Hydrologic Modeling of Pathogen Fate and Transport - ACS Publications

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Environ. Sci. Technol. 2006, 40, 4746-4753

Hydrologic Modeling of Pathogen Fate and Transport S A R A H M . D O R N E R , * ,† WILLIAM B. ANDERSON,† ROBIN M. SLAWSON,‡ NICHOLAS KOUWEN,§ AND PETER M. HUCK† NSERC Chair in Water Treatment and Waterloo Hydrology Lab, Department of Civil Engineering, University of Waterloo, Waterloo, Ontario, Canada, N2L 3G1, and Department of Biology, Wilfrid Laurier University, Waterloo, Ontario, Canada, N2L 3C5

A watershed-scale fate and transport model has been developed for Escherichia coli and several waterborne pathogens: Cryptosporidium spp., Giardia spp., Campylobacter spp, and E. coli O157:H7. The objectives were to determine the primary sources of pathogenic contamination in a watershed used for drinking water supply and to gain a greater understanding of the factors that most influence their survival and transport. To predict the levels of indicator bacteria and pathogens in surface water, an existing hydrologic model, WATFLOOD, was augmented for pathogen transport and tested on a watershed in Southwestern Ontario, Canada. The pathogen model considered transport as a result of overland flow, subsurface flow to tile drainage systems, and in-stream routing. The model predicted that most microorganisms entering the stream from land-based sources enter the stream from tile drainage systems rather than overland transport. Although the model predicted overland transport to be rare, when it occurred, it corresponded to the highest observed and modeled microbial concentrations. Furthermore, rapid increases in measured E. coli concentrations during storm events suggested that the resuspension of microorganisms from stream sediments may be of equal or greater importance than land-based sources of pathogens.

Introduction Investigations into the quality of water resources throughout the world have often identified elevated levels or frequent occurrence of indicator and pathogenic microorganisms of fecal origin such as Escherichia coli, Cryptosporidium oocysts, Giardia cysts, and Campylobacter (1-4). In 1993, an outbreak in Milwaukee, WI, involving more than 400 000 cases of gastroenteritis caused by the protozoan parasite Cryptosporidium parvum was associated with the city’s drinking water supply (5). Another outbreak in 2000, in Walkerton, Ontario, Canada, for which the identified causative organisms were the bacteria E. coli O157:H7 and Campylobacter jejuni, resulted in more than 2000 cases of gastroenteritis and seven * Corresponding author phone: (413) 545-1778; fax: (413) 5452304; e-mail: [email protected]. † NSERC Chair in Water Treatment, University of Waterloo. ‡ Wilfrid Laurier University. § Waterloo Hydrology Lab, University of Waterloo. 4746

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deaths (6). Both of these outbreaks followed periods of intense precipitation, as have others (7, 8), suggesting a mechanistic linkage between watershed hydrology and waterborne disease outbreaks. Potential waterborne pathogenic microorganisms include bacteria, fungi, viruses, protozoa, and helminths (9). Others, such as prions (e.g. the pathogen causing bovine spongiform encephalopathy), have not been ruled out as being capable of causing disease through waterborne transmission, although initial estimates suggest negligible risk (10). Cryptosporidium has largely been the pathogen driving drinking water treatment regulations, particularly with respect to disinfection/inactivation, because of its resistance to conventional treatment (including chlorine disinfection) (11, 12) and its role in waterborne disease outbreaks around the world (13). The magnitude of pathogen densities are needed for the design and operation of water treatment plants. Zoonotic pathogens cause 75% of the emerging infectious diseases in humans, many of which can be transmitted indirectly through water or environmental contamination (14). Zoonotic waterborne pathogens will continue to be recognized as an increasing public health concern worldwide because of a number of driving forces that include climate change and severe weather events, changing patterns in water use, livestock operation intensification, and the globalization of trade in animal products (9). Jamieson et al. (15) provide a review of microbial modeling approaches at the watershed scale; however, most models have examined only fecal indicators, which are frequently not correlated with pathogenic microorganisms (e.g. ref 1). Others have modeled pathogens such as Cryptosporidium directly but have not yet included all important processes and pathways (e.g. ref 16).

Theoretical Basis and Calculations Study Area. Canagagigue Creek Watershed, Ontario, Canada, was selected for the development of a distributed hydrologically based pathogen fate and transport model. Canagagigue Creek, a tributary of the Grand River, drains an agricultural region of approximately 130 km2. The water supply in the Grand River Watershed (population of approximately 800 000) is from both groundwater and surface watersin the northern region, groundwater is used exclusively. In the central and southern regions, downstream of Canagagigue Creek, three water treatment plants draw water from the Grand River. Long-term average annual precipitation in the Grand River Watershed ranges from 892 to 940 mm. Average annual snowfall ranging from 101 to 169 mm as rainfall equivalent (17) is included in the precipitation values. The quaternary geology of the Canagagigue Creek Watershed consists primarily of till (silty clay to clay matrix) but also has glaciofluvial outwash deposits of gravel and sand, as well as glaciolacustrine deposits of clay and silt (18). Clay loam is predominant in the northern region of the watershed. In the central region, loam is the predominant soil type. The southern and southeastern regions can be characterized by moraine deposits of very fine sand and fine sandy loam (19). Approximately 60% of the agricultural land in Canagagigue Creek has subsurface tile drainage. Canagagigue Creek also receives treated wastewater (with filtration and UV irradiation, 4157 m3 per day in 2003) from a town with a population of 7848 receiving wastewater treatment (20). Estimating the Environmental Loading of Pathogens. Unlike other forms of nonpoint source contaminants, such as sediment and nutrients, the presence of pathogenic 10.1021/es060426z CCC: $33.50

 2006 American Chemical Society Published on Web 07/01/2006

TABLE 1. Mean First-Order Inactivation Constants of Select Microorganisms in Manure, Soil and Manure Amended Soil, and Water (K day-1) season winter spring/fall summer

medium manure soil/manure water manure soil/manure water manure soil/manure water

Cryptosporidium 10-2

1× 1 × 10-2 1 × 10-2 3 × 10-2 3 × 10-2 1 × 10-2 4 × 10-2 6 × 10-2 6 × 10-2

microorganisms is related to the epidemiology of their host populations and is therefore more transient in nature and may be clustered in space and in time (e.g. ref 21). The peak occurrence of pathogens in streams will be linked to the numbers that are shed by infected populations as well as the locations of those populations within the watershed. A probabilistic pathogen loading model (PPLM) based on animal prevalence and shedding intensity data was developed to estimate the generation of the selected pathogens (22, 23). The PPLM was used to estimate the mean daily potential environmental loading per hectare of E. coli, E. coli O157:H7, Campylobacter spp., Cryptosporidium spp., and Giardia spp. for Canagagigue Creek. To model the inactivation of microorganisms once released into the environment, the literature was examined to estimate inactivation rates through the combination of model fitting of published data and recording of previously reported inactivation rates (see Supporting Information). Generally, pathogen inactivation has been modeled as a firstorder process (e.g. ref 24, 17); thus a first-order lumped parameter representing all processes of inactivation was calculated for each pathogen. Mean inactivation rates based on temperature were calculated for each season (winter, e4 °C; spring/fall, >4 °C e 15 °C; summer, >20 °C) for manure, soil and manure amended soil, and water (Table 1). Data for Cryptosporidium and Giardia inactivation were available for unsterilized Grand River water (25). Where data were unavailable for a given medium or temperature range, best estimates were made on the basis of available data for other media or temperature ranges. For example, missing values for inactivation in manure were estimated using available values for inactivation in soil and manure amended soil, or if no data were available for winter, spring/fall inactivation rates were used. The variability among reported inactivation rates is large, and differences as a function of temperature are frequently not significant (P > 0.05). The daily loading of pathogens to manure in storage was estimated on a mass basis, rather than a concentration basis, to avoid the need to keep track of changes in the manure volume (see Supporting Information). The pathogens from livestock sources were assumed to be randomly distributed across the watershed, but associated with land class and manure application times. On the basis of observed practices in the watershed, manure application for a given region was more likely to occur in the spring and the fall; however, low levels of manure spreading also occurred in the winter and summer. Mass loadings from the wastewater effluent were based upon reported mean pathogen densities in wastewater from the literature (e.g. ref 26-30). Watershed Data and Hydrology. The WATFLOOD modeling system was augmented for pathogen fate and transport. WATFLOOD is a distributed model that uses a grouped response unit (GRU) approach for watershed representation and simulation. A detailed description of WATFLOOD is

Giardia

Campylobacter

E. coli (O157)

3 × 10-1 5 × 10-2 5 × 10-2 2 × 10-2 2 × 10-1 1 × 10-1 1 × 10-1

4 × 10-1 8 × 10-2 8 × 10-2

2 × 10-1 1 × 10-1 4 × 10-1 2 × 10-1 6 × 10-2 6 × 10-2

available elsewhere (31, 32). The Canagagigue Creek Watershed was modeled using 1 km × 1 km grids. A digital elevation model (33) was used to estimate slopes and drainage directions for each grid in the Canagagigue Creek Watershed using ENSIM. The percentages of individual land classes in each grid were calculated from a classified land use image created from four separate LANDSAT MSS images acquired by the sensor in 1989 (resolution of 80 m). The classification contained five land classes: forest, agricultural, wetland, urban, and water. The agricultural land class was further subdivided into two classes: agricultural with tile drainage systems and agricultural without tile drainage systems. Artificial drainage maps (34) were used to determine the percentage of tile drained agricultural land. Only land classified as agricultural was assumed to be tile drained. Tile drainage systems were modeled as the interflow component of the water balance generated by WATFLOOD. Radar data collected by Atmospheric Environment Service’s (AES) King City Weather Radar Research Station in Southwestern Ontario were used to generate hourly precipitation input files. Rain gauge data from two locations near the Canagagigue Creek Watershed (35, 36) were used for periods of missing radar data, in addition to temperature and snow course data (35). Streamflow and reservoir release data were obtained from the Grand River Conservation Authority and Water Survey Canada. The location of sewage treatment plants and streamflow gauges were obtained from the Grand River Conservation Authority as GIS vector data (37). The WATFLOOD model had previously been calibrated for the Grand River Watershed (including Canagagigue Creek) and another watershed in Southwestern Ontario (32). To facilitate the calibration process, parameters from the previously calibrated models were initially selected to ensure that the correct parameter ranges were used for hydrologic conditions typical of Southwestern Ontario. The revised calibration for Canagagigue Creek was necessary because of the inclusion of tile drainage in the model and a smaller grid size. Parameters were then calibrated for 2002-2003 in a trial and error manner to fit the observed hydrographs. The parameters for the pathogen transport subroutine were calibrated for E. coli in a trial and error manner following the calibration of the hydrologic model. A total of 99 E. coli observations were available for model calibration (June 2002May 2003, n ) 32) and validation (June 2003-May 2004, n ) 67). Pathogen Transport Model. The pathogen transport model presented here is an original model developed by Dorner (22). As data regarding the transport of microorganisms in the environment have been limited, it is useful to draw analogies to other types of contaminants. An analogy is the soil particle, eroded from the soil surface and transported overland to the stream. Pathogens such as VOL. 40, NO. 15, 2006 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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Cryptosporidium are similar in size to silt and are subject to the same mobilizing forces; however their supply is limited, they are highly transient in space and time, they will be inactivated, and have a lower specific gravity.

Using a similar approach, the concentration of microorganisms in the Upper Zone as a function of time was

Overland Transport. As pathogen detachment from the soil surface is generally analogous to soil erosion and sediment transport, the simple process-based model by Hartley (38) can be used to estimate potential detachment of pathogens from the soil surface and transport with overland flow. The Hartley model was selected because it models erosion and sediment transport from single storm events, in contrast to soil erosion models such as the USLE (39), which was designed to model average annual erosion losses from agricultural fields. Also, it includes the effects of vegetation and canopy cover, which could be used to investigate effects of land cover changes.

R2qint + R3qdrn R1qinfCss + exp + ks t R2qint + R3qdrn + ksUZS UZS R1qinfCss Cuzo (2) R2qint + R3qdrn + ksUZS

The advantage of using such a model is that processes such as overland flow rate will be considered in the model of oocyst detachment. In contrast, the simple linear lumped parameter model developed by Bradford and Schijven (40) does not account directly for changes in shear forces due to changing flow rates. No process-based model exists for the detachment of microorganisms from the soil surface and entrainment with overland flow, and no experimental data have been available to validate the Hartley model for microbial transport. Only the agricultural land classes (tile drained and nontile-drained) are assumed to potentially have a pathogen supply at the land surface. This assumption was reasonable for the Canagagigue Creek Watershed, since the predominant land use activity was agricultural. This may not be the case for other types of watersheds that are more pristine or heavily urbanized. Movement of Pathogens through the Soil. WATFLOOD has three soil storage layers: UZS, the saturated upper zone storage (mm); IZS, the unsaturated intermediate zone storage (mm); and LZS, the saturated lower zone storage (mm). Pathogens moving through the soil to tile drainage systems, if present, are assumed to contribute to stream concentrations. The WATFLOOD model’s equation for interflow was used to simulate tile drainage by selecting parameters to represent more rapid movement of water to the stream through interflow. Groundwater baseflow was not assumed to contribute sufficient numbers of pathogens to streams to require inclusion in the model. Using a mass balance approach and keeping track of flows that may carry pathogens, the concentration of microorganisms at the soil surface was modeled as the solution to the ordinary differential equation describing the change in pathogens at the soil surface as a function of time

Css(t) ) -

qroCro + R1qinf + ksDI R1qinf + ksDI qroCro exp (1) t Co + DI R1qinf + ksDI

(

)(

)

where Css is the concentration of microorganisms at the soil surface (no./cm3); Co is the initial concentration of microorganisms at the soil surface (no./cm3); Cro is the concentration of microorganisms in the surface runoff, estimated using the adapted Hartley model (no./cm3); qro is the runoff from the soil surface (cm/h); qinf is the infiltration rate (cm/h); DI is the depth of incorporation (cm), ks is the first-order inactivation coefficient of the microorganism in soil or manure-amended soil (1/h); and R1 is a parameter describing the connectivity from the surface to the upper zone as a result of macropores (unitless). 4748

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Cuz(t) )

(

[(

)

)]

where Cuz is the concentration of pathogenic microorganisms in the upper zone (no./cm3), Cuzo is the initial concentration of pathogenic microorganisms in the upper zone at the beginning of the time step (no./cm3), Css is the concentration of pathogenic microorganisms at the soil surface calculated from eq 1 (no./cm3), qinf is the flux from the upper zone to the intermediate zone (cm/h), qint is the interflow (cm/h), qdrn is the flux from the upper zone to the lower zone (cm/h), UZS is the upper zone storage (cm), ks is the inactivation coefficient of the pathogenic microorganism in soil (1/h), R1 is a parameter describing the connectivity from the surface to the upper zone as a result of macropores (unitless), R2 is a parameter describing the connectivity from upper zone storage to tile drains as a result of macropores (unitless), and R3 is a parameter representing the connectivity from upper zone storage to the lower zone (unitless). The storage of pathogens was modeled in each soil layer given the fluxes of pathogens moving with the flow of water (i.e., infiltration, interflow) calculated in WATFLOOD. As there is no feedback from the upper zone storage to the soil surface, the equations were solved sequentially (i.e. the output of eq 1 was used as input to eq 2). Although the values of R1, R2, and R3 are not known and must be optimized, they are likely to be a function of the soil and vegetation type, soil moisture, and air entry pressure. An investigation by Fenlon et al. (41) into the transport of E. coli and E. coli O157 in the upper layers of soil to tile drains provides a sense as to the possible values for the R-parameters. Initially, almost all E. coli was retained in the upper layers of the soil, but approximately 2% was transported to the deeper layers of the soil. Transport to drains was mainly associated with rainfall events and amounted to approximately 7% of the applied E. coli. An important consideration is that the upper zone storage is a constantly changing depth, which is recalculated by WATFLOOD for each time step. A limitation of the above model is that as the upper zone storage decreases, the pathogen concentration will increase, regardless of where the pathogens are physically located. The assumption is deemed acceptable because below a given soil water content, when the upper zone storage is small, interflow and tile drainage will not occur. Furthermore, pathogen numbers are conserved (i.e. principle of conservation of mass is preserved), and pathogens are removed from storage either through inactivation or transport from the zone of storage. The effects of desiccation on microbial inactivation are not explicitly considered in the model. Channel Routing and Sedimentation of Free and Attached Microorganisms. In-stream routing is based on continuity, using a mixing cell approach, similar to the adaptation of WATFLOOD for sediment transport (32). During the routing process, pathogens are inactivated and will settle out. Experimental results by Medema et al. (42) demonstrated that observed sedimentation velocities for Cryptosporidium oocysts were low and may not result in significant settling in natural water systems. Their results also suggested that oocysts may attach to organic particles, and approximately 30% of oocysts sorbed to flocs within the first minutes of mixing. Another consideration is that the surface charge of

FIGURE 1. E. coli results for June 2002 to May 2003 below Elmira.

FIGURE 2. E. coli results for June 2003 to May 2004 below Elmira. Cryptosporidium oocysts measured as ζ-potential has been shown to be negative (43) and it has also been observed that they do not attach to natural soil particles, which are also often negative under laboratory conditions (44). More recently, Searcy et al. (45) found that Cryptosporidium parvum oocysts sorbed readily to soil particles and that oocystparticle interactions were not dominated by electrostatic repulsive forces. In our fate and transport model, sedimentation of attached microorganisms was assumed to occur with flocs of mean size 9.1 µm, with a range from 2.8 to 219.5 µm as was measured in situ in the Grand River by Droppo and Ongley (46). The settling velocity can be estimated by Stokes’ law (42). Assuming that the floc has a density of 2.65 g cm-3 (which will in all likelihood be an overestimation of the true density), the estimated settling velocity using Stokes’ law is 4.6 m d-1 or 0.19 m h-1. For modeling, 30% of microorganisms were assumed to be attached to flocs in the river, based upon an approximation of results by Medema et al. (42) and Searcy et al. (45). The settling velocity of free floating Cryptosporidium oocysts was assumed to be 0.029 µm s-1 (or 1 × 10-4 m h-1), as was measured by Kulkarni et al. (47). The settling velocity for the other microorganisms was assumed to be of the same order of magnitude as for Cryptosporidium, as it would be governed primarily by attachment to flocs rather than the size of the microorganisms.

Results and Discussion Hydrologic Simulation. Hydrologic simulation for Canagagigue Creek was performed for two years, June 2002 to May 2003 and June 2003 to May 2004. The years of simulation were chosen based upon the availability of microbial data (22, 48). The numbers of observations were the following: 99 E. coli, 85 Campylobacter, 86 E. coli O157: H7, 30 Cryptosporidium, and 30 Giardia. Quantitative realtime PCR methods for the enumeration of Cryptosporidium,

Giardia, and Campylobacter as well as standard methods for the detection of E. coli and E. coli O157:H7 are described elsewhere (48). Like all hydrologic models, the WATFLOOD model was observed to be sensitive to precipitation values, and on occasion, it appeared that a small number of events were missed while others were artificially created in the simulation, a result of the spatial variability of precipitation that was not always captured for some events in Canagagigue Creek. Two measures of goodness-of-fit were calculated for the hydrographs, the Nash-Sutcliffe coefficient, R2 (49), and deviation of runoff volumes, Dv (%) (50). The Nash-Sutcliffe coefficient works best when the coefficient of variation for the data set is large (51), which is not the case for Canagagigue Creek as compared to larger rivers. In general, the WATFLOOD model performed better for the second year of study (2003-2004), which had higher recorded streamflows. Although the deviation of runoff volumes statistic showed good fit between simulated and observed values, the Nash-Sutcliffe coefficient sometimes indicated that the model did not always predict better than the average. The Nash-Sutcliffe coefficient estimated that the best fit (0.55) occurred for the gauging station below Elmira for the period of 2003-2004 (a period with the highest coefficient of variation for streamflow), even though the estimate for the deviation of runoff volumes was not as good for the same period (-20%). The first year of simulation frequently underestimated streamflow, while the second year overestimated streamflow (results not shown). Pathogen Simulation. Figures 1 to 5 present results from hydrologic simulation of pathogen transport in Canagagigue Creek for the periods from June 2002 to May 2003 and June 2003 to May 2004. Observed samples with nondetects are represented in the figures as a single theoretical microorganism per volume. Simulations were performed for E. coli, Campylobacter spp., Cryptosporidium spp., Giardia VOL. 40, NO. 15, 2006 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 3. Campylobacter results for June 2002 to May 2003 below Elmira.

FIGURE 4. Campylobacter results for June 2003 to May 2004 below Elmira.

FIGURE 5. E. coli O157:H7 results for June 2003 to May 2004 below Elmira. spp., and E. coli O157. The parameters representing pathogen loading to the soil surface and first-order inactivation were the only parameters that varied among the microorganisms. The other parameters representing other aspects of microbial transport were assumed to be common to all microorganisms. This was reasonable because other processes such as settling are dominated by floc size rather than the size or density of the microorganism. For overland transport, too few mechanistic studies are available to differentiate values of transport parameters among microorganisms. The pathogen transport model was initially calibrated to observed E. coli occurrence in the year from June 2002 to May 2003, which partly explains why simulation results are marginally better in the first than the second year of simulation of E. coli. Observed data were censored as a result of nondetects; therefore, the nonparametric Spearman rank 4750

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correlation coefficient (Rs) was calculated to determine correlations between observed and simulated pathogen densities (52). The results for E. coli were generally within the correct order-of-magnitude, with the exception of the wet spring of 2004, in which concentrations were overestimated. Order-of-magnitude estimates are what are required for water treatment design and operation, and greater precision is not necessary, nor expected. Correlations between observed and simulated E. coli data revealed that the model performed well in some seasons and not as well in others. For example, correlations between observed and simulated E. coli densities were significant (p < 0.05) for the periods of summer and fall 2002 (Rs ) 0.875, n ) 6), summer 2003 (Rs ) 0.85, n ) 9), fall 2003 (Rs ) 0.46, n ) 30), and spring 2004 (Rs ) 0.58, n ) 16). Spring 2003 demonstrated weak positive correlation (Rs ) 0.33, n ) 19), and the winter

months showed little or no positive correlation for either year (Rs ) 0.1, n ) 9 for 2003; Rs ) -0.5, n ) 12 for 2004). For both years and for all other pathogens investigated, calculated Rs values indicated that correlations between simulated and observed concentrations were significant (p < 0.05), with the exception of Cryptosporidium in 20022003 (Rs ) 0.69, n ) 6, see Supporting Information). However, it should be noted that although the model performed reasonably well predicting low or undetectable pathogen concentrations for periods when no pathogens were observed, there is greater uncertainty with results with large numbers of nondetects. The model predicts that more than 70% of the time it would not be possible to detect Cryptosporidium in stream samples and that Giardia would be observed in fewer than 20% of samples. However, at other times, the concentrations may be very high (see Supporting Information). This type of response was also observed through monitoring that was performed in Canagagigue Creek and the Grand River Watershed (22, 48). The implications of these results are that (in a watershed with similar or greater variability) large numbers of samples must be collected (greater than 12 samples per year) or else it will not be possible to appropriately assess raw water quality for protozoa. Only one E. coli sample point was collected during the period of 2002-2003 that also corresponded to an event producing overland flow (and was the highest recorded E. coli concentration for the year, as seen in Figure 1). Therefore, the model accurately predicts the sample point, but may represent an overfitting of the model. Data for Cryptosporidium, Giardia, and Campylobacter are not available for that sampling date and E. coli O157:H7 was not detected. The model predicts that most of the pathogen loading to the streams from land-based sources will occur from subsurface drainage systems; however, the peak pathogen occurrences will occur during events that produce overland flow. The observed and simulated peak concentration in 2002-2003 occurred in November, a time when common practice is to spread manure on the land following crop harvest. The model proved to be highly sensitive to the R (connectivity) parameters, even though they were small (on the order of 10-5) since the predicted pathway of greatest importance was through tile-drained interflow. On a cumulative annual basis, approximately 72% of precipitation was infiltrated. As expected, a comparison of the transport of different microorganisms revealed that loading and inactivation rates play a role in the estimated concentrations with bacteria present in greater numbers than protozoa. The model was very sensitive to the initial loading rate, which as discussed by Dorner et al. (23) is highly sensitive to the shedding intensities of the animals. Using the parameters calibrated for the watershed hydrology, the model was generally not sensitive to the pathogen “erodibility” factor, a reflection of the primary pathway of pathogens, which was observed to be through interflow (tile drainage) the majority of the time. Only during the most intense precipitation/ snowmelt events was pathogen “erodibility” a factor in estimating stream concentrations. For agricultural watersheds without tile drainage, it can generally be anticipated that pathogen stream concentrations are coming from sources within the stream or its riparian area (with the exception of periods of intense precipitation/snowmelt events that generate overland flow). Measured concentrations of E. coli were nearly always observed to increase with increasing flowrate, regardless of the reason for the streamflow increase. However, as the model does not presently consider the resuspension of microorganisms from the river’s sediments, an increase in flow resulting from a reservoir release may result in a model

prediction of lower concentrations downstream as a result of greater dilution. Others have observed that E. coli can persist in bottom sediments and become resuspended during storm events (53). The difference between the observed and simulated concentrations during periods of increased flow as a result of reservoir releases may provide some indication of the magnitude of the sediment load for future investigations. Furthermore, it would be useful to determine the origins of pathogens in the sediments, as they may be more likely to represent fecal inputs from aquatic wildlife sources. In July 2002, a raw sewage spill occurred in Canagagigue Creek. It was modeled as an increase in the point loading to the stream for the duration of the spillsa total of approximately 1000 m3 over 17 h. During the spill, microorganisms were also predicted to be transported from landbased sources. Therefore, the modeled impact of the spill was partly masked by nonpoint sources of microorganisms. The true pathogen concentration observed downstream resulting from the point source spill could be expected to be higher than the predicted values, even though it showed a large increase (as can be seen in Figure 1 at hour 1008). A current limitation of the model results from the use of a mixing cell approach that creates excessive dispersion. For nonpoint sources, which may be spatially averaged, this does not pose a problem. For point sources, the simulated concentration may be underestimated. In general, E. coli concentrations were underestimated with the exception of spring 2004, when wet conditions led to the prediction of high numbers for all pathogens. Although the model consistently overpredicted indicator and pathogen numbers for the spring of 2004, it is interesting to note that this was the only period that Campylobacter and E. coli were continuously observed at elevated levels (relative to baseline concentrations). Therefore, the model correctly predicted that concentrations would generally remain elevated for the spring of 2004, but it did not predict the correct order-ofmagnitude. At times, the model predicted no E. coli when E. coli was observed. This underestimation of E. coli densities was expected, since the model only considered wastewater treatment effluent and agricultural land-based sources but no wildlife sources. Also not considered were the effects of cattle grazing in pasture near (and in) streams. During the summer months, agricultural land-based sources were not estimated to contribute to pathogen loading to streams because the water balance calculated for that period showed that all water was lost as evapotranspiration or was retained by the soil. Observed concentrations of E. coli and other microorganisms at that time may be as a result of wildlife or livestock near or in streams. It is possible that during periods of base flow, the groundwater may contribute E. coli to the streams. At the present time, the base flow contribution to pathogens in streams has not been considered, since the loadings were expected to be low. Also not considered at this time are the interactions between the channel sediments and the microorganisms. The resuspension of pathogens originating from wildlife and livestock with direct access to streams during periods of higher streamflow is a process that is likely important and will be included in the future. In contrast to E. coli, Campylobacter concentrations were frequently overestimated (see Figures 3 and 4). Unlike indicators such as E. coli, which can be expected to be widely present throughout a watershed, pathogens are more likely to occur in clusters in time and in space. The discrepancy between observed and estimated Campylobacter results is considered to lie primarily in the estimated loading of Campylobacter on the soil surface. To replicate the observed stream Campylobacter concentrations, it would be necessary to determine the locations within the watershed where the VOL. 40, NO. 15, 2006 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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Campylobacter is originating. The importance of sources (timing and location) is true for all pathogens. The method for estimating pathogen loading to the soil surface should be an integral part of any future uncertainty/sensitivity analysis, as it has a dramatic impact on estimated stream pathogen concentrations. E. coli is a good surrogate for understanding pathogen transport on the watershed scale. Its widespread nature in the environment assists with determining the important factors for watershed-scale pathogen fate and transport, since its predicted numbers were observed to be clearly linked to hydrologic processes. The order-of-magnitude of simulated pathogen concentrations are generally within the ranges of observed concentrations. Also, the relative concentration differences between the indicator, E. coli, and the pathogens are as would be expected. E. coli is more frequently observed and observed in higher concentrations than Campylobacter, which is more frequently observed in higher numbers than E. coli O157:H7. For the protozoa, estimated Cryptosporidium concentrations are higher than Giardia concentrations, reflecting the greater environmental resistance of Cryptosporidium oocysts as compared to Giardia cysts rather than large differences in loading rates. The pathogen transport model’s results are considered to be reasonable and useful at this time, given that not all important sources and processes have been included. One of the greatest challenges for modeling pathogen occurrence is the sparsity of the pathogen data, which is the result of the limitations and costs associated with methods for the detection and enumeration of pathogens from environmental samples. The development of improved molecular methods for identifying pathogens in the environment remains critical for the understanding of pathogen fate and transport. One of the greatest benefits arising from the development of a pathogen fate and transport model is that it can be used to point to the most important future research directions.

(4)

(5)

(6) (7)

(8) (9) (10) (11) (12)

(13) (14)

(15)

(16)

Acknowledgments This study was funded by the Canadian Water Network and the Natural Sciences and Engineering Research Council of Canada (NSERC). The authors acknowledge the Grand River Conservation Authority, the City of Brantford, and the Regional Municipality of Waterloo for providing data. We thank Frank Seglenieks and Jayson Innes for assistance with the preparation of WATFLOOD input files and David George Cosh for assistance with summarizing inactivation data.

Supporting Information Available (1) map of the location of the Grand River Watershed; (2) complete tables of estimated first-order inactivation coefficients for Cryptosporidium, Giardia, Campylobacter, and E. coli O157:H7; (3) equations used for modeling pathogens in storage and overland transport; (4) figures of observed versus predicted E. coli densities and Cryptosporidium and Giardia simulation results. This material is available free of charge via the Internet at http://pubs.acs.org.

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Received for review February 22, 2006. Revised manuscript received May 30, 2006. Accepted June 1, 2006. ES060426Z

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