Water Clarity-Based Prediction of Faecal Bacterial Density In New Zealand Rivers

Regulatory authorities in New Zealand have communication strategies as part of their monitoring programmes to ensure the public is informed of a health risk at a swimming site, for example through the release of swimming advisories when E.coli levels exceed single sample bathing water criterions. By the time the swimming advisories are released, however, they are typically at least a day or two late, due to the 24-48h turn-around time before results from culture-based microbiological analysis are available. Methods are thus needed that improve the timeliness and accuracy of recreational water quality risk assessments. An important strategy until reliable continuous monitoring is in place, is to combine existing monitoring programmes with predictive models. While predictive faecal indicator bacteria (FIB) models have been used to estimate bacteriological water quality at some swimming sites, these models are largely ‘top-down’ in their approach to safeguarding public health. Beyond being simply ‘advised when to avoid swimming’, there is an increasing awareness amongst the general public regarding the role they can play in water quality monitoring. This presents novel opportunities for citizen participation in predictive FIB monitoring and modeling. This study reports on the possibility of developing intuitive, public-friendly models that are based on the physical appearance of water (clarity) as a predictive variable in estimating the E.coli concentrations in rivers, and to assess if water is safe to swim in. The goal of this study was to evaluate the possibility of water clarity-based E.coli models for now-cast prediction, at local and national scales in New Zealand.

Using an easily measured parameter (water clarity), this study calibrated and validated models that could be used by anyone, without the need for specialist technical knowledge. The models allow the user to assess whether or not it is safe to swim in their local waterways. At a national scale, the applicability of water clarity as a surrogate for E.coli concentration was also assessed using a total of 8103 E. coli datasets that have been routinely collected over the past two decades by regional authorities, for most New Zealand rivers and tributaries. Our results show that if swimmers were to avoid river waters with <1.1 m black disc visibility during autumn and summer or river waters with <0.5 during spring and winter, they would also avoid microbial hazards that are associated with exceedances of the 540 CFU/100 mL single sample bathing water standard. Regardless of the climatic season modelled, the clarity-based E.coli models performed well as they presented with sensitivity, specificity and accuracy values of at least 73%. The developed models offer the benefit of providing a faster method for estimating E. coli concentration, potentially engaging the public in water monitoring, and allowing them to make informed decisions on whether it is safe to swim at their favourite swimming spot.