Fate and Transport Modeling of Potential Pathogens: The Contribution From Sediments1


  • 1

    Paper No. JAWRA-07-0184-P of the Journal of the American Water Resources Association (JAWRA). Discussions are open until August 1, 2009.

(E-Mail/Dorner: sarah.dorner@polymtl.ca)


Abstract: Escherichia coli was used as a bacterial tracer for the development, calibration, and validation of a watershed scale fate and transport model to be extended to a suite of reference pathogens (Cryptosporidium, Giardia, Campylobacter, E. coli O157:H7). E. coli densities in water and sediments from the Blackstone River Watershed, Massachusetts, were measured at three sites for a total of five wet weather events and three dry weather events covering three seasons. The confirmed E. coli strains were identified by ribotyping for tracking the sources of E. coli and for determining the association of downstream E. coli isolates with isolates from upstream sediments. A large number of downstream samples were associated with upstream sediment sources of E. coli. E. coli densities ranged from 71 to 6,401 MPN/100 ml in water samples and from 2 to 335 MPN/g in sediments. Pearson correlation analysis revealed significant correlations between E. coli and total coliforms in water (r = 0.777, p < 0.01) and sediments (r = 0.728, p < 0.01). In addition, E. coli concentrations in water were weakly correlated with sediment particle size and sediment concentrations (r = 0.298, p < 0.01). A hydrologic model, WATFLOOD/SPL9, was used to predict the temporal and spatial variation of E. coli in the Blackstone River. The rapid rise of stream E. coli densities was more accurately predicted by the model with the inclusion of sediment resuspension, thus demonstrating the importance of the process.


Concerns of microbial contamination of waters have increased as a result of documented waterborne disease outbreaks (Leclerc et al., 2002). Impairment of water quality due to the presence of pathogens is typically assessed by monitoring for microbial indicators such as total coliforms, fecal coliforms, and Escherichia coli. Indicators have been associated with enteric pathogens present in water from human or animal fecal contamination (e.g., Hörman et al., 2004). However, the reliability of the use of indicators to predict pathogens has been challenged by some studies (Hegarty et al., 1999; Schets et al., 2005). As such, researchers have been developing new methods for the direct detection of pathogens in water samples (e.g., Guy et al., 2003) and have been studying pathogen fate and transport characteristics (e.g., Jiménez et al., 1989).

Most investigations into pathogen occurrence in watersheds have focused only on the water column and have seldom considered interactions with sediments. Sediments can affect the transport and survival of microorganisms in natural waters in several ways: (1) sediments at the bottom of rivers, lakes, and estuaries may serve as reservoirs of viable indicator bacteria and enteric pathogens; (2) sediments and sediment particles can affect concentrations and transport processes of pathogens in the water column – they can be adsorbed to sediment particles and settle out of the water column more rapidly. These are important processes for microbial transport and removal in waters.

The types of particles have a great impact on the settling velocity. Generally, denser inorganic particles will settle out more quickly than others. The microbial adsorption to settleable particles is different for different types of microorganisms and the adsorption behavior of each organism changes between dry and wet weather (Characklis et al., 2005). The release of bacteria during sediment resuspension caused by storms, flood, tides, or strong winds will result in high concentrations of bacteria in the water column (Muirhead et al., 2004; Jamieson et al., 2005). Lee et al. (2006) reported that the peaks of E. coli and enterococci levels in water and sediment were consistent with storm activity in the beaches which were exposed to fecal contaminants. Studies have shown that indictor organisms have a tendency to survive longer in sediments than in natural water (Burton et al., 1987; Davies et al., 1995). The persistence of pathogens in sediments may create a longer period of risk to public health.

The development of computer-based simulation models are needed for the characterization of the variability of emerging microbial contaminants in relation to watershed conditions. Recently, many process-based models for fecal indicator bacteria and/or pathogens have been created and include hydrologic (Ferguson et al., 2005; Hayden and Deletic, 2006, 2007), hydrodynamic (McCorquodale et al., 2004; Kashefipour et al., 2006; Bai and Lung, 2006) and simple reservoir models (Antenucci et al., 2005). Most models have been process-based, although stochastic approaches have also been proposed (Yeghiazarian et al., 2006). Identified research needs for an improved understanding of microbial contaminant fate and transport include: (1) the acquisition of experimental data, (2) better quantification of potential sources of microbial contaminants, (3) improved understanding of the effects of particulates, (4) characterization of the relation of microorganism fate and transport parameters to environmental variables, (5) the evaluation of tracers with similar transport behaviors, and (6) the development of process-based models (Pachepsky et al., 2006).

In this study, we investigated the microbial contamination in sediment and water samples from the Blackstone River Watershed. The objective was to illustrate the role of sediments in the fate and transport of potential pathogens during wet weather events. The specific goals were: (1) to quantify E. coli densities and identify E. coli associated with bed sediments as sources of downstream bacterial contamination, and (2) to improve predictions of microbial fate and transport by including sediment resuspension in a hydrologically based microbial contaminant fate and transport model. The research approach involved wet weather and dry weather sampling, identifying E. coli ribotypes present in samples, and augmenting the WATFLOOD model to include processes of sediment resuspension and loading from urban, impervious surfaces.

Watershed and Land Use Characteristics

The area of this study is the Upper Blackstone River Watershed (Figure 1), a subwatershed of the Blackstone River encompassing an area of 186.7 km2 (72.1 mi2). The Blackstone River originates in the Worcester hills in central Massachusetts, and flows southeasterly into Rhode Island, discharging eventually into Narragansett Bay. It is known as the “Birthplace of the American Industrial Revolution.” With a steep gradient [the river drops 134 m (438 ft) over 74 km (46 miles)] the hydraulic potential of the river was first tapped by Samuel Slater who built a mill at the outlet of the Blackstone in 1793. Others followed, which led to one dam for nearly every mile of the river (Shanahan, 1994; BRNHC, 2006). The industrial history and population density have contributed to numerous water quality issues that include urban runoff in the headwaters where the population density is greatest, and contaminants such as nutrients, toxics, and heavy metals trapped in sediments behind the many impoundments.

Figure 1.

 Map of Massachusetts With Study Area in the Blackstone River Watershed. Sample sites: (1) BS01, USGS streamflow gauging site at Millbury and downstream extent of Upper Blackstone River; (2) MR02, Middle River; and (3) BS04, upstream of wastewater discharge from the Upper Blackstone Water Pollution Abatement District.

Another potential source of water quality impairment is the Upper Blackstone Water Pollution Abatement District, a 212 million liter per day (56 million gallons per day) activated sludge treatment plant serving the city of Worcester and surrounding communities. The facility treats on average approximately 146 million liters per day (38.5 million gallons per day) with average reported effluent concentrations of 5.0 mg/l of biochemical oxygen demand and 7.2 mg/l of suspended solids (UBWPAD, 2006).

Most of the Blackstone River Watershed is covered by till and sand and gravel (glacial outwash) deposits. The upland areas are predominantly covered by till, which covers approximately 71% of the watershed and stream valleys are generally underlain by stratified, well-sorted sand and gravel deposits (Barbaro and Zarriello, 2006). Land use in the Upper Blackstone River Watershed is 41.5% forest, 29.9% residential, 8.7% open land, 4.4% industrial, 4% water, 3.6% commercial, 3.6% transportation, 3.5% agricultural, and 0.8% wetland. The entire Blackstone River Watershed is urbanized with a population density of approximately 386 people/km2 (1,000 people/mi2) based on the 2000 census data (USCB, 2002; Mangarillo, 2006).

Hydrologic and Water Quality Information

Monthly mean discharge in the Blackstone River at the outlet of the Upper Blackstone River Watershed (at the USGS gauging station 01109730 in Millbury, Massachusetts) (lat 42°11′20′′N long 71°45′56′′W) for the period of study (September 2005 to November 2006) ranged from 2.09 m3/s [73.9 cubic feet per second (CFS)] in September 2006 to 16.9 m3/s (596.9 CFS) in October of 2005. Peak streamflow occurred on October 15, 2005 where gauge height reached 3.584 (11.76 ft) and the daily mean discharge was 127 m3/s (4,500 CFS). The Blackstone River at the Millbury, Massachusetts station has an average depth of approximately 1 m, channel width of 18 m, and average velocity of 0.76 m/s (USGS, 2008).

Sediment and surface water samples were collected from three sites – MR02, BS04, and BS01 (Figure 1) for five wet weather events (November 2005, May 2006, June 2006, October 2006, and November 2006) and three dry events (September 2005, September 2006). Total suspended solids (TSS) for the dry weather events ranged from below the detection limit (2.50 mg/l) to 4.60 mg/l at Site BS04. For wet weather events at Site MR02, TSS ranged from 4.0 mg/l to a maximum of 17 mg/l with a mean of 8.3 mg/l. At Site BS04, TSS ranged from 3 mg/l to 46 mg/l with a mean of 13.7 mg/l. Site BS01 had TSS samples that ranged from below the detection limit to 106 mg/l with a mean of 18.8 mg/l.

A wastewater treatment plant is located immediately downstream of Site BS04. At times, sediment samples were not collected at specific sites due to excessive flooding. Composite sediment samples were collected up to a depth of 3 cm using long glass pipettes (May 2006 event) and subsequently using sterile syringes (September 2006, October 2006, and November 2006 events) and placed in small sterile bottles. The composite samples were created by combining five samples for each site to account for spatial variability. All samples were placed on ice in a cooler and immediately sent for analysis. Analysis was done within four hours of sample collection.

Bed sediments were characterized at the three sites with regards to particle size and were determined to be predominantly coarse sand (0.5-1 mm) to very fine gravel (2-4 mm) (Wu, 2007). E. coli was enumerated using the Colilert method (IDEXX, Atlanta, Georgia). Water samples were diluted with PBS buffer (phosphate buffered saline pH = 7.2) by 10 and 100 times except for the samples collected on May 12, 2006, which were diluted by 100 and 1,000 times. The Colilert media and 100 ml of the diluted samples were placed into vessels and mixed. The mixtures were poured into Quanti-Tray®/2000, sealed in a Quanti-Tray® Sealer (IDEXX, Atlanta, Georgia) and incubated for 24 hours at 35 ± 0.2°C. Sediment samples were analyzed according to the method presented by Craig et al. (2002). Twenty-five grams of sediments (wet weight) were placed into 75 ml of 0.1% peptone solution. Sediments were shaken and resuspended in solution to separate bacteria from the particles. Ten milliliters of the resuspended solution was placed into vessels and diluted 10 and 100 times. An additional 10 ml were used for measurement of both wet and dry weights. The E. coli densities in the sediments are reported per dry weight throughout this study. During the experiments two control strains were used: (1) E. coli ATCC 29194 (Remel Europe, Ltd., Dartford, United Kingdom), as a positive control, and (2) Klebsiella pneumoniae ATCC® 33495TM (Quality Technologies Ltd, Newbury, California) as a negative control.

To isolate E. coli from the Quanti-Trays, a syringe was used to remove 0.5 ml liquid from the fluorescent wells. The liquid was transferred to a tube with 10 ml PBS buffer, mixed, and serially diluted three times at 10−1, 10−2, and 10−3, as described by Eckner (1998). Then, 0.1 ml liquid was spread onto the Luria-Bertani agar (LB-agar) plates. After half an hour, plates were inverted and placed in an incubator at 35°C for 12-24 hours (depending on their growth status, allowing for a colony to grow to at least 1 mm in diameter for inoculation). A colony from the plate was visually selected and placed in a test tube containing inverted Durham tubes with EC broth medium as described by Feng and Hartman (1982). The tubes were put into an incubator at 35°C for 24 hours. The tubes with gas production were then observed under UV light at 365 nm, to identify tubes with fluorescence to confirm the presence of E. coli. Subsequently, 0.1 ml of liquid was extracted from the positive tubes and diluted 10 and 100 times in PBS buffer. The diluted liquid (0.1 ml) was then spread on an LB agar plate. The plates were incubated at 35°C for 12-24 hours. A single colony was used to inoculate an LB agar tube. After inoculation, the LB agar tube was incubated at 35°C for 12 hours until E. coli growth was visible with the naked eye. The tube (E. coli slant) was removed from the incubator, sealed with paraffin to prevent microbial contamination and stored at 4°C.

The ribotyping was carried out by the New York City Department of Environmental Protection (NYC DEP) using the RiboPrinter® Microbial Characterization System (DuPont Qualicon, Inc., Wilmington, Delaware). This system uses Riboprint pattern similarities generated by restriction fragment length polymorphisms of 16S ribosomal RNA genes from sampled E. coli and compares them with patterns of other E. coli from known hosts in the DuPont Identification library and a custom library. Upon arrival at the NYC DEP E. coli isolates were sub-cultured into Brain Heart Infusion agar. Escherichia coli was heat lyzed to make the sample available for processing and the DNA was digested by either one or two restriction enzymes. In this study, restriction enzymes EcoR1 and PvuII were used. DNA was separated by size on pre-cast agarose gels via electrophoresis. The DNA and fragments were subsequently transferred to, and immobilized on, a nylon membrane. On each membrane, denatured DNA fragments were hybridized with chemically labeled E. coli rRNA. The membrane was then rinsed and treated with blocking buffer and an anti-sulfonated DNA antibody/alkaline phosphatase conjugate. Unbound conjugate was removed and ultimately a chemiluminescent substrate was used. The procedure allowed each electrophoretic band containing the rRNA genes’ genetic information, to be observed by the custom camera in the RiboPrinter® system. Images from the membrane were captured, digitized, and then transferred to the workstation where it is processed through a series of proprietary algorithms (RiboPrinter® Data Analysis User’s Guide, 1999). The ultimate output was a set of RiboPrint® patterns for each isolate. The bacteria were ultimately identified based on how similar the sample patterns were to the source specific patterns in the Custom Identification library. Similarity is assumed according to band position, weight, and intensity. Isolates were only categorized into the same ribogroup if their genetic (riboprint) pattern matched other riboprints by 90% or greater similarity (Price et al., 2002).


Microbial fate and transport modeling was performed using modeling code developed by Dorner et al. (2004, 2006). The fate and transport model was linked with the WATFLOOD/SPL9 modeling system for simulating watershed hydrology. The distributed hydrologic model uses a grouped response unit approach for watershed representation and simulation. A detailed description of WATFLOOD/SPL9 is available elsewhere (Kouwen and Mousavi, 2002; León et al., 2001). The microbial fate and transport model included routines for simulating overland transport, movement through soil layers, and channel routing and sedimentation of free and attached microorganisms. The model was developed for simulating and predicting peak pathogen concentrations and was first tested on a predominantly agricultural watershed (Dorner et al., 2006). Dorner et al. (2006) hypothesized that including the process of sediment resuspension would improve the correlations between simulated and observed results because the rapid rise of E. coli during storm events were most likely from in-stream sources.

The Dorner et al. (2006) was therefore upgraded to improve simulations of the processes governing sediment resuspension during periods of increased streamflow. Resuspended microorganisms were estimated using a simple power function


where R is the density of resuspended microorganisms (#/m3), Q is streamflow (m3/s), and a and b are calibration parameters.

Sediment sources were assumed to be unlimited based upon the sediment monitoring results and thus resuspension was modeled as a function of streamflow and not sediment concentration. Fate and transport processes and model parameters (see Table 1) are described in greater detail by Dorner et al. (2006); however, the fraction of E. coli sorbed to flocs was changed to 50%, which is in accordance with modeling studies by Ferguson et al. (2007). In addition, a base loading function was added to simulate the base load during dry weather. The land surface load was selected based upon estimates obtained by Dorner et al. (2004, 2006) and the observation that an urban watershed had similar E. coli densities as an adjacent agricultural watershed (Dorner et al., 2007).

Table 1.   Primary Parameters Used in the Microbial Fate and Transport Model.
  1. *Calibrated parameter.

Inactivation in soil, ks (l/h) 5 × 10−3E. coli inactivation in soil for spring and fall temperatures
Inactivation in water, kw (l/h)2 × 10−2E. coli inactivation in water for spring and fall temperatures
Erodibility, d (#/joule)4 × 10−3*Number of microorganisms detached from the soil surface as a result of energy from raindrops and overland flow
Free-floating settling velocity, vs (m/h)1 × 10−4Settling velocity of unattached E. coli
Attached settling velocity, vsa (m/h)2 × 10−1Settling velocity of E. coli that are attached to flocs
Fraction of attached microorganisms, Fa (percent)50%Fraction of E. coli that are attached to flocs
a, b100; 4.5*Parameters for estimating E. coli resuspended from bed sediments
α1, α2, α3 (unitless)8 × 10−3; 8 × 10−3; 8 × 10−3*Parameters describing flow of E. coli through the soil, with interflow or artificial drainage systems and to deeper ground water
Base load, Lb (#)3 × 108*Number of E. coli that are added to the base flow in each grid for the time step (time step = 1 hour)
Wastewater load, Lww (#)1 × 101Number of E. coli that are added to the stream below the wastewater treatment plant each time step (time step = 1 hour)
Land surface load, Ls (#/ha)1 × 1018Number of E. coli that are deposited on the land surface in the watershed

ArcGIS 9.2 (ESRI, 2006) software was used for GIS analysis for preprocessing of the hydrologic model input data files. The base maps (shapefiles), including the Blackstone River Watershed were acquired from MassGIS. The elevation data needed to simulate the water balance were acquired from Digital Terrain Model downloaded from MassGIS website (http://www.mass.gov/mgis/). The studied area was divided into 226 grids, each with an area of 1 km2. In each grid, the elevation of the center was assumed to represent the elevation of the grid. The slope, contour, and aspect were calculated using the Spatial Analyst tools in ArcGIS. The flow direction was calculated using the hydrology function in the Spatial Analyst tools and used to create input files for the WATFLOOD/SPL9 model.

Land use data for the Blackstone River Watershed was also obtained from MassGISA total of 17 distinct land uses exist within the modeling area. The 17 land uses were then classified into the following categories each having unique hydrologic parameters for the model: urban impervious, water, wetland, forest, cropland and pasture, urban open. Hydrologic parameters in the WATFLOOD/SPL9 model are primarily a function of land class.

The hydrological component of the model was calibrated first using the month of November 2005 for calibration and other months for validation. Hourly hydrometric data from USGS gauging station 01109730 at Millbury (lat 42°11′20′′N long 71°45′56′′W) were used for model calibration and validation. Watershed hydrology was simulated for months that included events sampled for E. coli and other water quality parameters. Hourly precipitation and air temperature data were available from the National Climate Data Center for Worcester airport, located within the upper Blackstone River Watershed (lat 42°16′N long 71°53′W). Model calibration was performed by trial and error, varying parameters one at a time to reduce the error between observed streamflow and model predictions. The hydrologic component of the model was calibrated prior to the calibration of the pathogen transport model. Parameters for the microbiological model were then also calibrated for the November 2006 event and validated using results from other events.

Results and Discussion

According to the monitoring data, E. coli densities at the three sites ranged from 71 to 6401MPN/100 ml in water samples and from 2 to 335 MPN/g in sediments (dry weight). The spatial and temporal variation of E. coli in sediments is demonstrated in Figure 2. Escherichia coli densities increased to high levels during storms and returned to baseline levels during dry weather. In water samples, peak E. coli concentrations corresponded to the peaks in streamflow (Figure 3). Pearson correlation analysis indicated that E. coli concentrations in water were weakly correlated with sediment E. coli concentrations (r = 0.298, p < 0.01). There existed significant correlations between E. coli and total coliforms in water (r = 0.777, p < 0.01) and sediments (r = 0.728, p < 0.01). Correlations between E. coli and total coliforms are expected as total coliforms include E. coli and often correlate well with each other (Brookes et al., 2005; Ferguson et al., 1996). E. coli densities were higher at the two upstream locations (MR02 and BS04) as compared with the downstream location (BS01, below the discharge of the wastewater treatment plant). Spearman correlations between E. coli densities in sediment and particle size are provided in Table 2. Only significant correlations with very fine sand (p < 0.05), and highly significant correlations with silt and clay (p < 0.01) were observed.

Figure 2.

 Temporal and Spatial Variation of E. coli in Sediment Samples.

Figure 3.

 Daily Streamflow and E. coli Densities in the Water Column at Site BS01 (Millbury Station).

Table 2.   Correlations Between E. coli Densities in Sediment and Particle Size.
 2 mm %1 mm %0.5 mm %0.25 mm %0.1 mm %0.053 mm %Silt and Clay %
  1. *Significant at 0.05 level (two-tailed).

  2. **Significant at 0.01 level (two-tailed).

E. coli in sediment

Sixty-two E. coli isolates (the total number of isolates was 71) were analyzed by the riboprinting system using restriction enzymes EcoR1 and PvuII. Eleven ribogroup patterns were observed that matched with other isolates (not <2) and 26 ribogroups had no match with another isolate. Of the 62 analyzed isolates, 36 isolates (58%) successfully matched ribogroups of other E. coli collected during this study (Table 3). Isolates were only categorized into the same ribogroup if their genetic pattern (riboprint) matched other riboprints by 90% or greater similarity.

Table 3.   Matched Ribogroups of Escherichia coli.
EcoR1 RibogroupMatched E. coli Isolates
  1. Note: A and B denote two duplicate samples taken at the same time at the same site.

1000-3May 13BS04AWater
May 13BS04BSediment
1000-4May 13MR02AWater
May 13BS04ASediment
107-8May 9BS01AWater
May 12BS04AWater
May 13BS01AWater
October 18BS04AWater
October 18BS04BWater
28-1May 17MR02BSediment
May 17MR02BWater
May 9BS04BSediment
33-4May 12MR02ASediment
May 12BS04BWater
May 13MR02ASediment
34-2May 14MR02AWater
May 12BS01BSediment
34-5November 10BS01ASediment
November 10BS01BSediment
48-8May 14MR02BSediment
May 17BS01AWater
May 17BS01BWater
49-3November 8MR02ASediment
November 8BS04BSediment
November 9BS01ASediment
November 10MR02BSediment
October 19BS01AWater
November 8BS04AWater
November 9BS01BWater
November 9BS01BSediment
64-6October 19MR02BSediment
October 19BS04AWater
November 10BS01BWater
69-7November 13MR02BSediment
May 10BS04BWater
November 9MR02BSediment

E. coli from the upstream site, MR02, shared several of the same ribogroups as E. coli collected from the downstream sites, BS04 and BS01 (Table 3). Matching ribogroups are linked to common sources of E. coli. Ribogroups have also been used to differentiate human from nonhuman sources of fecal contamination (e.g., Parveen et al., 1999); however the objective of this research was to characterize the fate and transport E. coli using ribogroups as a means to track the geographic origins of fecal bacteria and to observe the ensuing transport to downstream locations. Both sediment and water isolated E. coli were found downstream; however, 9 of the 12 E. coli matching with the upstream site (MR02) were from the sediments, demonstrating that sediment source characterization is important for understanding the fate and transport of microorganisms. In some cases, E. coli from MR02 on a given date were matched with downstream E. coli from an earlier date. This difference illustrates the complex transport dynamics in the natural environment and confirms the importance of sediments as a source of E. coli. Additionally, there were some E. coli isolates recovered from Site BS04 that were also found downstream at BS01 (Table 3). Thus, common isolates were found at both upstream and downstream locations on different dates, suggesting a common long term source and transport from upstream sites.

The Blackstone River was historically impaired by intense industrial development and urbanization. Fecal contamination has been documented at numerous locations throughout the Blackstone River watershed. During wet weather, resuspension of contaminated sediments in the river has been shown to be a source of water quality criteria violations (Wright et al., 2001). Our study showed that E. coli densities increased during wet weather events. This trend has been observed in other reports (Jamieson et al., 2005; Dorner et al., 2006). In water samples, peak concentrations of E. coli in wet weather were generally more than an order of magnitude higher than in dry weather. However, the temporal variation of E. coli in sediment and water samples varied between sites. For example, the May 2006 event water samples at Site BS01, had increasing E. coli concentrations from May 9 to May 14, which decreased to baseline levels by May 17. For the same event at Site BS04, E. coli concentrations fluctuated with the peak concentration occurring on May 12. A similar trend was observed at site MR02. In sediment samples at Site BS01, E. coli concentrations were highest on May 13 and decreased to baseline concentrations on May 17. At Site BS04, the concentration of E. coli varied slightly over the course of the event. At MR02, E. coli concentration increased sharply from May 10 to May 14, and decreased to normal by May 17. The difference in temporal variation among sites suggests that the sediments are not the only source of E. coli in the water column. Stormwater runoff may also be transporting fecal bacteria that accumulate in the river during events. Furthermore, variations in flow velocity at the different sites may result in different settling characteristics such as partitioning rates for bacterial deposition.

The correlations between observed streamflow and simulated streamflow at Site BS01 were significant, with results from November 2005 (Figure 4) and October 2006 demonstrating the highest correlations (r2 = 0.90 and 0.92, respectively). For May, September, and November 2006 correlations were highest for November (r2 = 0.90, Figure 5) and lowest for September (r2 = 0.64). R2 for May 2006 was 0.84. The lowest correlation between observed and simulated streamflow was found for the month with the lowest streamflow – September. In September, the streamflow is more heavily influenced by diurnal variations related to the wastewater input to the Blackstone River and is thus not captured as effectively by the WATFLOOD/SPL9 model at this time.

Figure 4.

 Observed and Estimated Streamflow for the Blackstone River at Site BS01 for November 2005.

Figure 5.

 Observed and Estimated Streamflow for the Blackstone River at Site BS01 for November 2006.

Pathogens and indicators exhibit a large degree of natural variability. It is not expected, nor necessary to predict pathogen or indicator concentrations to a level of precision above the order-of-magnitude level. Figure 6 demonstrates simulated and observed E. coli for the November 2006 event. The addition of the process of sediment resuspension improved the model by capturing the rapid rise in E. coli concentrations at the onset of the stormwater event, clearly demonstrating the importance of in-stream sources of the indicator (Figure 6). When the process of resuspension was removed from the calibrated model, the model predictions were not significantly correlated with observations and 88% of simulated values were underestimated. However, as the model was calibrated with the process of resuspension, it is expected that it would not perform as well when the process was removed.

Figure 6.

 Observed and Simulated E. coli for the Blackstone River at Site BS04 for the November 2006 Event.

With the fully calibrated model, for sites BS04 and MR02, the model predictions were significantly correlated at the order-of-magnitude level with observed E. coli measurements (Figure 7), grouping all dry and wet weather events (Spearman rank correlation coefficient = 0.31, p < 0.05). The model did not perform as well downstream of the wastewater treatment plant and combined sewer overflow discharge (Site BS01, Figure 7) as a result of the large variability in observed and simulated results. An improved temporal characterization of E. coli loads from both the wastewater and combined sewer overflow may assist with future predictions for the BS01 site. At times, a disinfectant residual from the wastewater effluent may result in lower densities than are predicted by the model because of higher inactivation of E. coli and at other times, lower treatment efficiency may lead to higher E. coli loading than was estimated.

Figure 7.

 Simulated vs. Measured E. coli at Sites MR02, BS01, and BS04 Along the Blackstone River for Wet and Dry Weather Events.


The ribogroups of E. coli isolates from water and sediment samples demonstrated that a large number of downstream water and sediment samples were associated with upstream sediment samples, which suggested that sediments have an effect on the sources and transport processes of E. coli in the water column. E. coli in sediments have a tendency to increase during wet weather, and sediments were associated with the sources and transport of E. coli in the water column. However, the degree of the contribution of E. coli from sediments during wet weather is yet unclear since both stormwater runoff and the resuspension of the sediment affected the E. coli concentrations in water. The relative importance of land-based vs. sediment based sources of pathogens and indicators is important for the development of source water protection plans, as well as modeling the fate and transport of pathogens at a watershed-scale. The addition of the process of sediment resuspension greatly improved the model’s ability to predict the rapid rise of E. coli concentrations, thereby demonstrating its importance. The continued development of the hydrologic model for pathogen (Cryptosporidium, Giardia, Campylobacter, E. coli O157:H7) fate and transport will assist with the assessment of the relative and absolute contributions of the various sources of microbial contaminants over time.


This research is supported by the U.S. Geological Survey 104b program, and a UMass Faculty Research Grant. This research has been conducted in coordination with sampling and modeling funded by the Upper Blackstone Water Pollution Abatement District.