Parramatta River

A Case Study of the Parramatta River, Australia
W Chirgwin, B Maheshwari
Publication Date (Web): 21 February 2018

In areas of scientific study relating to public health, the ability to effectively communicate results to the general public is crucial, both for directing public health initiatives, and developing public education strategies. However, this task is often difficult, especially when datasets include numerous parameters and units of measurement, such as for water quality. The most common solution in such cases is to create an aggregate measurement in the form of water quality index (WQI). However, the accuracy and interpretation of aggregate measurements can vary significantly, limiting their usefulness as a tool for public education. With this in mind, this study set out to compare two different models of WQI, the National Sanitation Foundation WQI (NWQI), and two formulations of the Canadian Council of Ministers for the Environment WQI, one using ANZECC guidelines, and one using modified parameter limits (CWQI & modified CWQI), to determine their relative accuracy, and potential as a tool for public education.

To do this, water quality data was collected at locations throughout the Upper Parramatta River; a highly urbanised river catchment in the Sydney Metropolitan area currently in the process of rehabilitation. The parameters for WQI considered were dissolved oxygen, pH, electrical conductivity, total dissolved solids, turbidity, phosphates (as filterable reactive phosphates) and nitrates. This study also measured biological parameters, including macro-invertebrate richness (as SIGNAL scores), and levels of faecal coliforms, total coliforms, Escherichia Coli (E. Coli) and enterococci. Finally, to provide data to analyse accuracy against, the corridor imperviousness of tributaries and the river as a whole was calculated using Grass GIS, as high levels of imperviousness are known to negatively impact on water quality. Once the indices were created, their behaviour was compared to three non-physiochemical factors known to be linked to water quality: macro-invertebrate richness, corridor imperviousness, and bacteriological indicators, using range comparison and regression analysis.

Map of Sample Areas
Figure 1: Map of Sample Areas. The above map showcases the study areas used in this study. Study areas match up with the points used in other figures.

Analysis of the WQI's found that the NWQI performed poorly, universally over-estimating water quality, in line with what had been found with other studies. The unadulterated CWQI performed better, matching the range suggested by SIGNAL scores, however, its performance in relation to catchment imperviousness was weak, and higher bacteriological levels counter-intuitively correlated to better WQI results. When the nutrient limit values for the CWQI model were modified to be more reflective of background levels, the resulting index was found to perform well compared to SIGNAL data, and showed an extremely high level of correlation with corridor imperviousness. However, it suffered from the same flaw as the unadulterated CWQI when compared to bacteriological parameters. Based on these results, this study cannot recommend the use of any index analysed here for meaningful analysis. However, it does recommend that a modified CWQI be considered for use in public education and preliminary assessment, as well as a starting point for the formulation of an Australian WQI.

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