Philip Schmidt

Granular media filtration remains a critical treatment process and regulatory requirement for managing pathogenic protozoa in drinking water. It is a dynamic process in which performance inherently varies. While research has focused on characterizing or maximizing (oo)cyst removal in individual filters, the risk implications of combinations of filters moving through different phases of the filter cycle (leading to temporal variation in plant‐scale performance) have not been described. Increasing threats from climate‐change‐exacerbated landscape disturbances leading to more variable source water quality emphasize the need for such evaluations. Here, a modeling framework was developed to investigate the impacts of individual filter performance variation on plant‐scale performance. It is shown that improving maximal removal during stable operation does not necessarily improve average performance. The effect of other design and operational strategies like increasing the number of filters or implementing proactive operations (e.g., avoiding breakthrough) are analyzed, thereby providing guidance for increasing treatment resilience.