Evaluating Population Normalization Methods Using Chemical Data for Wastewater-Based Epidemiology: Insights from a Site-Specific Case Study

Abstract

Wastewater-based epidemiology (WBE) has been widely employed to track the spread of human pathogens; however, correlating wastewater data with clinical surveillance remains challenging due to population variability and environmental factors affecting wastewater composition. This study evaluated different SARS-CoV-2 normalization methods, comparing static population estimates with dynamic normalization based on common physicochemical parameters: chemical oxygen demand (COD), biochemical oxygen demand (BOD5), and ammonia (NH4-N). Wastewater samples were collected from four urban wastewater treatment plants (WWTPs) in northwestern Tuscany (Italy) from February 2021 to March 2023. The correlations between normalized viral loads and clinical COVID-19 cases were highest for static normalization (ρ = 0.405), followed closely by dynamic normalization using COD and BOD5 (ρ = 0.378 each). Normalization based on NH4-N was less effective. These findings suggest that chemical parameters, particularly COD and BOD5, offer a valid alternative for viral normalization when population estimates or flow rate measurements are unavailable. These parameters provide a cost-effective and practical approach for improving WBE reliability, particularly in resource-limited settings. Our results reinforce the importance of normalization in WBE to enhance its representativeness and applicability for public health surveillance.