Survival of Escherichia coli in stream water in relation to carbon dioxide and plant photosynthesis

Explaining and modeling the concentration and loading of Escherichia coli in a stream-A case study

Escherichia coli (E. coli) level in streams is a public health indicator. Therefore, being able to explain why E. coli levels are sometimes high and sometimes low is important. Using citizen science data from Fall Creek in central NY we found that complementarily using principal component analysis (PCA) and partial least squares (PLS) regression provided insights into the drivers of E. coli and a mechanism for predicting E. coli levels, respectively. We found that stormwater, temperature/season and shallow subsurface flow are the three dominant processes driving the fate and transport of E. coli. PLS regression modeling provided very good predictions under stormwater conditions (R² = 0.85 for log (E. coli concentration) and R² = 0.90 for log (E. coli loading)); predictions under baseflow conditions were less robust. But, in our case, both E. coli concentration and E. coli loading were significantly higher under stormwater condition, so it is probably more important to predict high-flow E. coli hazards than low-flow conditions. Besides previously reported good indicators of in-stream E. coli level, nitrate-/nitrite-nitrogen and soluble reactive phosphorus were also found to be good indicators of in-stream E. coli levels. These findings suggest management practices to reduce E. coli concentrations and loads in-streams and, eventually, reduce the risk of waterborne disease outbreak.

Comparing temperature effects on Escherichia coli, Salmonella, and Enterococcus survival in surface waters

The objective of this study was to compare dependency of survival rates on temperature for indicator organisms Escherichia coli and Enterococcus and the pathogen Salmonella in surface waters. A database of 86 survival datasets from peer-reviewed papers on inactivation of E. coli, Salmonella and Enterococcus in marine waters and of E. coli and Salmonella in lake waters was assembled. The Q10 model was used to express temperature effect on survival rates obtained from linear sections of semi-logarithmic survival graphs. Available data were insufficient to establish differences in survival rates and temperature dependencies for marine waters where values of Q10  = 3 and a survival rate of 0·7 day(-1) could be applied. The Q10 values in lake waters were substantially lower in marine waters, and Salmonella inactivation in lake water was, on average, twice as fast as E. coli; data on E. coli substantially outnumber data on Enterococcus and Salmonella. The relative increase in inactivation with increase in temperature is higher in marine waters than lake water, and differences in inactivation between Salmonella and E. coli at a given temperature were significant in lake water but not in marine waters.

SIGNIFICANCE AND IMPACT OF THE STUDY

Microbiological quality of surface waters is of paramount importance for public health. The novelty of this work is using a large compendium of published data to develop the first comparison of temperature effects on survival of the pathogen Salmonella and water quality indicator micro-organisms Escherichia coli and Enterococcus in natural waters. The existing relatively large body of knowledge on E. coli survival appears to be useful to assess the effect of temperature on survival of Salmonella. Moreover, results of this work constitute an essential input in models to support environmental management decisions on the use of surface water sources in agriculture, aquaculture and recreation.

Escherichia coli survival in waters: temperature dependence

Knowing the survival rates of water-borne Escherichia coli is important in evaluating microbial contamination and making appropriate management decisions. E. coli survival rates are dependent on temperature, a dependency that is routinely expressed using an analogue of the Q₁₀ model. This suggestion was made 34 years ago based on 20 survival curves taken from published literature, but has not been revisited since then. The objective of this study was to re-evaluate the accuracy of the Q₁₀ equation, utilizing data accumulated since 1978. We assembled a database of 450 E. coli survival datasets from 70 peer-reviewed papers. We then focused on the 170 curves taken from experiments that were performed in the laboratory under dark conditions to exclude the effects of sunlight and other field factors that could cause additional variability in results. All datasets were tabulated dependencies "log concentration vs. time." There were three major patterns of inactivation: about half of the datasets had a section of fast log-linear inactivation followed by a section of slow log-linear inactivation; about a quarter of the datasets had a lag period followed by log-linear inactivation; and the remaining quarter were approximately linear throughout. First-order inactivation rate constants were calculated from the linear sections of all survival curves and the data grouped by water sources, including waters of agricultural origin, pristine water sources, groundwater and wells, lakes and reservoirs, rivers and streams, estuaries and seawater, and wastewater. Dependency of E. coli inactivation rates on temperature varied among the water sources. There was a significant difference in inactivation rate values at the reference temperature between rivers and agricultural waters, wastewaters and agricultural waters, rivers and lakes, and wastewater and lakes. At specific sites, the Q₁₀ equation was more accurate in rivers and coastal waters than in lakes making the value of the Q₁₀ coefficient appear to be site-specific. Results of this work indicate possible sources of uncertainty to be accounted for in watershed-scale microbial water quality modeling.