Modelling of river faecal indicator bacteria dynamics as a basis for faecal contamination reduction

Majedul Islam, M.M.; Sokolova, Ekaterina; Hofstra, Nynke


To improve microbial water quality and to prevent waterborne disease outbreaks, knowledge on the fate and transport of contaminants and on the contributions from different faecal sources to the total contamination is essential. The fate and transport of faecal indicators E. coli and enterococci within the Betna River in Bangladesh were simulated using a coupled hydrodynamic and water quality model. The hydrodynamic model for the river was set up, calibrated and validated with water level and discharge in our earlier study. In this study, the hydrodynamic model was further validated using measured water temperature and salinity and coupled with the water quality module. Bacterial load data from various faecal sources were collected and used as input in the water quality model. The model output corresponded very well with the measured E. coli and enterococci concentrations in the river; the Root Mean Square Error and the Nash-Sutcliffe efficiency for Log10-transformed concentrations were found to be 0.23 (Log10 CFU/100 ml) and 0.84 for E. coli, and 0.19 (Log10 CFU/100 ml) and 0.86 for enterococci, respectively. Then, the sensitivity of the model was tested by removing one process or forcing at a time. These simulations revealed that the microbial decay, the upstream concentrations and the discharge of untreated wastewater were the primary factors controlling the concentrations in the river, while wind and the contribution from the diffuse sources (i.e. urban and agricultural runoff) were unlikely to have a major influence. Finally, the model was applied to investigate the influence of wastewater treatment on the bacteria concentrations. This revealed that wastewater treatment would result in a considerable improvement of the microbial water quality of the Betna River. This paper demonstrates the application of a comprehensive state-of-art model in a river in a data-poor tropical area. The model can potentially be applied to other watersheds and can help in formulating solutions to improve the microbial water quality.