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Abstract：
Chlorine is a widely used water disinfectant in humanitarian emergency water supply. However, its effective application can be limited by the uncertainty in initial dose determination. The target free chlorine residual in water should achieve both health objectives and aesthetic considerations, but the varying field conditions and changing source water quality may affect the performance of chlorination strategies. A chlorine dose predictive tool could assist in initial dose determination. To this end, an accurate chlorine decay kinetic model can serve as a strong foundation for developing such a tool. In this study, a literature search identified 7 basic chlorine decay kinetic models that were subsequently tested with 610 different chlorine decay test data (from a semi-systematic literature search and laboratory-generated results). The models were then ranked based on their goodness of fit (R2) and root mean square error. An empirical model, power models and parallel models were found able to fit most decay data with more than half of the regressions resulting in R2 value over 0.97. First order models can achieve R2 value above 0.95 when the data points in the rapid phase are excluded from the model fitting. The power models and parallel models can form a strong basis for developing a chlorine dose predictive tool if the power term and the ratio term (model parameters) can be controlled. An essential next step is to evaluate the relationships between easily obtainable water parameters in the field and the decay term in the models to allow rapid model calibration.
摘要：
氯是人道主义应急供水中广泛使用的水消毒剂。然而,初始剂量测定的不确定性会限制其有效应用。目标水中的游离氯残留量应同时达到健康目标和美学考虑，但是不同的现场条件和不断变化的水源水质可能会影响氯化策略的性能。氯剂量预测工具可以帮助初始剂量确定。为此,准确的氯衰变动力学模型可以作为开发这种工具的坚实基础。在这项研究中,文献检索确定了7个碱性氯衰变动力学模型,随后用610个不同的氯衰变试验数据(来自半系统文献检索和实验室生成的结果)进行了试验.然后基于模型的拟合优度(R2)和均方根误差对模型进行排序。一个经验模型，发现功率模型和并行模型能够拟合大多数衰减数据，其中一半以上的回归导致R2值超过0.97。当从模型拟合中排除快速阶段中的数据点时,一阶模型可以实现大于0.95的R2值。如果可以控制功率项和比率项(模型参数),则功率模型和并行模型可以形成用于开发氯剂量预测工具的强基础。重要的下一步是评估现场容易获得的水参数与模型中的衰减项之间的关系，以允许快速的模型校准。
