No abstract

The implementation of microbial fecal source tracking (MST) methods in drinking water management is limited by the lack of knowledge on the transport and decay of host-specific genetic markers in water sources. To address these limitations, the decay and transport of human (BacH) and ruminant (BacR) fecal Bacteroidales 16S rRNA genetic markers in a drinking water source (Lake Rådasjön in Sweden) were simulated using a microbiological model coupled to a three-dimensional hydrodynamic model. The microbiological model was calibrated using data from outdoor microcosm trials performed in March, August, and November 2010 to determine the decay of BacH and BacR markers in relation to traditional fecal indicators. The microcosm trials indicated that the persistence of BacH and BacR in the microcosms was not significantly different from the persistence of traditional fecal indicators. The modeling of BacH and BacR transport within the lake illustrated that the highest levels of genetic markers at the raw water intakes were associated with human fecal sources (on-site sewers and emergency sewer overflow). This novel modeling approach improves the interpretation of MST data, especially when fecal pollution from the same host group is released into the water source from different sites in the catchment.

BackgroundThe river Göta Älv is a source of freshwater for 0.7 million swedes. The river is subject to contamination from sewer systems discharge and runoff from agricultural lands. Climate models projects an increase in precipitation and heavy rainfall in this region. This study aimed to determine how daily rainfall causes variation in indicators of pathogen loads, to increase knowledge of variations in river water quality and discuss implications for risk management.MethodsData covering 7 years of daily monitoring of river water turbidity and concentrations of E. coli, Clostridium and coliforms were obtained, and their short-term variations in relation with precipitation were analyzed with time series regression and non-linear distributed lag models. We studied how precipitation effects varied with season and compared different weather stations for predictive ability.ResultsGenerally, the lowest raw water quality occurs 2 days after rainfall, with poor raw water quality continuing for several more days. A rainfall event of >15 mm/24-h (local 95 percentile) was associated with a three-fold higher concentration of E. coli and 30% higher turbidity levels (lag 2). Rainfall was associated with exponential increases in concentrations of indicator bacteria while the effect on turbidity attenuated with very heavy rainfall. Clear associations were also observed between consecutive days of wet weather and decreased water quality. The precipitation effect on increased levels of indicator bacteria was significant in all seasons.ConclusionsRainfall elevates microbial risks year-round in this river and freshwater source and acts as the main driver of varying water quality. Heavy rainfall appears to be a better predictor of fecal pollution than water turbidity. An increase of wet weather and extreme events with climate change will lower river water quality even more, indicating greater challenges for drinking water producers, and suggesting better control of sources of pollution.

A failure in treatment or in the distribution network of a surface water-works could have serious consequences due to the variable raw water quality in combination with an extended distribution. The aim of this study was to examine the theoretical impact of incidents in the drinking water system on the annual risk of infection in a population served by a large water treatment plant in Sweden. Reported incidents in the system were examined and a microbial risk assessment that included three pathogens, Cryptosporidium parvum, rotavirus and Campylobacter jejuni, was performed. The main risk incidents in water treatment were associated with sub-optimal particle removal or disinfection malfunction. Incidents in the distribution network included cross-connections and microbial pollution of reservoirs and local networks. The majority of the annual infections were likely to be due to pathogens passing treatment during normal operation and not due to failures, thus adding to the endemic rate. Among the model organisms, rotavirus caused the largest number of infections. Decentralised water treatment with membranes was also considered in which failures upstream fine-pored membranes would have little impact as long as the membranes were kept intact.