Prediction of Sunlight- and Salinity-Driven Inactivation Kinetics of Microbial Indicators with Validation in a 3D Water Quality Model

Abstract

We conducted laboratory experiments under varied solar radiation and salinity levels to investigate their influences on the natural attenuation of multiple promising microbial indicators including fecal bacteria and two types of bacteriophages. Inactivation coefficients were estimated and compared following first-order kinetics. Somatic coliphage was found to be the most resistant, while fecal bacteria exhibited higher susceptibility to both factors. The estimated inactivation coefficients of E. coli were applied to a 3D water quality model and validated with a daily basis monitoring dataset. The validation revealed high consistency among modelled and monitored concentrations, with a less than 1-log concentration difference. Further, the effect of actual solar radiation and salinity on E. coli inactivation after a rainfall event was calculated and compared. The results exhibited that solar radiation is a stronger influential factor. Simulation illustrated that lower-strength radiation exposure can limit E. coli inactivation, enabling them to survive up to one week after combined sewer overflow (CSO) discharge. The model revealed a promising capacity as a tool for the timely prediction of the CSO-induced severity of microbial contamination and associated risk, as well as associated natural attenuation; thus, this model can enhance the competency of public water managers for decision making.

Analysis of Chlorophyll-a Variability in the Eastern Indonesian Waters Using Sentinel-3 OLCI from 2020-2021

“…et al, 2021). A Neural Network (NN) was trained for atmospheric correction and IOP determination, with additional inputs of temperature and salinity data required (Poopipattana c. et al, 2024). Atmospheric correction produced Water Leaving Radiance Reflectance (RLw) (Kulha et al, 2024).…”