E. coli kinetics--effect of temperature on the maintenance and respectively the decay phase

The knowledge of enteric bacteria survival kinetic is very important for environmental scientists. Enteric bacteria andspecifically the fecal indicator bacteria are typically used to measure the sanitary quality of water for recreational, industrial, agricultural and water supply purposes. They are released into the environment with feces, and are then exposedto a variety of environmental conditions that eventually causetheir death. In general, it is believed that the fecal indicatorcannot grow in natural environments, since they are adapted to live in the gastrointestinal tract. Studies have shown that fecalindicator bacteria survive from a few hours up to several daysin surface water, but may survive for days or months in lake-sediments, where they may be protected from sunlight andpredators. We assume that pathogens similar to the fecal indicator bacteria die at the same rate as fecal indicator bacteria. Therefore, if we find relatively high numbers of fecalindicator bacteria in an environment, we assume that there is anincreased likelihood of pathogens being present as well. The kinetic of enteric bacteria survival in natural waters is affected by a large number of factors. One of them is the temperature. The aim of this contribution was the experimentalresearch of the survival kinetic of enteric bacteria applying a simple mathematical formula, which describes the survival kineticpredicting the decay phase at various temperatures. We aspire that the results will lead both to the solution of many engineering problems and to future research.     

Estimating the Microbial Risk of E. coli in Reclaimed Wastewater Irrigation on Paddy Field

Microbial risk was quantified to assess human health risk as a result of exposure to E. coli in reclaimed wastewater irrigation. Monitoring data on E. coli were collected from pond water in paddy rice plots during the growing season. Five treatments were used and each was triplicated to evaluate the changes in E. coli concentrations in experiments performed in 2003 and 2004. The Beta-Poisson model was used to estimate the microbial risk of pathogen ingestion among farmers and neighboring children. A Monte Carlo simulation (10,000 trials) was conducted to estimate the risk associated with uncertainty. In this study, risk values ranged from 10⁻⁴ to 10⁻⁸. UV-disinfected irrigation water showed a lower risk value than others, and its level was within the range of the actual paddy rice field with surface water. Agricultural activity was thought to be safer after 1–2 days, when the paddy field was irrigated with reclaimed wastewater. Also, children were found to have a greater risk of infection with E. coli. This paper should be viewed as a first step in the application of quantitative microbial risk assessment of wastewater reuse in paddy rice culture.     

Characterization and statistical modeling of bacterial (Escherichia coli) outflows from watersheds that discharge into southern Lake Michigan

Two watersheds in northwestern Indiana were selected for detailed monitoring of bacterially contaminated discharges (Escherichia coli) into Lake Michigan. A large watershed that drains an urbanized area with treatment plants that release raw sewage during storms discharges into Lake Michigan at the outlet of Burns Ditch. A small watershed drains part of the Great Marsh, a wetland complex that has been disrupted by ditching and limited residential development, at the outlet of Derby Ditch. Monitoring at the outlet of Burns Ditch in 1999 and 2000 indicated that E. coli concentrations vary over two orders of magnitude during storms. During one storm, sewage overflows caused concentrations to increase to more than 10,000 cfu/100 mL for several hours. Monitoring at Derby Ditch from 1997 to 2000 also indicated that E. coli concentrations increase during storms with the highest concentrations generally occurring during rising streamflow. Multiple regression analysis indicated that 60% of the variability in measured outflows of E. coli from Derby Ditch (n = 88) could be accounted for by a model that utilizes continuously measured rainfall, stream discharge, soil temperature and depth to water table in the Great Marsh. A similar analysis indicated that 90% of the variability in measured E. coli concentrations at the outlet of Burns Ditch (n = 43) during storms could be accounted for by a combination of continuously measured water-quality variables including nitrate and ammonium. These models, which utilize data that can be collected on a real-time basis, could form part of an Early Warning System for predicting beach closures.     

Using Multiple Antibiotic Resistance and land use characteristics to determine sources of fecal coliform bacterial pollution

Multiple Antibiotic Resistance (MAR) analysis and regression modeling techniques were used to identify surface water areas impacted by fecal pollution from human sources, and to determine the effects of land use on fecal pollution in Murrells Inlet, a small, urbanized, high-salinity estuary located between Myrtle Beach and Georgetown, South Carolina. MAR analysis was performed to identify areas in the estuary that are impacted by human-source fecal pollution. Additionally, regression analysis was performed to determine if an association exists between land use and fecal coliform densities over the ten-year period from 1989 to 1998. Land-use variables were derived using Geographic Information System (GIS) techniques and were used in the regression analysis.MAR analyses were conducted by comparing the frequency and patterns of antibiotic resistance found in Escherichia coli isolates derived from surface water samples and from sewage sources in the Murrells Inlet sewage collection system. The MAR results suggest that the majority of the fecal pollution detected in the Murrells Inlet estuary may be from non-human sources, including fecal coliforms isolated from areas in close proximity to high densities of active septic tanks.A MAR Index, which measures the frequency of antibiotic resistance, was calculated for each of twenty-three water samples and nine sewage samples. The antibiotic resistance pattern comparisons were performed using cluster analysis. Although the MAR indices indicated that several surface water sites had potential human-source contamination, the cluster analysis suggests that only one sampling site had MAR patterns that were similar to those found in the sewage samples. This site was in close proximity to several large pleasure boats as well as a sewage collection system lift station, but was not near areas with active septic tanks. The results of the regression analysis also suggest that sewage sources and rainfall runoff from urbanized areas may contribute to fecal pollution in the estuary.