Hierarchical Bayesian modeling for predictive environmental microbiology toward a safe use of human excreta: Systematic review and meta-analysis
Hierarchical Bayesian modeling for predictive environmental microbiology toward a safe use of human excreta: Systematic review and meta-analysis
复制标题
用于预测环境微生物学的分层贝叶斯模型，以安全使用人类排泄物：系统评价和荟萃分析
DOI：
10.1016/j.jenvman.2021.112088
复制
发表时间：
2021
影响因子：
8.7
通讯作者：
Sano Daisuke
中科院分区：
文献类型：
--
作者：
Oishi Wakana;Kadoya Syun-suke;Nishimura Osamu;B. Rose Joan;Sano Daisuke
人类排泄物中的病原体浓度需要适当管理，但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不同环境条件的微生物灭活动力学模型，并确定在消毒过程中可以监测的适当指标。我们针对先前的研究进行了系统回顾，这些研究提出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，包括处理时间，pH值，温度，氨浓度和水分含量，动力学模型参数使用分层贝叶斯建模进行统计学估计。建立了大肠杆菌、沙门氏菌、肠球菌、蛔虫、噬菌体MS2、肠杆菌噬菌体phiX174和腺病毒的灭活动力学模型。利用所建立的模型对微生物的灭活率进行了预测，结果表明，蛔虫卵是最耐受的微生物，其次是噬菌体MS2和肠球菌。氨浓度、温度和含水量是蛔虫灭活的关键因素。我们的模型预测与当前世卫组织的指导方针一致。所开发的灭活动力学模型使我们能够预测在不同的环境条件下，这是必不可少的微生物风险管理，在新兴的资源回收实践从人类排泄物中的微生物排泄物基质中的微生物浓度。
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 forEscherichia coli,Salmonella,Enterococcus,Ascariseggs, bacteriophage MS2, enterobacteria phage phiX174 and adenovirus. The inactivation rates of a microorganism were predicted using the established model.Ascariseggs were identified as the most tolerant microorganisms, followed by bacteriophage MS2 andEnterococcus. Ammonia concentration, temperature and moisture content were the critical factors for theAscarisinactivation. 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.