Assessing microbial pollution of rural surface waters

Agricultural Water Management 70 (2004) 1–17

Review

Assessing microbial pollution of rural surface waters A review of current watershed scale modeling approaches R. Jamieson a,∗ , R. Gordon b , D. Joy a , H. Lee c a School of Engineering, University of Guelph, Guelph, Ont., Canada N1G 2W1 Engineering Department, Nova Scotia Agricultural College, Truro, NS, Canada B2N 5E3 Department of Environmental Biology, University of Guelph, Guelph, Ont., Canada N1G 2W1 b

c

Accepted 14 May 2004

Abstract Liquid and solid wastes generated from both animal and domestic sources can significantly impair drinking, irrigation and recreational water sources in rural areas. The assessment and management of non-point sources of microbial pollution, in particular, is an issue of great interest. A representative watershed scale water quality model would be an invaluable tool in addressing microbial pollution issues. The objective of this review is to present and evaluate current approaches to modeling the microbial quality of surface waters in rural watersheds. A complete watershed scale microbial water quality model includes subroutines which (i) characterize the production and distribution of waste and associated microorganisms, (ii) simulate the transport of microorganisms from the land surface to receiving streams, and (iii) route microorganisms through stream networks. Current watershed scale models only account for microbial transport to surface waters through overland flow and ignore subsurface transport. The movement of microorganisms on the soil surface is predicted using simple empirical equations or by assuming that microorganism transport is only associated with sediment erosion. However, several studies have indicated that the assumption that microorganism transport is directly linked with sediment transport may not be valid. The simulation of microorganism survival and transport in receiving streams is complicated by sediment/microorganism interactions. More research is needed to be able to quantitatively assess and model microbial processes in alluvial streams. © 2004 Elsevier B.V. All rights reserved. Keywords: Modeling; Watersheds; Microbial; Fecal coliforms; Agricultural; Rural; Water quality; Transport; Survival

∗

Corresponding author. Tel.: +1 519 824 4120x53588; fax: +1 519 836 0227. E-mail address: [email protected] (R. Jamieson). 0378-3774/$ – see front matter © 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.agwat.2004.05.006

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1. Introduction The management of microbial pollution sources in rural watersheds is challenging. Significant efforts have been made to eliminate bacterial pollution sources from urban areas (e.g., wastewater treatment plant discharges and combined sewer overflows). Many urban rivers, however, remain impaired with respect to microbial water quality. In many cases, upstream rural areas are the suspected sources (Murray et al., 2001). Treatment and control options for non-point sources of microbial pollution are more difficult to identify than point sources. A comprehensive understanding of the problem requires that many watershed factors including climatic conditions, hydrologic parameters, and site-specific physical parameters be considered (Sadeghi and Arnold, 2002). Two primary stores of bacteria exist in rural landscapes: (i) the land store and (ii) the channel store. Movement from the land store is related to hill slope hydrological processes whereas movement within the channel store is related to fluvial processes (McDonald et al., 1982). Pathogens that have the potential to infect humans can be divided into the categories of bacteria, protozoans and viruses. Important bacterial pathogens include E. coli O157:H7, Salmonella, Shigella and Vibrio cholerae. Protozoans of concern include Cryptosporidium, Giardia lamblia, and Entamoeba histolytica. Infectious viruses found in water systems include Enterovirus, Rotavius, Hepatitis A, and Reovirus (USEPA, 2001). Difficulties and expenses involved in the testing for specific pathogens, however, have generally led to the use of indicator organisms of enteric origin to estimate the persistence and fate of enteric pathogens in the environment (Crane et al., 1981). Fecal coliforms (FC) are the most commonly used indicator organisms. Escherichia coli is the most common FC and although most E. coli strains are non-pathogenic, some strains, such as E. coli O157:H7, pose a serious health risk to humans. The United States Environmental Protection Agency (USEPA) now recommends that E. coli be used as the principle indicator organism in freshwaters, instead of FC. Research has shown E. coli densities are more strongly correlated with swimming-associated gastroenteritis (USEPA, 2001). Current water quality standards are based on the concentration of indicator organisms and the intended use of the water system (drinking water, irrigation, livestock watering, recreational). The Canadian Council of the Ministers of the Environment (CCME) water quality standard is 100 and 200 FC 100 mL−1 for irrigation and recreational water uses, respectively (CCME, 1999). Fecal coliform bacteria should not be present in potable water supplies. Many jurisdictions have two part water quality standards. The USEPA standard for freshwater recreational waters is that the geometric mean of at least five samples during a 30-day period must not exceed 126 E. coli 100 mL−1 with no one sample exceeding 235 E. coli 100 mL−1 . Several states also have bacterial standards that depend on season, which correspond with seasonal water use designations (USEPA, 2001). A need exists to develop rural waste management systems, which minimize environmental risks and contribute to a sustainable agri-food industry. Analysis tools must be developed to properly evaluate alternate management practices and to predict water quality improvements at the watershed scale. Simulation models can play an important role in the assessment and management of natural water systems. Watershed-scale water quality models have the ability to: (i) simulate the movement of pollutants from the land surface to receiving streams, and (ii) route the pollutants through the stream network to the watershed outlet. A representative

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and thoroughly tested model can aid watershed planners in land use decision making and reduce water quality monitoring requirements. Unfortunately, knowledge deficiencies with respect to the behavior of enteric microorganisms in the environment have been an impediment to the development of a useful microbial water quality modeling tool. In particular, the influences of sediment–microorganisms associations is not fully understood, or represented, within current modeling frameworks. The objective of this review is to summarize and evaluate current approaches to predicting the microbial water quality of surface waters at the watershed scale. The review will primarily focus on models which have been specifically designed to simulate microbial water quality processes in agricultural watersheds.

2. Description of microbial water quality models A complete watershed water quality model would include components which (i) characterize and track microbial sources, (ii) model the survival and transport of microorganisms within/on the landscape, and (iii) model the survival and transport of microorganisms in streams and lakes. Several process-based models have been developed to simulate various aspects of microbial surface water pollution at the watershed scale. Models that have been developed to simulate exclusively landscape microbial pollution processes include MWASTE (Moore et al., 1989), COLI (Walker et al., 1990), and SEDMOD (Fraser et al., 1998). These models can be termed as “loading” models. Other models have also been developed to simulate survival and transport of fecal bacteria in receiving waters such as lakes (Canale et al., 1993) and rivers (Wilkinson et al., 1995). Models that incorporate both landscape and in-stream microbial processes include the Soil and Water Assessment Tool (SWAT) (Sadeghi and Arnold, 2002) and a watershed model developed by Tian et al. (2002). The aforementioned models simulate the survival and transport of indicator organisms, typically FC.

3. Microbial source characterization The primary source of microbial pollution in agricultural watersheds is fecal matter generated from livestock production. The microbial loading potential from point sources, such as storage facilities and feedlots, and from non-point sources, such as grazed pastures and rangelands, is substantial. Non-agricultural sources of microbial pollution in rural watersheds include failing septic systems and wildlife. Non-point sources of microbial pollution are inherently more difficult to identify and characterize than point sources. Walker et al. (1990) stated that source areas can be divided into four categories: (i) areas where manure is surface applied, (ii) areas where manure is incorporated into the soil, (iii) areas where manure is directly deposited by livestock, and (iv) non-manured areas. The transport of potential pathogens from non-point source areas to streams is linked to temporally and spatially variable factors, which affect runoff. The number of organisms which will be available for transport will also depend on the timing of manure application and a combination of physical, chemical and biological factors which influence survival (Edwards et al., 1997).

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A full characterization of livestock waste microbial sources within an agricultural watershed would include obtaining information on: (i) livestock densities, (ii) livestock confinement and grazing schedules, (iii) access of livestock to streams, (iv) manure application rates and timing, (v) locations of feedlots, and (vi) manure production estimates and waste characteristics (USEPA, 2001). Some assessment must also be made with respect to the spatial and temporal distribution of livestock. Specifically, the amount of time animals spend in confinement, in pastures, and watering in streams must be estimated. Concentrations of indicator and pathogenic organisms in animal waste vary widely depending on animal type, waste storage system, and the level of pretreatment prior to land disposal. Animal age, ration, and antibiotic treatment may also affect the number and type of microorganisms voided from animals (Walker et al., 1990). Detailed summaries of bacterial densities for species commonly found in wastes for both domestic and wild animals are provided by Reddy et al. (1981), Crane et al. (1983) and USEPA (2001). One of the major difficulties in microbial pollution assessment is characterizing wildlife or “background” levels of contamination. Wildlife, such as waterfowl, can be a significant contributor to fecal pollution within rural watersheds. Weiskel et al. (1996) found that waterfowl accounted for 67% of the FC loading to a coastal embayment. Faust (1982) stated that a reasonable estimate for FC densities in soils uncontaminated by livestock is 400 FC g−1 soil.

4. Sediment–microorganisms associations A wealth of literature indicates that the majority of enteric bacteria in soil and aquatic systems are associated with sediments and that these associations influence their survival and transport characteristics. Two types of bacterial adsorption have been identified: (i) weak adsorption, which is due to van der Waals forces exceeding repulsive forces, and (ii) strong adsorption, which is due to cellular appendages or extracellular polymers excreted from the cell (Palmateer et al., 1993). Bacteria which are weakly adsorbed are not actually attached to the soil surface, only closely associated with it. Weak adsorption is considered to be a reversible process, whereas strong bonding mechanisms are thought to be irreversible (Berry and Hagedorn, 1991). Bacteria, and most soil and sediment surfaces, in the natural environment are negatively charged and would therefore repel each other. However, in solutions containing high electrolyte concentrations, the repulsive forces are suppressed and attractive London–van der Waals can result in reversible adsorption of bacteria to solid surfaces (Marshall, 1980). It is believed that bacteria are initially drawn to solid surfaces by London-van der Waals forces. Once they are positioned close to the surface they can then use extracellular polymers to form a strong, permanent attachment (Marshall, 1985). The extracellular polymers, or glycocalyx, which have been shown to coat bacterial surfaces, allow bacteria to strongly adhere to surfaces in flowing water systems (Costeron et al., 1978). Bacteria can be held to surfaces even in the presence of large shear forces (Marshall, 1980). Adsorption of microbes to surfaces at equilibrium is commonly described by a linear adsorption isotherm (Ling et al., 2002):

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Kd =

Cs Cw

5

(1)

where Kd is the distribution coefficient (L g−1 ), Cs is the concentration of microorganism in soil (organisms g−1 ), and Cw is the concentration of microorganism in water (organisms L−1 ). Ling et al. (2002) attempted to determine Kd values for E. coli in two soils (clay loam and silt loam) through batch equilibrium experiments. They differentiated between weakly bound and strongly bound (still bound after being washed twice with physiological saline) bacteria. In weak adsorption trials, 25% of E. coli cells were adsorbed in the silt loam while 99% were adsorbed in the clay loam soil. The Kd values for the clay loam was 127 mL g−1 and 0.33 mL g−1 for the silt loam. In strong adsorption experiments, the Kd values for the clay loam was 25 mL g−1 and 0.62 mL g−1 for the silt loam. They speculated that the higher Kd for strong adsorption in the silt loam soil was due to the increase in ionic strength because of washing with 0.85% NaCl. Ling et al. (2002) stated that methods for investigating bacterial adsorption parameters require more development.

5. Modeling microbial survival 5.1. Modeling microbial survival Factors which have been shown to influence microbial survival in the aquatic environments include temperature (Davenport et al., 1976; Barcina et al., 1986; Flint, 1987), light (McCambridge and McMeekin, 1981; Davies and Evison, 1991), pH (McFeters and Stuart, 1972; Sjogren and Gibson, 1981), availability of nutrients (Dutka and Kwan, 1980; Lessard and Sieburth, 1983), and the presence of predators (Barcina et al., 1986; Medema et al., 1997). In soil–water environments, survival is influenced by moisture content (Mubiru et al., 2000; Entry et al., 2000b) soil type (Chandler et al., 1981; Zhai et al., 1995), temperature (Reddy et al., 1981; Kudva et al., 1998), nutrients and competing microorganisms (Reddy et al., 1981) and pH (Sjogren, 1994). Survival of microorganisms are typically simulated assuming first-order die-off kinetics. The equation takes the form a simple first-order time-dependent decay function (Gannon et al., 1983): Nt = No e−kt

(2)

where t is time, k is the first-order inactivation constant (t−1 ), No is the initial number of organisms, and Nt is the number of organisms at time t. In this formulation, all of the factors and processes which influence survival are lumped into a single coefficient (k), which is usually estimated from data obtained through laboratory-based survival experiments. Reddy et al. (1981) conducted a review of bacterial survival and attempted to develop first-order rate constants to describe the inactivation of several indicator organisms and pathogens in soil systems. Average first-order inactivation rate constants were 1.14 day−1

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for FC and 0.41 day−1 for fecal streptococci (FS). Moore et al. (1989) also complied a list of inactivation coefficients computed for bacterial survival in soil systems. Several workers have attempted to develop formulations in which the inactivation coefficient is explicitly dependent on environmental variables. Moore et al. (1989) incorporated air temperature, pH, and manure application method into their model equation, which predicts the inactivation of enteric bacteria in soils. The microbial sub-model component of SWAT simulates the survival of enteric organisms as two different populations: (i) non-persistent microorganisms, and (ii) persistent microorganisms such as Cryptosporidium and E. coli O157. Users can specify inactivation and regrowth rate constants for each population. The SWAT model also contains an option by which different rate constants can be specified for soluble bacteria and bacteria attached to soil particles. Auer and Niehaus (1993) developed an inactivation model for Onondanga Lake, which incorporated temperature and irradiance as independent variables. A model developed by Mancini (1978) included temperature, % sea water, and solar radiation as predictive variables. Beaudeau et al. (2001) modelled inactivation kinetics of E. coli concentrations in several small rivers in France as a linear function of flow rate, water temperature, and suspended sediment concentration. These formulations were developed and calibrated for very specific conditions and their versatility has yet to be tested. It has also been noted that bacterial decay curves in aquatic environments may not follow an ideal exponential decay curve. A lag phase, in which a negligible decrease in the bacterial population is observed, often precedes the exponential decay phase (Gonzalez, 1995). Gonzalez (1995) found that bacterial decay curves could be more accurately modelled using non-linear, sigmoidal models as opposed to log–linear models.

5.2. Influence of sediment associations Extended survival patterns have been noted for bacteria that have attached to sediment particles and settled to the bottom of streams and lakes (Burton et al., 1987). Several studies have shown that concentrations of indicator organisms are typically higher in sediment as opposed to the overlying water column in both marine and freshwater systems (Hendricks, 1971; Stephenson and Rychert, 1982; Gary and Adams, 1985; Burton et al., 1987; Sherer et al., 1992). It has been postulated that enteric bacteria can survive longer, and possibly grow, within stream bottom sediments. Enteric bacteria are typically associated with fine sediment particles (0.45–10 Fm) in aquatic environments (Gannon et al., 1983; Auer and Niehaus, 1993). The survival of fecal bacteria in sediments is primarily attributed to the availability of soluble organics and nutrients (Davies et al., 1995; Marino and Gannon, 1991) as well as to increased protection from predatory protozoans (Enzinger and Cooper, 1976). Gerba and McLeod (1976) examined the effects of sediments on E. coli survival in laboratory-incubated marine water. E. coli survived longer in the presence of sediments, and even grew, in non-autoclaved mixtures. LaLiberte and Grimes (1982) conducted in situ survival studies in a shallow lake using cellulose-based dialysis tubing. E. coli populations which were inoculated into unsterile sediments decreased only slightly over a

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5-day period and increased in number in sterilized sediments. The influence of sediment particle size on FS and FC survival was studied by Howell et al. (1996). Their results indicated that the survival of fecal indicator bacteria increased with decreasing particle size. Maki and Hicks (2002) studied the effects of suspended particles on the survival of Salmonella typhimurium in laboratory microcosms. They examined the impacts of three types of particles: clay, silt and flocculent organic particles. Inactivation constants computed for S. typhimurium populations in control microcosms were not significantly different from those computed for microcosms containing any of the particle types. The authors verified that a large portion of the bacterial population was attached to particles through a direct viable count preceded by an in situ hybridization procedure. Results indicated that attachment to suspended particles did not enhance the survival of S. typhimurium, which is contrary to information presented by other researchers (namely Sherer et al., 1992; Howell et al., 1996). However, Sherer et al. (1992) and Howell et al. (1996) examined the survival of coliform bacteria in settled sediments.

6. Modeling microbial transport 6.1. Landscape transport 6.1.1. Surface versus subsurface transport There is a general consensus that overland flow is the primary microbial transport process associated with non-point source pollution of surface waters. As such, current watershed models only account for microbial movement in overland flow, ignoring all forms of subsurface transport. Bacteria which enter the soil profile with infiltrating water are assumed to be lost from the system (Moore et al., 1983). It has been shown, however, that microbial transport in the subsurface environment can be significant within certain landscapes, such as those that are artificially drained. The transport of microorganisms in the subsurface environment has been extensively reviewed (Corapcioglu and Haridas, 1984; Gerba and Bitton, 1984; Abu-Ashour et al., 1994; Jamieson et al., 2002) and thus will not be discussed in detail in this paper. Weiskel et al. (1996) investigated sources of FC pollution in a coastal embayment near Buzzards Bay, MA. Although on-site septic systems were the largest source of bacteria in the watershed, the authors estimated that only 0.001% of the FC input to the bay was due to septic systems. This small percentage was attributed to effective subsurface filtration of the bacteria. Hunter et al. (1992) conducted one of the few studies that attempted to separate overland transport from subsurface transport of bacteria to receiving streams. A short section of streambank in a northen England upland watershed was instrumentated to collect samples of overland flow, matrix flow, and non-matrix flow (macropores). In this manner, the relative contributions of each transport mechanism could be determined. The majority of bacterial load originated from overland flow sources. Bacterial concentrations in matrix flow were relatively low. Non-matrix flow loads were higher than matrix flow but lower than overland flow. The catchment area was characterized by springs that contributed overland flow

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in-between storm events. Therefore, overland flow was the major contributor of bacteria even during baseflow periods. 6.1.2. Surface transport processes Factors affecting the ability of overland flow to transport microorganisms include rainfall duration and intensity, method of manure application, fecal deposit age, and adsorption to soil particles (Walker et al., 1990). Most microorganisms of interest, including E. coli and parasitic protozoans, possess a neutral buoyancy and would theoretically be easily entrained in overland flow (Walker et al., 1990). The movement of microorganisms is also influenced by their tendency to adsorb to soil and organic surfaces. The clay component of soil acts as a major transport mechanism for enteric bacteria. The surface transport of enteric microorganisms from agricultural land has been studied at the field scale by several researchers (Doran and Linn, 1979; Doran et al., 1981; Gary et al., 1983; Patni et al., 1985; Edwards et al., 1997). Unfortunately, these studies have provided limited information on the factors which influence microbial transport in surface runoff. They have shown that when a source of microorganisms is present on the land surface, runoff generated from that area of land contains fecal bacteria. Field scale studies have only been able to illustrate the complexity of microbial transport and the need to fully understand hydrologic characteristics. Coyne et al. (1995) examined FC transport through 4.6 × 9.0 m grass buffer strips (grass height = 4 cm). Runoff was generated from plots amended with poultry manure upgradient of the filter strips using a rainfall simulator. They applied simulated rainfall at a very high rate of 6.4 cm h−1 . The strips filtered 99% of the sediment, while in contrast, FC removal efficiencies were only 74 and 43%. Concentrations of FC in runoff leaving the filter strips were usually greater than 200 counts 100 mL−1 . It appeared that grassed buffer strips were ineffective in trapping small particles such as clays and bacteria cells. It is likely that rapid surface flows may keep buoyant FC cells in solution while larger, denser soil particles settle out. Srivastava et al. (1996) examined the effects of filter strip length on FC removal from manured pasture runoff and also found that filters strips were ineffective in removing FC from runoff. Abu-Ashour and Lee (2000) sprayed two 10 × 10 m2 soil plots with a strain of E. coli resistant to nalidixic acid (E. coli NAR) and monitored the surface movement of the biotracer during two rainfall events. The average slopes of the two plots were 2 and 6%. A 25 mm rainfall event occurring 2 days after the sites were inoculated resulted in movement of the biotracer 20 m downgradient of the source area on the 2% slope and 35 m downgradient of the source area on the 6% slope. The authors were unable to identify if the biotracer was moving as a free colloid or if it was transported attached to eroded soil. The association of microorganisms with sediment particles during the overland flow process has received little attention. The few studies which have been conducted suggest that strategies intended to limit sediment transport, such as grassed filter strips, may have little impact on microbial transport. Dai and Boll (2003) conducted lab-scale batch experiments designed to evaluate the association of Cryptosproidium oocysts and Giardia cysts with soil particles. Their experiments indicated that oocysts do not attach to soil particles and that overland flows probably carry oocysts as free entities. They also concluded that the selection of control strategies which limit sediment transport may have little effect on oocyst movement.

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6.1.3. Surface transport modeling Landscape model components consist of: (i) a waste generator subroutine which continuously tracks the volume and characteristics of manure produced from an individual farming operation and when it is land applied, (ii) a hydrology subroutine, and (iii) a microbial transport subroutine (Moore et al., 1989). Equations used to predict landscape loading are largely empirical or have been developed from sediment transport formulae, such as the Universal Soil Loss Equation (USLE). Since surface runoff is assumed to be the primary transport mechanism, the model must possess well-tested routines for predicting surface runoff characteristics. The accurate prediction of surface runoff requires information on watershed size, soil types, land slopes, surface cover, and precipitation characteristics. Walker et al. (1990) assumed that all bacteria deposited on the land surface are associated with soil particles. Surface transport of bacteria within COLI (Walker et al., 1990) is computed using the modified universal soil loss equation (MUSLE). The mass of soil eroded is multiplied by the bacterial cell density of the soil/manure mixture at the time of the runoff event to determine the number of bacteria cells which are transported. Moore et al. (1989), Fraser et al. (1998) and Tian et al. (2002) all estimate surface transport of bacteria using simple empirical equations which relate bacteria transport to runoff rate or depth. Fraser et al. (1998) also incorporates a delivery ratio which is computed as a weighted function of slope, slope shape, surface roughness, stream proximity, soil texture, and soil moisture. However, their model provides a bacteria loading at one point in time, considering only steady-state land use and climate factors. The model presented by Tian et al. (2002) is fully dynamic and also incorporates the distance between the source location and the receiving water system using a delivery ratio. The empirical transport equations within these models have not been well tested and validated. The empirical transport parameters have no physical basis and must be obtained through calibration. SWAT is the only model which has attempted to partition bacteria into adsorbed and non-adsorbed fractions. Adsorbed bacteria transport is predicted using the USLE. All non-adsorbed bacteria within the surficial soil layer are assumed to be mobile during runoff events. Although SWAT presents the most physical representation of bacteria transport in surface runoff, reliable data on bacteria partitioning are currently not available. 6.2. In-stream transport 6.2.1. General transport mechanisms Microorganisms can be transported by both advection and dispersion processes in stream environments. Advection refers to transport with the mean water flow, and dispersion represents the movement of contaminants through the action of random motions (Mihelic, 1999). In a stream, turbulent (the mixing of turbulent eddies), and mechanical (variations in the speed of water in different regions) dispersion processes would dominate. Field studies which have specifically examined the transport of enteric microorganisms in streams are limited in number. Dutka and Kwan (1980) injected Serratia marcescens into a small stream in southern Ontario and tracked its movement. The biotracer was recovered 21 km from where it was introduced. Transport rates were slower than stream velocity, indicating the bacteria were interacting with various components of the stream (sediments, vegetation); however, they were unable to investigate this speculation. Palmateer et al.

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(1993) examined the movement of enteric bacteria in an agricultural drainage channel. A biotracer, E. coli NAR, was introduced into the channel. The biotracer, which was adsorbed to sediment from the drain before injection, was recovered 18 km downstream within 24 h. 6.2.2. Influence of sediment associations The phenomena of resuspending bacteria from the sediment pool back into the water column is a process that has received little attention. Resuspension could occur as a result of storm-induced streamflow surges or as a result of human or animal disturbances (cattle entering streams, canoeists, dredging). McDonald et al. (1982) created artificial storm hydrographs on a river section by releasing water from an upstream reservoir. All experiments were conducted during baseflow periods therefore, the authors assumed any increases in stream FC concentrations were due to resuspension of bacteria from the sediment store. They found that bacteria concentrations increased 10-fold in response to stage increases. Gary and Adams (1985) and Sherer et al. (1988) both attempted to simulate cattle disturbances by raking a 1 m2 section of stream bottom within watersheds used for cattle and sheep grazing. Downstream water quality was monitored before and after raking. Gary and Adams (1985) reported that stream sediment was not a significant source of FC but was a significant reservoir for FS, which appeared to multiply within the sediment. Conversely, Sherer et al. (1988) noted that stream bottom disturbances played a significant role in elevated water column FC concentrations. Pettibone and Irvine (1996) examined the sources of indicator bacteria in the Buffalo River area of concern (AOC) near Buffalo, NY. It was originally assumed that high indicator bacteria levels in the AOC were due to urban stormwater runoff; however, Pettibone and Irving found that upstream tributaries that drain primarily rural watersheds contributed a large portion of the bacterial loading. They also reported a strong correlation between total suspended solid concentrations and FC levels, indicating that sediments could be playing a role in bacterial transport. Fecal coliform levels in river sediment during the summer were 1 to 5 logs higher than in the overlying water column. Jamieson et al. (2003) attempted to ascertain the sources of FC bacteria in a small rural watershed located in the Annapolis Valley, Nova Scotia, Canada. Results showed that FC levels consistently exceeded recreational water quality guidelines (>200 counts 100 mL−1 ) during low-flow periods, leading the authors to believe that reservoirs of FC in the stream sediments were the primary source during dry summer months. It is speculated that Cryptosporidium oocysts and Giardia cysts also attach to sediment and organic particles in freshwater systems and that their settling behavior will be governed by the properties of the material to which they are attached. Medema et al. (1998) examined the settling characteristics of both free-floating and attached C. parvum oocysts and G. Lamblia cysts in freshwater. C. parvum oocysts were approximately 5 ␮m in diameter while the G. lamblia cysts were 10–12 ␮m in size. Both oocysts and cysts had a density of approximately 1.04 g cm−3 . The measured sedimentation velocities for free-floating oocysts (0.35 ␮m s−1 for oocysts and 1.4 Fm s−1 for cysts at 23 ◦ C) were closely matched to theoretical velocities calculated by Stokes’ law. When the oocysts were mixed with a solution containing secondary sewage, 75% of oocysts attached to biological particles within 24 h. Attachment to particles significantly enhanced oocyst settling velocities. Sedimentation velocities for free-floating oocysts are probably too low to cause sedimentation in

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most natural water systems; however, attachment of oocysts to suspended particles greatly enhances sedimentation of the two organisms. These results contradict the observations of Dai and Boll (2003). But, Medema et al. (1998) used organic particles, which may possess different surface charges than inorganic sediments. 6.2.3. In-stream transport modeling Most deterministic water quality models simulate microbial pollutants as free-floating entities that are transported through a channel network by advection and dispersion (Wilkinson et al., 1995). Bacterial cells are assumed to be neutrally buoyant, possessing negligible settling velocities. An additional term is included within the one-dimensional advection dispersion equation to account for die-off of the microorganism. SWAT simulates the movement of contaminants through channel networks using a volume routing approach (Muskingum–Cunge Method). Enteric bacterial dynamics in rivers have also been modelled using statistical approaches. These models typically use multivariate analysis which relate indicator organism concentrations to any number of physical and/or chemical factors (Wilkinson et al., 1995). Kay and McDonald (1983) constructed a multivariate model to predict coliform concentrations in an upland impoundment. In total, the model possessed 20 predictor variables consisting of both chemical (e.g., conductance, DO, pH) and hydrologic (e.g., rainfall in the preceeding of 4 weeks, height of previous hydrograph) parameters. Elder (1987) used a regression analysis to relate FC and FS concentrations in the Apalachicola River to several hydrologic variables including discharge, whether the river stage was falling or rising, whether the flood was in early or late phase, and the volume of the current flood peak relative to earlier peaks. The model indicated that 53% of the FC variability was accounted for by river discharge alone. Unfortunately, empirical models are of limited use in guiding management decisions regarding receiving water systems (Canale et al., 1993). Few attempts have been made to include sediment associations within water quality models. Canale et al. (1993) developed a comprehensive physically based model for simulating FC levels in an urban lake located in Syracuse, NY. The lake was modelled as a two-layer system (surface and bottom) consisting of a series of interconnected, completely mixed cells. A mass balance approach was used to track the movement of FC horizontally and vertically between cells by advection and dispersion. The disappearance of FC from the surface layer occurred through death and sedimentation. Bacterial cells were assumed to be primarily associated with particles that were 10 ␮m in diameter and their settling velocities were computed using Stokes’ law. They assumed that FC which settled into the bottom could not be resuspended and exchanged back to the surface layer. Wilkinson et al. (1995) developed a simple river model that incorporated the release of FC from a sediment store. They also performed some initial calibration of the model by manipulating stream flows and measuring bacterial concentrations at different flow rates, similar to McDonald et al. (1982). Their model uses a mass balance approach in which the river reach is treated as a two-box system: organisms in the flow and those in the sediment. Within their model are terms to describe entrainment of microorganisms from the channel bed, resettlement within the channel bed, and losses due to die-off. The entrainment of microorganisms from the channel bed was assumed to increase linearly with increases in discharge. The model was calibrated for a situation in which the flow rate in a stream is

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incrementally increased during a short time period. Die-off processes and any extraneous bacterial inputs to the stream such as surface runoff, interflow or baseflow were ignored. The resettlement of bacteria after the high flow period was also not modeled. Wilkinson et al. (1995) stressed that this was an initial attempt to model the impact of a sediment bacterial store and that the model would have to be improved and calibrated to handle natural flow events and extended time periods where resettlement (redistribution) and die-off of bacteria will play a role. Tian et al. (2002) incorporated sediment associations within the stream routing component of their watershed model. A bacteria mass balance is performed on the sediment and water components of each stream reach on a daily time step. A flow volume value (vol0 ) is entered by the user to specify the threshold between resuspension and deposition. If the flow volume is below the threshold, deposition of bacteria occurs. The fraction of bacteria which will deposit is computed by:
 vol SED = exp − (3) vol0 − 0.66 vol where SED is the fraction of bacteria which will deposit on the stream bed and vol is the volume of water moving through the stream reach during the time step. When vol is >vol0 , resuspension of bacteria occurs. The resuspension rate is computed by:
 vol − vol0 RS = 1 − exp − (4) Qr where RS is the daily resuspension rate and Qr is the parameter controlling the resuspension rate. After each time step, the bed population is updated to account for losses due to die-off, deposition and resuspension. 7. Calibration of watershed scale microbial water quality models Calibration of watershed scale microbial water quality models has been limited. Tian et al. (2002) calibrated their model with observed data from a small (140 ha) grazed watershed in New Zealand. Their model explained 50% of the variation in field measurements, however, the model was only calibrated with monthly data. Wilkinson et al. (1995) calibrated their model for single hydrograph events generated from dam releases. Kleen et al. (2002) modeled microbial water quality in four small watersheds using the hydrologic simulation program (HSPF). The HSPF simulates bacteria as a free-floating contaminant with no sediment associations. The watersheds were dominated by rural land uses. The primary sources of fecal pollution were septic systems, manure application, grazing animals, and several small-scale wastewater treatment plants. The models were calibrated using monthly FC measurements and then used to generate remediation scenarios without validation. 8. Conclusions and recommendations This paper has reviewed a variety of approaches used to predict microbial water quality processes at the watershed scale. GIS-based, pollution index models, such as the system

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developed by Fraser et al. (1998) could be useful in characterizing the relative pollution potential in large spatially variable watersheds. However, due to key knowledge deficiencies, it is concluded that current physically based, continuous watershed models are limited in their predictive capacities. Nonetheless, physically based models provide a logical framework in which researchers can investigate and relate survival/transport processes. The primary conclusions and recommendations of this review are: 1. Characterizing the spatial and temporal patterns of waste deposition throughout a watershed is time consuming and challenging. GIS could be a valuable tool for characterizing the spatial nature of microbial pollution sources. The effectiveness of GIS, however, is governed by the data used to construct it. Detailed livestock census data is lacking in most jurisdictions. Major source characterization knowledge deficiencies, which require further research, include: (i) loading from septic systems, (ii) estimating the amount of fecal material directly deposited into streams from grazing livestock, and (iii) characterizing wildlife or “background” levels of microbial pollution. 2. The survival of enteric microorganisms in soils and freshwater environments has received the greatest attention in the literature. The majority of research has found that survival can be modeled as a first-order decay process. However, several studies have shown that microbial inactivation can deviate from first-order kinetics, particularly in nutrient-rich sediments. In situ survival studies should be conducted to provide information on the pattern of decline of enteric bacteria in nutrient-rich streams to improve modeling efforts. 3. Although overland flow is recognized as the primary transport mechanism of microorganisms to receiving streams, the physical processes involved in the movement of microorganisms on the land surface has received little attention. Surface transport studies have largely been conducted at the field or small watershed scale and have provided little insight into surface transport processes. The linkage between runoff characteristics (i.e. rate and volume) and microorganism transport should be evaluated at a smaller scale. More specifically, the relationship between microorganism transport and sediment transport requires further investigation. The assumption that microorganism transport is directly linked with sediment transport may not be valid. 4. SWAT is the only model which has explicitly attempted to partition microorganisms into adsorbed and non-adsorbed fractions. Unfortunately, reliable data on bacteria partitioning is currently not available. The adsorption characteristics of microorganisms in heterogeneous soils and sediments requires further study. It is still unclear as to whether bacteria adsorption should be modeled as a reversible or irreversible process. 5. Landscape modeling approaches do not account for subsurface transport of microorganisms, although several field scale studies have shown that subsurface transport can be significant, especially within tile drained land. Efforts should be made to incorporate this transport process within watershed scale models. 6. Sediment-associated microorganism resuspension and deposition rates in streams are simulated as simple empirical functions of discharge. No attempt has been made to assess the movement of microorganisms by directly modeling the sediment particles to which they are attached. The pattern and magnitude of bacteria resuspension and deposition in streams should be related to the sediment particles to which they are attached.

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7. An additional process that has not been addressed is the passive release of microorganisms from the stream bed due to interstital flow, which could occur in the absence of sediment resuspension. 8. The majority of research on transport has focussed on indicator bacteria. The physical and chemical properties of pathogens, which influence transport, require more detailed research. The behaviour of the protozoans Cryptosporidium and Giardia, in particular, are poorly understood. The extent of adsorption of oocysts to soils and fluvial sediments is still a topic of debate. This review has illustrated that the prediction of microbial water processes at the watershed scale is complex and challenging. The combined efforts of microbiologists, soil scientists, engineers and hydrologists will be needed to fully understand these processes and to further the development of microbial water quality models.

Acknowledgements This work was supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada, The Ontario Ministry of Agriculture and Food and Agriculture and Agri-Food Canada.

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