A Software System for Predicting Trihalomethanes Species in Water Distribution Networks Using Online Networked Water Sensors

Abstract

Drinking water chlorination reduces the risk of pathogenic infection, but it may be harmful to human health because of trihalomethanes formation. At present, trihalomethanes concentrations are periodically monitored in fixed points along the water distribution network by in situ sampling and laboratory tests. Simulation models combined with online data sources can be useful for reproducing trihalomethanes concentrations in all part of the network and over time. The current challenge is to make reliable the model predictions. At this scope, a novel method and software system has been developed able to reproducing reliable trihalomethanes concentrations, including their species, in all parts of the water network and over time. The system has been tested on the real aqueduct Santa Sofia, disinfected by sodium hypochlorite and monitored by seven networked online multi-parameter sensors.