Establishing correlations between time series of wastewater parameters under extreme and regular weather conditions

This study investigates the correlations between key wastewater parameters — water level, turbidity, and electrical conductivity — under varying weather conditions, including extreme rainfall events such as the May 2023 flood event in Bologna, Italy. Data collected via IoT-based sensors are analyzed using Detrended Cross-Correlation Analysis (DCCA) and Autoregressive Distributed Lag (ARDL) models. The results highlight significant correlations between water level and other parameters, with distinct patterns emerging during extreme and regular weather periods. Notably, water level correlates negatively with electrical conductivity, particularly during flood events, due to the dilution effect of rainwater. Turbidity shows a complex relationship with water level, influenced by weather conditions and the opposing effects of different factors. ARDL models further demonstrate the potential to predict turbidity and electrical conductivity from water level data, offering valuable insights for wastewater management in urban areas. • We investigate correlations between key wastewater and hydrological parameters. • We applied data-driven methods to time series collected by IoT sensors. • The analysis is performed under regular and extreme weather conditions.