ReviewAssessing microbial pollution of rural surface waters: A review of current watershed scale modeling approaches
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 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.
Section snippets
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
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
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).
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
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
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
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 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,
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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