Innovative modeling of the physicobiochemical determinants of fecal indicator bacteria

The risk of infection by enteric pathogens in bathing waters is generally indicated by monitoring fecal indicator bacteria (FIB) concentrations. Mechanistic models are efficient tools for predicting FIB concentrations and corresponding contributions from various impact factors based on historical records and different climatic scenarios. However, most existing FIB physicobiochemical models are limited by the availability of FIB observations and knowledge of the physicobiochemical processes. Modeling studies that performed advanced sensitivity analyses or model comparisons to disentangle the contributions from different processes and impact factors, are rare.

To enhance the understanding of the relative importance of the various processes that affect FIB concentrations in different aquatic systems, we developed a comprehensive and generic FIB physicobiochemical model, including an improved die-off module and sediment interaction module. The new die-off module includes a cumulative endogenous photo-inactivation. By developing the relationship between dissolved organic carbon (DOC) concentrations and Ultraviolet diffuse attenuation coefficients, the module calculates the Ultraviolet-A (UVA) and Ultraviolet-B (UVB) extinction by waters. The penetrated UVA + UVB light under different wavelengths is used for endogenous photoinactivation rate calculation via the biological weighting function.  Distinct from using a constant partition rate in previous sediment interaction modules, the new sediment interaction module calculates the dynamic partition rate based on not only suspended sediment (SS) concentrations but also its composition via two different classes of SS: sand and clay.

Separate validation of the two sub-modules demonstrated the reliability of our modeling approach. Contrary to previous die-off modules, our new die-off module implied an improvement after adding UV endogenous photo-inactivation. According to sediment interaction module validation, the dynamic partitioning coefficient can reasonably allocate E. coli between water and sediment through sedimentation and resuspension, which is an essential precondition for incorporating sediment into the model as a reservoir for E. coli.

The sensitivity analysis result showed that 1) photo-inactivation is important in low DOC waters, but not in high DOC waters since the UV penetration is limited; 2) The impact of sediment interaction is insignificant under steady E. coli input conditions, but vital during and after a peak event. Interactions with sediments can extend the half-life of E. coli in water columns up to four times after a peak event.

This work demonstrated the significance of sediment interactions and DOC concentrations for predicting the duration of episodes of insufficient bathing water quality. The new generic module enables better simulation of bathing water quality across different types of aquatic environments and conditions. Future applications can choose processes selectively from the new FIB physicobiochemical model and couple it with hydrological or hydrodynamic models to address specific environmental conditions and research purposes.