J Water Health 03 (2005) 349-358
Spatial and rainfall related patterns of bacterial contamination in Sydney Harbour estuary
Grant C. Hose, Brad R. Murray, Geoff Gordon, Fiona E. McCullough and Nicholas Pulver
Institute for Water and Environmental Resource Management, University of Technology, Sydney, PO Box 123, Broadway, NSW 2007, Australia, Tel.: +61 2 9514 4087, Fax: +61 2 9514 4095, grant.hose@uts.edu.au
Institute for Water and Environmental Resource Management, University of Technology, Sydney, PO Box 123, Broadway, NSW 2007, Australia, Tel.: +61 2 9514 4087, Fax: +61 2 9514 4095, grant.hose@uts.edu.au
Ecotoxicology and Water Science Section, New South Wales Department of Environment and Conservation, PO Box A290, Sydney South, NSW 1232, Australia
Environmental Protection and Operations Division, New South Wales Department of Environment and Conservation, PO Box 668, Parramatta, NSW 2124, Australia
Beachwatch Programs Section, New South Wales Department of Environment and Conservation, PO Box A290, Sydney South, NSW 1232, Australia
ABSTRACT
Water quality in recreational areas in Sydney Harbour, Australia, was analysed first to identify spatial patterns in faecal coliform and enterococci densities, and then to determine the relationship between bacterial densities and catchment rainfall. Non-metric multidimensional scaling separated sites closest to the mouth of the harbour from those further up the harbour's west and north-west arms. Sites closest to the harbour mouth generally had lower frequencies of high bacterial densities that exceeded median water quality guideline values. We attribute this to greater tidal flushing at sites closer to the harbour mouth. Eight site groups were identified within the harbour. Within each group, multiple regression analyses indicated rainfall accounted for between 15 and 66% of the variability in the bacterial densities. Variation in bacterial densities explained by rainfall was lower for sites closer to the harbour mouth where tidal flushing is greatest. Thus, our findings indicate that simple rainfall-based regression models are appropriate for predicting bacterial concentrations when flushing at a site is limited. More complex models incorporating a suite of environmental variables may improve the ability to predict bacterial concentrations at well-flushed sites, but even then, their predictive ability may be low.
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