Stochastic modeling of Cryptosporidium parvum to predict transport, retention, and downstream exposure

Rivers are a means of rapid and long-distance transmission of pathogenic microorganisms from upstream terrestrial sources. Thus, significant fluxes of pathogen loads from agricultural lands can occur due to transport in surface waters. Pathogens enter streams and rivers in a variety of processes, notably overland flow, shallow groundwater discharge, and direct inputs from host populations such as humans and other vertebrate species. Viruses, bacteria, and parasites can enter a stream and persist in the environment for varying amounts of time. Of particular concern is the protozoal parasite, Cryptosporidium parvum, which can remain infective for weeks to months under cool and moist conditions, with the infectious state (oocysts) largely resistant to chlorination. In order to manage water-borne diseases more effectively we need to better predict how microbes behave in freshwater systems, particularly how they are transported downstream in rivers and in the process interact with the streambed and other solid surfaces. Microbes continuously immobilize and resuspend during downstream transport due to a variety of processes, such as gravitational settling, attachment to in-stream structures such as submerged macrophytes, and hyporheic exchange and filtration within underlying sediments. These various interactions result in a wide range of microbial residence times in the streambed and therefore influence the persistence of pathogenic microbes in the stream environment. We developed a stochastic mobile-immobile model to describe these microbial transport and retention processes in streams and rivers that also accounts for microbial inactivation. We used the model to assess the transport, retention, and inactivation of C. parvum within stream environments, specifically under representative flow conditions of California streams where C. parvum exposure can be at higher risk due to agricultural nonpoint sources. The results demonstrate that the combination of stream reach-scale analysis and multi-scale stochastic modeling improves assessment of C. parvum transport and retention in streams in order to predict downstream exposure to human communities, wildlife, and livestock.