint sources offecal pollution are a major contributor to water qualityimpairement.2 In order to identify the location one has toinclude certain environmental factors that play a key role in

microbial transport such as topography, soil properties, andprecipitation amount and intensity. Because fecal contami-nation of surface waters is a manifestation of the interplaybetween many environmental processes and factors, fecalcontamination data alone are not sufficient, and a systemwideperspective is needed. Several studies have employed integrateddata analysis to investigate the sources of fecal contami-nation.7−11 Most of these studies, however, were conducted intemperate climates, with results that may be hard to extrapolateto the tropics.Several studies4,12,13 have stressed the need of better

evaluation of the performance of assays targeting bacterialindicators 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 assessmentof human health risk since the soils in tropical environments(Hawaii and Guam) provide favorable growth conditions for

Received: January 21, 2013Revised: April 15, 2013Accepted: April 16, 2013

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FIB, creating secondary sources of these bacteria unrelated tofecal sources that can impact nearby waters. This makes thetask of identifying fecal sources in the tropics challenging.Unfortunately, the performance of MST assays in tropicalenvironments is not well-documented. Recently, Toledo-Hernandez et al.15 collected water samples during a 13-month period in the Rio Grande de Arecibo (RGA) watershedin Puerto Rico and used several source tracking markers toidentify the primary sources of fecal pollution in the watershed.The results from this study indicated that humans and cattlewere the most important sources of pollution, which is inagreement with the presence of wastewater treatment plantsand cattle in the most polluted sites. However, the occurrenceof the human and cattle markers was lower than expected,suggesting that additional assays may be needed to betterdetermine fecal loads associated with each of these sources.Another recent study by Santiago-Rodriguez et al.16 incorpo-rated rainfall events and MST data in the RGA watershed.While these studies were some of the first MST studies in atropical region, important geographic and anthropologicvariables such as topography, soils, land cover, and landmanagement were not considered.There are many studies7,10,17−19 on the occurrence and

dynamics of fecal bacteria and MST markers in temperateregions. However, to our knowledge there is scarce data thatcharacterize the impact of tropical climate and landscape on theoccurrence of MST bacterial genetic markers. As a result, it isdifficult to assess when, and to what extent, factors such aslandscape, precipitation, and solar irradiation have a differentimpact on the performance of MST assays and on the overallsurvival of targeted fecal bacteria in tropical settings than intemperate regions. To address these issues, we integratedmeteorological, land cover, soil, and anthropological informa-tion with MST bacterial assays that were first evaluated byToledo-Hernandez et al.15 at different sites within the RGAwatershed. The main objective of our study was to determinethe importance of environmental, topographical, meteorolog-ical, and anthropologic factors on the occurrence of MSTbacterial markers in tropical waters, using as a model the RGAwatershed. While we do not consider fate and transportdirectly, we associated variables that produce runoff (e.g.,precipitation, land cover, slope, and soil) with fecal pollution.We also compared descriptive statistics with other studiesconducted in temperate regions and mapped the fecalcontamination hot spots to show future locations for sampling.This strategy is likely to reduce the uncertainty associated withlocating microbial contamination sources, which, in turn, leadsto more effective development and implementation of manage-ment practices used to reduce microbial loads.

2. METHODS2.1. Study Area and Sample Sites. The RGA watershed

is located in the western-central part of Puerto Rico and has acatchment area of approximately 616 km2 (Figure S1 of theSupporting Information). We selected the RGA watershedsince it was identified as impaired in a recent total maximumdaily load (TMDL) study.20 In this watershed fecal pollutioncan result from multiple point sources, such as leaking septicand sewer systems, discharge from wastewater treatment plants(WWTPs), and nonpoint sources associated with agriculturalactivities. The microbial water quality of the RGA watershed isa major concern as it is an important drinking water reservoirand some of its sections are used in recreational activities. Thus,

in addition to the public health concerns, there is a significantnegative 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) andcontinues northward to the Atlantic Ocean. Karst formationsare present in the upper reaches, which allows surface water toseep underground through sinkholes and travel throughunderground fissures. Several lakes and reservoirs are scatteredthroughout the watershed; the largest ones being Lago DosBocas, Lago Guayo, and Lago Caonillas. Approximately 11 kmupstream from the coast, the river spreads to a 4-km widealluvial floodplain.21 Predominant land uses include forestreserves, coffee plantations, and minor crops such as beans,plantains, and citrus. Urban development is mainly confined tomunicipalities of Adjuntas, Utuado, and Jayuya. Most of thepopulation in the RGA watershed is located in the coastalalluvial plain near the municipality of Arecibo.22 The upperwatershed is mostly forested, undeveloped land.The climate in Puerto Rico varies due to its topography.

Climate varies from subtropical at the higher elevations nearthe headwaters to tropical in the lower elevations. Due to itsproximity to the equator, the temperature varies littlethroughout the year. Between the months of May andNovember rainfall can either be scarce, or flood-producingdue to disturbances in the east-to-west trade winds.23 FromNovember to April significant amounts of rainfall can occurwith monthly averages ranging from 78 to 180.6 mm forArecibo.24 Elevation also had a significant effect on the amountof precipitation. Daly et al.23 found a 140% increase inprecipitation 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 presumedpresence of human and cattle fecal sources (Figure S1of theSupporting Information). Three sites (4, 7, and 9) were locateddownstream of a wastewater treatment plant (WWTP) for themunicipalities of Adjuntas, Utuado, and Jayuya, respectively.Sites 1 and 2 were located near the Guilarte National Forestand were considered low-impact sampling sites since nodomesticated animals (e.g., cattle, swine, poultry) are locatednear these sites and very few humans populate the surroundingarea. Site 3 was located on the Cidras river before the WWTPwithin the municipality of Adjuntas. Sampling site 5 was locatedbefore the WWTP at the start of the RGA. Site 6 was locatedwithin the municipality of Utuado. Sampling site 8 is located atthe mouth of the watershed right before the river drains intothe Atlantic Ocean.

2.2. Sample Collection and Analysis. Sample collectionand data analyses are fully described in the work of Toledo-Hernandez et al.15 Briefly, sampling began on October 30,2009, and continued biweekly until December 22, 2010. A totalof 54 samples were collected for each of the nine sampling sitesthroughout the initial study period. Samples were collected inthe same day and were transported to the laboratory on icewithin a 6-h time frame. All the samples were filtered ontopolycarbonate membranes (0.4-μm pore size, 47-mm diameter;GE Water and Process Technologies, Trevose, PA) in theMicrobiology Laboratory at the University of Puerto Rico−RioPiedras Campus. Membranes were placed in microcentrifugetubes and sent to the USEPA laboratory (Cincinnati, OH) forDNA extractions. DNA was extracted from each filter asdescribed by Ryu et al.,25 and aliquots (2 μL) of the DNAextracts were used as a template in conventional andquantitative polymerase chain reaction (PCR) assays. To

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determine fecal pollution levels, we targeted Enterococcus spp. asthe general FIB group using the Enterol assay.26,27 The HF183assay was used to amplify human-specific Bacteroidetes, whilethe CF128 assay was used to selectively amplify ruminant-specific Bacteroidetes.12 These assays were chosen based on theknowledge of the area and presumed sources of contamination.The Enterol assay was used in quantitative PCR producingcontinuous data, while the HF183 and CF128 were used asconventional 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 transportof bacteria in the environment since bacteria are transported insubsurface, overland, and open channel flow.28 In this analysis,we used GIS to calculate the land area contributing runoff toeach sampling location, and associated the fecal contaminationwith slope, saturated hydraulic conductivity, land uses, andseveral other explanatory variables within the contributing area.A digital elevation model along with hydrography data wasdownloaded from the National Hydrography Data set Plus(NHDPlus) http://www.horizon-systems.com/nhdplus/. TheNHDPlus is an integrated suite of application-ready geospatialdata that incorporates the National Hydrography Data set withthe National Elevation Data set and Watershed Boundary Dataset. Following the procedure outlined in Kang et al.,8 sub-basinswere delineated for each of the sampling sites. Each samplingsite was used as an outlet point for the delineation. Samplingsites that were upstream of another sampling site were nestedin 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 wasreclassified into seven categories: open water, developed, barrenland, forested, grassland, agricultural, and wetlands. TheSSURGO soils data for Puerto Rico was obtained from theNational Resources Conservation Service http://soils.usda.gov/survey/geography/ssurgo/. For precipitation estimates, hourlyNEXRAD level III gridded data were obtained from the

National Weather Service. Solar irradiance was calculated usingthe GRASS GIS29 module r.sun.30 An estimate of thepopulation using septic, sewer, and latrines within eachsampling sub-basin was taken from a TMDL study performedby the USEPA.20 We then divided the population by the area ofthe sub-basin to obtain a septic, sewer, and latrine density.Additionally, the number of National Pollution DischargeElimination System (NPDES) permitted facilities within eachsampling sub-basin was obtained from the TMDL study.20

Each variable of interest, which included elevation, slope,land use, saturated hydraulic conductivity, precipitation, Shrevestream magnitude, the number of NPDES-permitted facilities,and the density of septic tanks, latrines, and sewers wereallocated to each sampling site’s sub-basin (Tables S1 and S2 ofthe Supporting Information). The average Enterococcus spp.(Enterol) concentrations were calculated for each samplingsub-basin, as well as the percent positive for the human(HF183) and bovine (CF128) marker.

2.4. Separation of Wet and Dry Sampling Events. Sinceit is known that bacteria are transported with surface water inthe rainfall-runoff process and that, during intense rainfallevents and increased runoff, fecal contamination is oftenhighest,28 it is necessary to separate the analysis into wet anddry sampling events.31 Without this separation, correlationswould be misrepresented. This effect is common in dataanalysis and is known as Simpson’s paradox.31 The dry and wetsampling events were determined by summing the precipitationover a three-day period for each sampling site, as follows:hourly NEXRAD level III gridded precipitation data wereaggregated to a daily rainfall total from 00 to 23 UTC for eachsampling sub-basin. The time of day for sampling was notavailable; therefore, only a daily average could be used. If morethan 2 mm of rain fell between the day of sampling and theprevious 2-day period, the sampling date was classified as wet.8

There were a total of 41 wet sampling days and 13 dry samplingdays. Table S3 (Supporting Information) provides a statisticalsummary for each of the sampling sub-basins for Enterol values,

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

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and Table S4 (Supporting Information) shows a summary forthe human and bovine assays.2.5. Correlation Analysis. To determine any effects of

landscape and environmental factors on the occurrence of MSTmarkers and enterococci concentrations, a correlation analysiswas performed using Spearman’s rank-correlation and Pearsonproduct−moment correlation for dichotomous (presence/absence) data. The Spearman rank-correlation is a non-parametric measure of statistical dependence between twovariables. The Pearson product−moment correlation measuresthe linear dependence of the variables. Two correlation analyseswere performed. The first was with variables measured throughtime separated by site, including Enterol, CF128, HF183, 24-hcumulative precipitation for the day of sampling, one and twodays prior to sampling, the 72-h cumulative precipitation, aswell as solar irradiance. The second analysis included theaverage Enterol concentration, percent positive for HF183 andCF128, seven land use types, percent impervious area, percentcanopy cover, saturated hydraulic conductivity, the Shrevestream magnitude, estimated density of septic, sewers, andlatrines, elevation, and slope. Average Enterol and percentpositive CF128 and HF183 were separated by wet and drysampling events for the spatial correlation analysis.

3. RESULTS

3.1. Occurrence of Microbial Markers in the RGAWatershed. To gain an understanding of the overalloccurrences of fecal pollution in the RGA watershed, we firstused descriptive statistics for Enterol (enterococci) at each ofthe sampling sites (Tables S3 and S4 of the Supporting

Information). During dry weather events samples from site 4(after the Adjuntas WWTP) had the highest enterococci meanconcentration (1.596 pg/reaction,) while site 8 (mouth ofArecibo river) had the lowest (0.005 pg/reaction). Site 7 (afterthe Utuado WWTP) had the highest mean value (27.28 pg/reaction), and site 3 (before the Adjuntas WWTP) had thelowest mean value (2.92 pg/reaction) during wet weatherevents, while site 9 (after the Jayuya WWTP) has the secondlowest Enterococcus concentration (3.53 pg/reaction). Site 4had the highest median value for both dry and wet weatherevents (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 andCF128 markers, respectively (Figure 1). For the human marker,sites 4, 7, and 9 (all located after WWTPs) had the threehighest occurrences during wet sampling events (53.8%; 47.6%;and 58.5%). Analysis shows that site 4 had the highestoccurrence of the human marker for dry sampling while site 9had the highest for wet weather. The sites with the least percentpositive for the human marker were sites 1, 5, 6, and 8 for dryweather (0%) and site 2 (7.32%) during wet events. The bovinemarker was highest at site 1 for dry weather (15.38%), whilesite 7 (28.6%) and site 8 (20.5%) were the highest for wetweather events. The lowest values for bovine marker occurredat sites 4, 6, and 8 all with 0% during dry events and at site 9 forwet events with 0%.

3.2. Correlation among Temporal Variables. Resultsfrom the temporal analysis of Enterol using Spearman-rank-correlation analysis showed that sites 1, 6, and 7 were positivelycorrelated with the 24-h cumulative precipitation on the day of

Table 1. Correlations with Precipitation and Solar Irradiancea

24-h cumulative precipitation onday of sampling

24-h cumulative precipitation oneday prior to sampling

24-h cumulative precipitation twodays prior to sampling

72-h cumulativeprecipitation

solarirradiance

site 1 Enterol 0.43c 0.12 0.22 0.39c −0.02HF183 −0.09 0.11 −0.04 −0.06 −0.13CF128 −0.10 −0.09 0.02 −0.09 0.06

site 2 Enterol 0.06 −0.05 −0.09 −0.04 −0.12HF183 −0.10 0.32b −0.01 0.07 −0.14CF128 −0.08 −0.11 0.36c 0.10 0.12

site 3 Enterol 0.15 0.08 −0.02 0.27b −0.28b

HF183 0.03 0.30b −0.04 0.11 0.02CF128 −0.11 0.10 −0.18 −0.12 0.14

site 4 Enterol −0.01 0.01 0.00 0.10 −0.20HF183 −0.08 −0.07 0.10 −0.04 0.04CF128 −0.03 0.01 0.33b 0.11 0.08

site 5 Enterol 0.18 0.13 −0.33b 0.10 −0.22HF183 0.00 0.15 −0.13 0.00 −0.24a

CF128 −0.01 0.17 0.38c 0.23b 0.15site 6 Enterol 0.29b 0.06 −0.19 0.16 −0.23a

HF183 0.17 0.12 −0.01 0.17 −0.10CF128 0.12 0.11 0.03 0.14 0.23

site 7 Enterol 0.35b −0.05 0.04 0.28b −0.19HF183 0.00 0.04 0.06 0.04 −0.20CF128 0.22 0.19 0.10 0.28b 0.28b

site 8 Enterol 0.22 0.02 0.05 0.22 −0.14HF183 0.05 −0.07 0.20 0.10 −0.13CF128 0.48d 0.25a −0.12 0.37c 0.15

site 9 Enterol 0.05 0.01 0.10 0.02 0.08HF183 0.18 0.29b 0.01 0.23a 0.34b

CF128 −0.06 −0.10 −0.09 −0.11 0.07aSignificance level (α): a = 0.1; b = 0.05; c = 0.001; d ≤ 0.001.

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sampling (r = 0.43, 0.29, and 0.35, respectively). The previousday 24-h cumulative rainfall had no significant impact onEnterol and the 24-h cumulative rainfall from two days prior tosampling had a negative effect on site 5 (r = −0.33). The 72-hcumulative rainfall positively impacted the levels of Enterol atsites 1, 3, and 7 (r = 0.39, 0.27, 0.28, respectively). Solarirradiance 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 positivecorrelation with the HF183 marker for sites 2, 3, and 9 (r =0.32, 0.30, 0.29, respectively), while only site 8 was statisticallysignificant for CF128 (r = 0.25). The 24-h cumulativeprecipitation on the day of sampling only had a significantcorrelation with the cattle marker at site 8 (r = 0.25); nosignificant correlations were observed for the human marker.No significant correlation was also observed for the humanmarker and the 24-h cumulative precipitation two days prior tosampling, whereas the cattle marker showed significant positivecorrelations at sites 2, 4, and 5 (r = 0.36, 0.33, 0.38,respectively). The 72-h cumulative rainfall had a positivecorrelation at site 9 for HF183 (r = 0.23) and at sites 5, 7, and 8for CF128 (r = 0.23, 0.28, 0.37). Solar irradiance was negativelycorrelated with the human marker at site 5 (r = −0.24). Forsites 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 eventscorrelated with the average of the Enterol, HF183, and CF128assays (Table 2). In wet sampling events, Enterol results weresignificantly correlated with agricultural land use (r = 0.71) andthe number of NPDES facilities (r = 0.63) within the sub-basin.HF183 correlated positively with developed (r = 0.68) andbarren 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 percentcanopy cover (r = −0.70). Agricultural land (r = 0.71), theShreve stream magnitude (r = 0.67), latrine density (r = 0.68),and number of NPDES facilities (r = 0.74) positively correlated

with the CF128 marker. A negative relationship was observedbetween CF128 and the average elevation of the sampling sub-basin (r = −0.80).

3.4. Correlation with Spatial Variables during DrySampling. Spatial analysis showed that dry sampling eventsdid not correlate with Enterol. For the CF128 marker there wasa negative correlation with the number of NPDES facilities (r =−0.67) and a positive relationship with elevation (r = 0.60). Wealso observed a positive correlation of HF183 and samplingsites directly downstream of WWTPs for dry events (r = 0.76).

4. DISCUSSION

Our observation that enterococci concentrations correlate withprecipitation in some of the RGA watershed sites is consistentwith previous fecal bacterial studies conducted in both tropicaland temperate regions.32,33 For example, Santiago-Rodriguez etal.16 observed that rainfall positively correlated with enterococciin most sites within the RGA watershed, except at the inlandlake (site 1). In another study, Eleira and Vogel33 found thatthe previous day’s fecal coliform concentrations and 24 and 168h antecedent rainfall amounts were the best predictors of fecalcoliform levels in the Charles river (Massachusetts, USA).Similarly, in this study we found the 24 h cumulativeprecipitation on the day of sampling and the 72 h cumulativeprecipitation 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 thiseffect. The amount of rainfall prior to sampling combined withthe saturated hydraulic conductivity and evapotranspiration willaffect the amount of runoff. If rainfall prior to sampling hasalready saturated the soil, the time until runoff is produced willbe shortened,34 increasing the microbial transport into thewatershed via runoff. The fact that not all the sites wereimpacted by precipitation in a similar fashion suggests thatthere are several factors that could play important roles in thefate and transport of different fecal bacteria such as proximity ofthe source, saturated hydraulic conductivity, evapotranspiration,and landscape associated with the sources. The positivecorrelations with precipitation strongly suggest that the

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

wet sampling events dry sampling events

variable Enterol CF128 HF183 Enterol CF128 HF183

Shreve 0.54 0.67b 0.31 −0.17 −0.50 −0.28open water 0.13 −0.12 −0.73b −0.13 0.21 −0.53developed −0.02 0.08 0.68b 0.12 −0.31 0.24barren 0.13 0.23 0.84c −0.38 −0.42 0.14forested −0.27 −0.22 −0.92d 0.22 0.38 −0.31agricultural 0.71b 0.71b 0.30 −0.18 −0.21 −0.01grassland 0.33 0.22 0.52 −0.27 −0.50 0.00wetland 0.41 0.41 0.14 −0.55 −0.42 −0.36canopy −0.30 −0.32 −0.70b 0.22 0.55 0.00saturated hydraulic conductivity −0.07 −0.13 0.20 −0.48 0.18 −0.09elevation −0.55 −0.80b −0.52 0.10 0.60a −0.10slope −0.30 0.08 0.63a −0.18 −0.37 0.21number of NPDES facilities 0.63a 0.74b 0.52 −0.02 −0.67b −0.08density of sewers −0.38 −0.20 0.67b 0.18 0.12 0.46density of septic tanks −0.12 0.13 0.78b 0.07 −0.08 0.42density of latrines 0.35 0.68b 0.52 −0.15 −0.38 0.02downstream of WWTP 0.27 −0.09 0.82c 0.46 −0.14 0.76b

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

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resulting pollution was a direct result of stormwater runoff atcertain sites (Table 1).It is reasonable to assume that solar irradiance would be

harmful to fecal bacteria and display a negative correlation within situ fecal bacterial levels. This would clearly be a significantfactor in tropical watersheds. However, in our study solarirradiance was negatively correlated with Enterol at some sitesbut was positively correlated with the human and cattle markerat other sites (Table 1). Such negative correlation has beenobserved in several studies indicating that bacterial die offincreases with an increase in solar irradiance.32,35 The positivecorrelation in dry sampling events could be linked to theoccurrence of algal mats and its impact on bacterial persistencein surface waters. Indeed, the streams in this study are veryshallow, allowing for significant sunlight penetration, resultingin algal growth stimulation. Such correlation has been observedin temperate waters. For instance, a recent study suggested thatalgal blooms in Lake Michigan could be an important source ofEscherichia coli and enterococci.36 The Cladophora algal speciesis common in lakes and streams worldwide,37 and even deadalgal mats have been shown to promote fecal bacterialgrowth.38 The fact that some of the sites exhibited inversecorrelations with solar irradiance for different markers suggeststhat there are different mechanisms of survival rendering solarirradiance a variable with poor predictive power in the studiedwatershed. Our calculations of the average solar irradiance forthe sampling site’s sub-basin using GIS suggest that there areseveral factors that cannot be accounted for when using thisapproach, such as stream depth, canopy cover, and cloud cover.The use of tools such as pyranometers should be investigated inthe future to more accurately determine the importance ofirradiance in survival of fecal bacteria in watersheds.32

Sites 7 and 8 are the only sub-basins within the RGAwatershed associated with land predominantly used foragricultural activities (Supporting Information Table S2).39 Itwould be expected that site 8 would have the highest levels offecal contamination in this watershed since agricultural andurban land dominate the northern section of the RGAwatershed, and all flow terminates at this site. However, thiswas not the case based on enterococci and MST markersduring dry sampling periods. It is plausible that salinity at themouth of the river (site 8) might be contributing to the die-offof these bacterial groups. Indeed, salinity has been shown tohave an inverse relationship with enterococci levels and fecalcoliforms in tropical9 and temperate waters.40 In contrast,during wet weather events, agricultural land use seems to have asignificant impact on fecal pollution in the RGA watershed.Although site 8 is associated with more agricultural activitiesthan site 7, the latter site had a higher incidence of the bovinemarker during wet weather. This suggests that salinity mightalso impact the survival of MST-targeted populations in tropicalwaters.The results indicate that a majority of the fecal pollution in

this watershed is due to loadings from wastewater treatmentplants. The three highest enterococci averages and the highestnumber of positive samples for the human assay were observedat the sampling sites just downstream of the wastewatertreatment plants. The highest enterococci average in drysampling events was the site just downstream of the AdjuntasWWTP (site 4). Moreover, this site had nearly the samepercentage positive for dry (53.3%) and wet (53.8%) samplingevents for the human marker, further suggesting that the fecalbacterial groups detected are coming from the treatment plants.

The data also indicate that regardless of the precipitation (i.e.,wet and dry sampling events), the proximity of sampling sitesto WWTPs had a significant positive impact on the correlationwith HF183.The human marker strongly correlated with barren and

developed land. In addition, we found a positive correlationwith the slope during wet events, although it was not as strongas with developed and barren land. A possible explanation forthe positive correlation between barren land and human markersignals could be that there is little vegetation to abate erosion,and fecal bacteria attached to soil particles are easily transportedto the watershed during rain events. Several studies havediscussed the importance of bacterial attachment to differenttypes of soil and bacterial transport.41−43 For example,Yeghiazarian et al.43 concluded that clay soils contribute moreto contamination events than sandy soils due to microbialattachment to clay particles and subsequent transport withsediment. The association of the human marker with developedland might be due to a higher density of septic systems andsewer 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 Aginginfrastructure and combined sewer overflows are known to beimportant sources of fecal contamination in temperateregions.45,46 This could also be a significant problem in PuertoRico as approximately 40% of the population is eitherconnected to an individual sewage treatment system or notconnected at all.44

The negative relationship of the enterococci densities withthe percentage of canopy cover and forested land use may beassociated 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 Mexicofound that the total apparent interception loss was 17% ofannual rainfall for mature forests and 8% for secondaryforests.48 With the reduction of rainfall intensity due tointerception, the impact on manure and soil particles will alsobe reduced and fewer particles will be transported.49 Addition-ally, there are fewer livestock and human populations in theforested areas of the watershed. Therefore, we would expect tosee less of an impact from heavily forested areas of thewatershed. Our findings are supported by similar observationsin other studies conducted in the tropics9 and in temperateregions.50 These data suggest that watershed segments nearbydensely forested areas associated with relatively low numbers ofwildlife (which is the case in Puerto Rico) might not need to bemonitored as intensively as developed areas. However, futurestudies should be conducted in tropical settings with higheranimal density to better understand the role of canopy cover onthe 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 free-range livestock or animal farming operations in the mountain-ous areas of the watershed. Very few cattle were spotted at sites1 and 2, which are the sampling sites with the highestelevations. However, there was also a positive correlationbetween the cattle assay and elevation during dry samplingevents (r = 0.60), and unexpectedly, the cattle markercorrelated with sites downstream of NPDES facilities (r =0.74). While the impact of cattle having direct access to thewater in some sites might explain such correlations, it should benoted that the cattle marker has been shown to exhibit somelevel of cross-reactivity with other sources.51−54 In the study by

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Toledo-Hernandez et al.,15 the CF128 marker had a 64%specificity when tested against 66 cattle fecal DNA extracts andthat it cross-reacted with swine, turkey, and chicken. Shanks etal.54 reported that the CF128 marker cross-reacted withchicken, swine, dog, and duck fecal samples and had a 76%specificity when challenged against 175 fecal samples from 24animal species.Where there is open water (i.e., reservoirs), there was a

negative correlation with the human marker. A possibleexplanation is that the reservoirs (e.g., Dos Bocas) are actingas a sink for the bacteria. It is possible that the way in whichthese reservoirs are managed could have an influence on thefecal bacterial levels. If the water is held for an extended period,the bacteria will settle to the bottom of the reservoir. Duringintense or prolonged rain events, water will need to be releasedfrom the reservoir, carrying bacteria as well. It has been shownthat stream and reservoir beds serve as sinks when microbes aresettled, only to become sources when they are resus-pended.55−57

By analyzing the positive correlations of the fecal markerswith explanatory variables, we can create a hot spot map tovisualize the areas that should be monitored for fecalcontamination (Figure 2). Where there is a certain land use,or an anthropogenic source that correlated positively with thefecal markers, the stream segments downstream of thisparticular land use or source should be monitored moreclosely. Due to the data resolution for some specific variables,the entire watershed would be considered a continuous hotspot, or fecally impacted. This is the case with the septic, sewer,and latrine density variables. We were only able to obtainpopulation estimates using each of these variables for the sub-basins. Future studies need to determine the exact location ofseptic and latrines in order to better identify specific monitoringpoints. Due to this limitation, in this study we used fineresolution data, such as land uses, soils, and point source data tohighlight the most impacted areas in this watershed. These

maps can help policy makers visualize impacted areas ofstreams that would require specific monitoring to help in theidentification 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 andintensity, topography, soil properties, and land cover and usewith data on occurrence of general, human, and cattle fecalmarkers. From this information, using GIS we generated hot-spot maps of sources of fecal contamination. We suggest thathot-spot mapping is a powerful technique to more efficientlyprocess a large amount of information and to present theresults in a visually compelling form that can be used forwatershed management and decision-making. Additionally, weprobed the geographic stability of two host-specific assaysdeveloped for temperate regions. We found that these assaysapplied in the tropics produce results that are inconsistent withstudies conducted in temperate regions. This suggests thatassays need to be tested for host-specificity in a given regionprior to being used in MST studies. The data also suggest thatthere is a need to develop additional assays that are compatiblewith host-specificity and host-distribution profiles of sourcetracking assays in order to accurately detect fecal sources intropical waters. Environmental, landscape, land use, andhydrological factors associated with each watersheds need tobe taken into consideration when using MST tools inenvironmental monitoring applications.

■ ASSOCIATED CONTENT

*S Supporting InformationFour tables and one figure. This material is available free ofcharge via the Internet at http://pubs.acs.org.

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

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■ AUTHOR INFORMATIONCorresponding Author*E-mail: santodomingo.jorge@epa.gov (J.W.S.D.); yeghialt@ucmail.uc.edu (L.Y.).NotesThe authors declare no competing financial interest.

■ ACKNOWLEDGMENTSThe U.S. Environmental Protection Agency, through its Officeof Research and Development, partially funded and collabo-rated in the research described herein. It has been subjected tothe Agency’s administrative review and has been approved forexternal publication. Any opinions expressed in this paper arethose of the author(s) and do not necessarily reflect the viewsof the Agency; therefore, no official endorsement should beinferred. Any mention of trade names or commercial productsdoes not constitute endorsement or recommendation for use.We thank Thomas Adams of the National Weather Service forproviding the NEXRAD precipitation data. H.R. was therecipient of a National Research Council Senior ResearchFellowship.

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