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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 0,10 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 Q(10) 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 Q(10) equation was more accurate in rivers and coastal waters than in lakes making the value of the Q(10) 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. Published by Elsevier Ltd.