Long-term monitoring through a wastewater-based observatory to model urban population dynamics and health indicators

Wastewater-based epidemiology (WBE) has emerged as a powerful tool for monitoring population health, yet its quantitative reliability remains constrained by uncertainties in population estimation and sampling frequency. This study evaluates whether dynamic de facto population estimates can be derived from hydrochemical wastewater parameters and whether monthly wastewater monitoring can be analytically refined to a daily timescale through population normalization. A Bayesian smoothing and hierarchical outlier-detection framework was developed to denoise concentration and flow measurements of standard hydrochemical parameters. From these smoothed flows, a multidimensional inference model based on five hydrochemical parameters was constructed to estimate connected populations at daily resolution and validated using 2020 population benchmarks from two major Parisian wastewater treatment plants. The model achieved mean absolute percentage errors of approximately 6% across sites with contrasting catchment characteristics, outperforming or matching existing literature models and demonstrating robustness to operational disturbances. Applied to four years of data from the City's wastewater Observatory, the method revealed population variations by a factor of three, substantially improving the interpretation of chemical and virological signals. After normalization, pharmaceutical trends derived from wastewater closely matched prescription data, enabling the detection of non-prescription usage. Conversely, monthly viral monitoring provided only limited correspondence with clinical data, confirming that such low-frequency sampling is insufficient for short-term epidemic tracking. Overall, this work demonstrates that hydrochemical-based population modeling enhances the interpretability, scalability, and operational value of long-term wastewater observatories, providing a practical route to integrate monthly monitoring into finer-scale WBE analyses.