Estimation of pathogen concentrations in a drinking water source using hydrodynamic modelling and microbial source tracking

Abstract

The faecal contamination of drinking water sources can lead to waterborne disease outbreaks. To estimate a potential risk for waterborne infections caused by faecal contamination of drinking water sources, knowledge of the pathogen concentrations in raw water is required. We suggest a novel approach to estimate pathogen concentrations in a drinking water source by using microbial source tracking data and fate and transport modelling. First, the pathogen (norovirus, Cryptosporidium, Escherichia coli O157/H7) concentrations in faecal contamination sources around the drinking water source Lake Rådasjön in Sweden were estimated for endemic and epidemic conditions using measured concentrations of faecal indicators (E. coli and Bacteroidales genetic markers). Afterwards, the fate and transport of pathogens within the lake were simulated using a three-dimensional coupled hydrodynamic and microbiological model. This approach provided information on the contribution from different contamination sources to the pathogen concentrations at the water intake of a drinking water treatment plant. This approach addresses the limitations of monitoring and provides data for quantitative microbial risk assessment (QMRA) and risk management in the context of faecal contamination of surface drinking water sources.

Assessing the impact of socio-economic development and climate change on faecal indicator bacteria in the Betna River, Bangladesh

“…Nguyen et al (2016) also reported in their modelling study that diffuse sources had substantial contributions to the total coliform concentrations in the Vietnamese Red River, but that was unlikely to increase near future (2050s) concentrations significantly. Comparatively lower diffuse source contributions are also reported in other studies (Ouattara et al, 2013, Sokolova et al, 2012, Gao et al, 2015.…”

“…While diffuse sources (urban and agricultural runoff) contributed less, up to 9.3% for E. coli and 12.6% for enterococci. Comparatively lower contribution from diffuse sources was also reported in other studies (Ouattara et al, 2013, Sokolova et al, 2012, Gao et al, 2015. During the wet period, a higher contribution from the upstream boundary is expected, because the river here is connected to the Bhairab River in the north and carries contaminated runoffs from the upstream urban and agricultural areas.…”

“…These simulations revealed that the decay process, upstream boundary FIB inputs and untreated wastewater discharge are the primary factors controlling FIB concentrations and their variability, while tide, wind and diffuse sources (urban and agricultural runoff) were unlikely to have a major influence. Comparatively lower diffuse source contributions are also reported in other studies (Ouattara et al, 2013, Sokolova et al, 2012, Gao et al, 2015. Finally, the model was applied to investigate the influence of wastewater treatment on FIB concentrations.…”

Molecular Analyses of Fecal Bacteria and Hydrodynamic Modeling for Microbial Risk Assessment of a Drinking Water Source

“…In Sweden, coupling fate and transport modeling with QMRA is still relatively rare, but some examples exist [23] and simplified methods have been developed for practical application to meet the increasing interest and need, see e.g., Åström and Johansson [24], Åström, Lindhe, Bergvall, Rosén, and Lång [17]. Additionally, an approach combining incidence data with hydrodynamic modeling to estimate pathogen concentrations for QMRA and the effect of mitigation measures has been suggested [25,26]. Measurements of FIB and pathogen concentrations would enhance the applicability of the fate and transport modeling and add to the overall risk assessment.…”

Hydrodynamic modelling and forecasting of microbial water quality in a drinking water source

“…According to the results of another study (Sokolova et al 2012b), the 105 contribution from the cattle grazing area to the concentrations of Bacteroidales genetic markers at 106 the water intake was approximately 1 log 10 unit less compared to the contributions from the on-site 107 systems and from the Pixbo Päls pumping station. Furthermore, in a study on the pathogen 108 concentrations in this lake (Sokolova et al 2012a), it was found that the contributions from the cattle 109 grazing area and the urban area to the Cryptosporidium concentrations at the water intake were 110 approximately 1 and 3 log 10 units less than from the Pixbo Päls pumping station, respectively. In 111 summary, based on the results of the earlier studies of this lake, we have concluded that the 112 contribution from these nonpoint sources is generally much smaller than from the other identified 113 contamination sources.…”