Animal Waste, Water Quality and Human Health

Indicators, sanitary surveys and source attribution techniques 323employed in tandem with this technique, may provide more useful information(Savitcheva & Okabe 2006). As part of a source tracking study, researchers inIdaho examined both faecal streptococci and E. coli ratios in dry and wetmanure, alongside other source tracking methods, as a means of assessingstream loading, with favourable results (Weaver et al. 2005).It has further been suggested that factors other than measured levels of FIOsshould be taken into account when monitoring ambient water quality (WHO1999). Non-human sources, such as avian species, are known to carry entericpathogens whose genus/species have strains known to infect and cause diseasein humans (Ferns & Mudge 2000, Newell 2002, Haag Wackernagel & Moch2004, Tizard 2004, Kinzelman et al. 2005) but their ability to transmit theseorganisms to humans within the context of ambient water exposure is as yetuntested by epidemiological studies. Alternative approaches would ideally beable to take into account the source of the contamination, providing a betterindication of health risk. The feasibility of a health-based monitoring approachwas proposed as the result of an expert consultation sponsored by the WHO andUSEPA (WHO 1999). A classification scheme for recreational waters based onhealth risk was suggested. Identification of the pollution source and recognitionof factors such as precipitation and wave action, which can influence thecondition of bathing water quality, should be part of assigning a water bodyclassification. The collection of environmental data may also be useful in thedevelopment of real-time assessment (Haugland et al. 2005, Griffith et al. 2009,Heijnen & Medema 2009) and water quality prediction methods.9.2.2 New approaches – predictive modelingPredictive models have been suggested as a means of utilizing site-specific datathat can be collected on a real-time basis as an alternative or in conjunction withmicrobial indicator testing (Bruesch & Biedrzycki 2002, Breusch & Biedrzycki2003, Olyphant et al. 2003, Collins & Rutherford 2004, Olyphant 2004, Whitman2004). Predictive modelling a surrogate for laboratory analyses, uses ambientconditions (temperature, precipitation, insolarity, wave height, wind speed/direction and other parameters) and hydrodynamic variables at the local orregional level to predict the outcome of bacteriologic analyses before they occur.For example, the Scottish Environment Protection Agency (SEPA) and ScottishGovernment have used their extensive national on-line hydrometric network ofrain and river monitoring across Scotland to develop “nowcast” models whichprovide real-time public information and advice for bathers (see: Chapter 8)(McPhail & Stidson 2009). Varying degrees of success have also beendemonstrated by these models in marine waters and the Great Lakes region of the