Predicting the transport of Escherichia coli to groundwater

Groundwater contamination with pathogens poses a health risk worldwide. Predictive modeling could provide decision support for risk analysis in this context. This study therefore aimed to improve predictive modeling of the transport of Escherichia coli (E. coli) to groundwater. Primarily, it included a review of the state-of-the-art of the underlying process, influencing factors and modeling approaches that relate to E. coli transport in the unsaturated zone. Subsequently, two recently developed models were innovatively applied to the context of microbial contamination. The Active Region Model was evaluated as an alternative to the traditional, uniform flow model (Richard’s equation) to describe bacterial transport in a wastewater treatment facility. It resulted in removal rates that were two orders of magnitude smaller than the traditional approach, more consistently with observations. The study moreover assessed the relevance of a spatial probit model to estimate the probability of groundwater source contamination with thermotolerant coliforms in a case study in Juba, South Sudan. A conventional probit regression model resulted in spatially auto-correlated residuals, pointing to that the spatial model was more accurate. The results of this approach indicated that the local topography and the near presence of areas with informal settlements (Tukul zones) were associated with contamination. Statistical analyses moreover suggested that the depth of cumulative, long-term antecedent rainfall and on-site hygiene were significant risk factors. The findings indicated that the contributing groundwater was contaminated in Juba, and that contamination could be both local and regional in extent. They are relevant for environments with similar climatic, hydrogeological and socioeconomic characteristics, which are common in Sub-Saharan Africa. The results indicated that it is important to consider spatial interactions in this subject area. There is a need for studies that assess the distance within which such interactions can occur, using both mechanistic and statistical methods. Lastly, the results in this study consistently emphasized the importance of flow patterns for E. coli transport. It is thus recommended that future studies evaluate how models of preferential flow and transport can incorporate microbial fate. The multidisciplinary nature of the subject calls for a systems approach and collaboration between disciplines.QC 20150529