Regression models for predicting the die-off rate of E. coli in solar water disinfection

Abstract

Solar disinfection (SODIS) is an economical and user-friendly method of water purification mostly applied in the tropical countries. SODIS uses the ultraviolet (UV) and temperature effects of the sun to kill pathogens in drinking water. The objective of the study was to develop a functional relationship between pathogen die-off rate and SODIS treatment conditions, namely daily maximum of 5-h averages of UV intensity , daily maximum water temperature , and water turbidity . Regression analyses/models based on 5-month data revealed that although and can be excellent predictors of pathogen die-off rate, their variable inflation factors (6.75 and 6.57, respectively), which is a measure of dependence between the two variables and adequacy of the estimated regression coefficients, suggest that they cannot be successfully combined in a single least-square regression model. was found to be a better predictor of in SODIS than . Water turbidities in the range of 1–30 NTU had no significant effect on the pathogen die-off rate. The study concluded that regression models/analyses can be successfully employed to explain the day-to-day variability in pathogen die-off rate, predict SODIS applicability, and exposure period in different regions of the world based on UV, water temperature, and water turbidity. HIGHLIGHTS Water temperature (Tm) can explain about 87% of daily variation in E. coli die-off rate.; UV intensity (UV5) can explain about 84% of daily variation in E. coli die-off rate.; In the Tropics, UV5 and Tm are highly correlated.; Combining UV5 and Tm in a single least-square regression model leads to false models.; Water turbidities in the range of 1–30 NTU do not have any significant effect on SODIS processes.;

Keywords

• drinking water
• exposure period
• multicollinearity
• temperature
• turbidity
• ultraviolet