Development of a physically-based model for transport of Cryptosporidium parvum in overland flow

Abstract

Surface runoff from animal production facilities contains numerous microbial pathogens which pose a direct health hazard to both humans and animals. In order to preserve safe water resources and sustainable agriculture by reducing runoff-mediated contamination of agricultural watersheds, microbial transport processes need to be properly understood and quantified. Cryptosporidium parvum is a protozoan parasite responsible for the largest outbreak of waterborne disease ever recorded in US history. Infected mammals can pass as many as 10 billion Cryptosporidium oocysts per gram of faeces, hence only a few infected animals can potentially contaminate a large watershed. Little information is available on the environmental or physicochemical factors governing the transport of microbial pathogen in surface and near-surface runoff, in particular the zoonotic pathogen Cryptosporidium. The objective of this study is to develop a physically-based model for simulating transport of C. parvum oocysts in overland flow, and to compare the model results with experimental observations. Transport of oocysts in overland flow can be simulated mathematically by including terms for the concentration of the oocysts in the liquid phase (in suspension or free-floating) and the solid phase (adsorbed to the fine solid particles like clay and silt). Oocysts adsorption and decay processes are considered. These processes are solved using numerical techniques to predict spatial and temporal changes in oocyst concentrations in solid and liquid phases. The model results are also compared with experimental results to validate the model. The model output reproduced the recovery kinetics satisfactorily, but under-predicted the total recovery in a few cases when multiple peaks were observed during experiments. Similarly, the calibrated model produced a good agreement between observed and modeled total oocysts recovery. With future modification, this model may provide a promising tool for developing effective management practices for controlling microbial pathogens in surface runoff.