Enhancing transport and decay models for faecal indicator organisms in nearshore coastal waters

Pathogens in nearshore coastal waters have far-reaching public health and economic implications. Faecal indicator organisms (FIOs) are commonly monitored and modelled to indicate pathogen levels in waterbodies. FIO decay modelling is an integral part of numerical hydro-epidemiological models to simulate the die-off of FIOs in the water bodies. This paper identifies the limitations of one of the comprehensive and widely used FIO decay models, developed by Stapleton et al. and enhances the model by remedying the limitations. The identified limitations are: (i) the decay rates for dark or highly irradiated environments are not accurately presented, and (ii) the effect of salinity is not included. Two enhanced models have been developed, namely (i) the ClipStap model, devised by imposing a minimum decay rate to the Stapleton model, and (ii) the RevStap model, devised by extrapolating the decay rate-irradiation slope at a reference irradiation ( 260 W / m 2 ) down to lower irradiation regions. The enhanced models reproduced the literature-reported dark decay rates better and significantly improved the agreement between the modelled and measured decay rate. The enhanced decay models were tested by including them in a hydro-epidemiological model for a data-rich case study, namely Swansea Bay, UK. Results show that the RevStap model improved FIO prediction in some cases. Besides the enhanced models, this research attributes the diurnal variations of FIO to the combined action of riverine FIO inflows, tide action, and FIO decay. These insights on the effect of irradiation and diurnal FIO variations are critical for assessing the impact of water quality on human activities and nearshore ecology. • The limitations of a comprehensive and widely used FIO decay model are identified •Enhanced models, ClipStap and RevStap, are developed to remedy the limitations • The Revised Stapelton model provides improved modelling for high irradiation • Enhanced FIO-hydro-epidemiological models gave better predictions in some cases • Daily FIO variations are caused by FIO inflows and decay and tide action