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Abstract：
The pathogen concentration in human excreta needs to be managed appropriately, but a predictive approach has yet to be implemented due to a lack of kinetics models for pathogen inactivation that are available under varied environmental conditions. Our goals were to develop inactivation kinetics models of microorganisms applicable under varied environmental conditions of excreta matrices and to identify the appropriate indicators that can be monitored during disinfection processes. We conducted a systematic review targeting previous studies that presented time-course decay of a microorganism and environmental conditions of matrices. Defined as a function of measurable factors including treatment time, pH, temperature, ammonia concentration and moisture content, the kinetic model parameters were statistically estimated using hierarchical Bayesian modeling. The inactivation kinetics models were constructed for Escherichia coli, Salmonella, Enterococcus, Ascaris eggs, bacteriophage MS2, enterobacteria phage phiX174 and adenovirus. The inactivation rates of a microorganism were predicted using the established model. Ascaris eggs were identified as the most tolerant microorganisms, followed by bacteriophage MS2 and Enterococcus. Ammonia concentration, temperature and moisture content were the critical factors for the Ascaris inactivation. Our model predictions coincided with the current WHO guidelines. The developed inactivation kinetics models enable us to predict microbial concentration in excreta matrices under varied environmental conditions, which is essential for microbiological risk management in emerging resource recovery practices from human excreta.
摘要：
人体排泄物中的病原体浓度需要适当管理，但是，由于缺乏在不同环境条件下可用的病原体灭活动力学模型，因此尚未实施预测方法。我们的目标是开发适用于排泄物基质的各种环境条件下的微生物灭活动力学模型，并确定可以在消毒过程中监测的适当指标。我们针对先前的研究进行了系统综述，这些研究介绍了微生物的时程衰减和基质的环境条件。定义为可测量因素的函数，包括治疗时间，pH值,温度,氨浓度和水分含量，动力学模型参数采用分层贝叶斯建模进行统计估计。构建了大肠杆菌失活动力学模型,沙门氏菌,肠球菌,蛔虫卵,噬菌体MS2、肠杆菌噬菌体phiX174和腺病毒。使用建立的模型预测微生物的失活率。蛔虫卵被确定为最耐受的微生物，其次是噬菌体MS2和肠球菌。氨浓度,温度和水分含量是蛔虫灭活的关键因素。我们的模型预测符合当前的WHO指南。开发的失活动力学模型使我们能够预测不同环境条件下排泄物基质中的微生物浓度，这对于从人类排泄物中新兴的资源回收实践中的微生物风险管理至关重要。
