A modeling approach to estimate the solar disinfection of viral indicator organisms in waste stabilization ponds and surface waters
Sunlight is known to be a pertinent factor governing the infectivity of waterborne viruses in the environment. Sunlight inactivates viruses via endogenous inactivation (promoted by absorption of UVB sunlight by the virus) and exogenous processes (promoted by adsorption of sunlight by external chromophores, which subsequently generate inactivating reactive species). The extent of inactivation is still difficult to predict, as it depends on multiple parameters including virus characteristics, solution composition, season and geographical location. In this work, we adapted a model typically used to estimate the photodegradation of organic pollutants, APEX, to explore the fate of two commonly used surrogates of human viruses (coliphages MS2 and X174) in waste stabilization pond and natural surface water. Based on experimental data obtained in previous work, we modeled virus inactivation as a function of water depth and composition, as well as season and latitude, and we apportioned the contributions of the different inactivation processes to total inactivation. Model results showed that X174 is inactivated more readily than MS2, except at latitudes >60°. X174 inactivation varies greatly with both season (20-fold) and latitude (10-fold between 0 and 60°), and is dominated by endogenous inactivation under all solution conditions considered. In contrast, exogenous processes contribute significantly to MS2 inactivation. Because exogenous inactivation can be promoted by longer wavelengths, which are less affected by changes in season and latitude, MS2 exhibits smaller fluctuations in inactivation throughout the year (10-fold) and across the globe (3-fold between 0 and 60°) compared to X174. While a full model validation is currently not possible due to the lack of sufficient field data, our estimated inactivation rates corresponded well to those reported in field studies. Overall, this study constitutes a step toward estimating microbial water quality as a function of spatio-temporal information and easy-to-determine parameters.