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基于浊度与颗粒数关系的饮用水中“两虫”去除率预测模型 被引量:7

The prediction model for the removal rate of Cryptosporidium and Giardia in drinking water based on the relationship between turbidity and particle counts
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摘要 隐孢子虫与贾第鞭毛虫(简称"两虫")是2种严重危害饮用水水质安全的病原微生物,由于其检测成本高昂,有必要研究如何通过常规指标对"两虫"去除效率进行有效预测。通过将"两虫"视为颗粒物,提出了一种基于模型选择基准筛选浊度与颗粒数关系模型后的预测"两虫"去除率的方法,收集浊度和颗粒数成组数据,分别应用线性函数、对数函数、二次函数、指数函数拟合建模。结果表明,指数函数预测原水颗粒数及二次函数预测出水颗粒数均表现出较高的稳定性;预测的"两虫"去除率中位数在国际推荐的合理范围内。研究结果为"两虫"的风险控制提供了技术支撑。 Cryptosporidium and Giardia are two pathogenic microorganisms that seriously endanger the safety of drinking water.It is necessary to research how to effectively predict the removal efficiency of pathogens with conventional indicators for the higher testing fees.Based on model selection criteria,one method was proposed for predicting the removal rate of Cryptosporidium and Giardia after selecting the relationship between turbidity and particle if pathogens were treated as particles.Collecting the pairing data of turbidity and particles,the linear function,logarithmic function,quadratic function and exponential function fitting model were constructed,respectively.The results showed that the exponential function and the quadratic function showed higher robustness in predicting the particles removal rate in source and treated water,respectively.The predicted median of Cryptosporidium and Giardia removal rates were within the reasonable range recommended by the international community,which provided technical support for risk control of Cryptosporidium and Giardia.
作者 韩明毅 安伟 桑晨惠 于建伟 Han Mingyi;An Wei;Sang Chenhui;Yu Jianwei(Research Center for Eco-Environmental Sciences,Chinese Academy of Sciences,Beijing100085,China;University of Chinese Academy of Sciences,Beijing100049,China;Sino-Danish College,University of Chinese Academy of Sciences,Beijing100190,China)
出处 《给水排水》 CSCD 北大核心 2019年第5期134-140,共7页 Water & Wastewater Engineering
基金 国家自然科学基金(21477158) 水体污染控制与治理科技重大专项(2018ZX07502001 2017ZX07201005)
关键词 浊度 颗粒数 隐孢子虫 贾第鞭毛虫 模型选择基准 Turbidity Particle counts Cryptosporidium Giardia Model selection criteria
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