Estimating the concentration of viral pathogens and indicator organisms in the final effluent of wastewater treatment processes using stochastic modelling

Abstract

The presence of waterborne microbial (including viral) pathogens, in wastewater poses a potential risk to human health when wastewaters are reused either directly or indirectly. Therefore, reuse activities need to be regulated in such a way as to protect human health and to this end, quantitative microbial risk assessment (QMRA) has been successfully used to formulate evidence-based reuse regulations. The QMRA approach depends, however, on reliable information about the various elements of the system, including the wastewater treatment component. One point of major concern is the determination of pathogen concentrations, especially viral pathogens, in treated wastewater, as a consequence of their low levels and problems associated with the detection limit of enumeration methods. Therefore, the research described here aimed to develop stochastic simulations from empirical data to estimate likely concentrations of specified enteric microorganisms in final effluents of municipal wastewater treatment plants based on either activated sludge (AS) or trickling filter (TF) as the secondary biological treatment stage and thereby support the construction of functional QMRA models. Wastewater samples were collected every fortnight, during a twelve-month period, at each stage of four full-scale wastewater treatment plants (WWTP) in southern England (two AS and two TF plants) (n = 360 samples) in order to build a robust dataset. Probability density functions (PDF) were then fitted to empirical data and used as input variables in the proposed model, which considered the concentration of the assessed micro-organisms in the raw wastewater and the removal rates in primary, secondary and tertiary treatment stages. Final concentrations of pathogenic and indicator organisms were then estimated using stochastic simulations. The proposed stochastic model was able to predict both accurately and reliably the likely concentration of microorganisms in the final effluent of both systems. Moreover, sensitivity analysis revealed that the concentrations of the microorganisms in raw wastewater and their removal rates in the secondary treatment stages had the greatest influence on the predictive output. It was therefore concluded that, provided due attention is paid to the quality of the specific input variables of the model, stochastic modelling may represent a valuable tool to support integrated water and sanitation safety planning approaches to human health risk management of wastewater reuse systems, based on the use of QMRA models. The approach may also support better design and operation of wastewater treatment processes so as to maximise pathogen removal in support of Sustainable Development Goal 6 Target 3 of the United Nations.