Towards a Predictive Model for Initial Chlorine Dose in Humanitarian Emergencies

• Main Authors: Hongjian Wu, Caetano C. Dorea
• Format: Article
• Language: English
• Published: MDPI AG 2020-05-01
• Series: Water
• Subjects: chlorine decay; natural organic matter; temperature; humanitarian emergency; WASH; bucket chlorination
• Online Access: https://www.mdpi.com/2073-4441/12/5/1506

Free chlorination is a widely employed disinfection method in humanitarian water provision due to its many advantages. However, its effective application is hindered by the challenge in determining adequate initial doses to achieve free chlorine residuals that satisfy both health and aesthetic requirements. Current guidelines show varying recommended dosing strategies, and many do not adequately consider chlorine decay mechanisms that occur during water storage. Even though turbidity is commonly used as a criterion for deciding chlorine dose, it may not be an adequate proxy for the water quality in many cases. This paper addresses the fundamental relationships between chlorine decay kinetics and selected key water parameters (i.e., natural organic matter, water temperature, chlorine demand) by conducting chlorine decay tests in controlled conditions and in jerrycans (i.e., simulating humanitarian water treatment conditions). Chlorine decay constant from the Feben and Taras’s empirical model and first order model formed linear and exponential relationships with two water parameters (UVA₂₅₄ and 30-min chlorine demand). With these relationships, the two chlorine decay models can be calibrated quickly and frequently in the field, allowing effective determination of initial chlorine dose. These two models calibrated based on the suggested water parameters from the study could predict chlorine decay in water having a main chlorine demand-inducing constituents as natural organic matter. However, they underpredicted chlorine decay in surface water with additional chlorine reactants. Further research on additional chlorine decay mechanisms is needed to expand the applicability of the models.