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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. [ABSTRACT FROM AUTHOR]

Efforts to regulate and monitor emerging contaminants are insufficient because new chemicals are continually brought to market, and many are unregulated and potentially harmful. Domestic wastewater treatment plants are not designed to remove micropollutants and are important sources of emerging contaminants in the aquatic environment. In this study, non-target screening, an unbiased method for analyzing compounds without prior information, was used to identify compounds that may be emitted in wastewater treatment plant effluent and should be monitored. Nine wastewater treatment plants using different treatment methods were studied, and a non-target screening data-processing method was used. The frequencies at which the contaminants were detected and contaminant persistence through the treatment processes were considered, and then the contaminants were prioritized. The predicted no-effect concentration of each prioritized contaminant was used to determine whether further analysis and monitoring of the contaminant was necessary. Quantitative analyses of five compounds (amantadine, atenolol, benzotriazole, diphenhydramine, and sulpiride) were performed using reference standards. Probable molecular formulae and structures were proposed for 17 contaminants, and the risks posed by the contaminants were estimated using predicted no-effect concentrations. The results provide valuable insights into how unregulated micropollutants can be identified and prioritized for monitoring in future studies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)