Seeing is Predicting: Water Clarity-Based Nowcast Models for E. coli Prediction in Surface Water

Given the 24–48 h turn-around time of conventional surveillance approaches, methods are needed that improve the timeliness and accuracy of recreational water quality risk assessments. Although one useful approach is to combine existing monitoring programmes with predictive faecal indicator bacteria (FIB) models, 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. Using quantile, maximum value and optimized incremental modelling approaches, this study reports on the possibility of developing intuitive, public-friendly models that are based on the physical appearance of water (clarity), to estimate 8103 nation-wide E. coli concentrations in rivers, and to assess whether water is safe to swim in. If swimmers were to avoid river waters with <1.1 m black disc visibility during autumn and summer, and river waters with values <0.5 m black disc visibility 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, stream order classification, catchment land cover or geology of streams considered, the clarity-based E. coli models performed well as they presented with sensitivity, specificity and accuracy values of at least 72%. 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.