Instituto de Investigaciones para la Industria Química (INIQUI), CONICET, Universidad Nacional de Salta (UNSa), Av. Bolivia 5150, Salta, 4400, Argentina.
Department of Civil and Environmental Engineering, University of California, Davis, 95616, USA.
Water Res. 2019 May 1;154:45-53. doi: 10.1016/j.watres.2019.01.041. Epub 2019 Feb 4.
Recreational waters are a source of many diseases caused by human viral pathogens, including norovirus genogroup II (NoV GII) and enterovirus (EV). Water samples from the Arenales river in Salta, Argentina, were concentrated by ultrafiltration and analyzed for the concentrations of NoV GII and EV by quantitative PCR. Out of 65 samples, 61 and 59 were non-detects (below the Sample Limit of Detection limit, SLOD) for EV and NoV GII, respectively. We hypothesized that a finite number of environmental samples would lead to different conclusions regarding human health risks based on how data were treated and fitted to existing distribution functions. A quantitative microbial risk assessment (QMRA) was performed and the risk of infection was calculated using: (a) two methodological approaches to find the distributions that best fit the data sets (methods H and R), (b) four different exposure scenarios (primary contact for children and adults and secondary contact by spray inhalation/ingestion and hand-to-mouth contact), and (c) five alternatives for treating censored data. The risk of infection for NoV GII was much higher (and exceeded in most cases the acceptable value established by the USEPA) than for EV (in almost all the scenarios within the recommended limit), mainly due to the low infectious dose of NoV. The type of methodology used to fit the monitoring data was critical for these datasets with numerous non-detects, leading to very different estimates of risk. Method R resulted in higher projected risks than Method H. Regarding the alternatives for treating censored data, replacing non-detects by a unique value like the average or median SLOD to simplify the calculations led to the loss of information about the particular characteristics of each sample. In addition, the average SLOD was highly impacted by extreme values (due to events such as precipitations or point source contamination). Instead, using the SLOD or half- SLOD captured the uniqueness of each sample since they account for the history of the sample including the concentration procedure and the detection method used. Finally, substitution of non-detects by Zero is not realistic since a negative result would be associated with a SLOD that can change by developing more efficient and sensitive methodology; hence this approach would lead to an underestimation of the health risk. Our findings suggest that in most cases the use of the half-SLOD approach is appropriate for QMRA modeling.
娱乐用水是许多由人类病毒病原体引起的疾病的源头，包括诺如病毒基因群 II（NoV GII）和肠病毒（EV）。从阿根廷萨尔塔的阿雷纳雷斯河采集水样，通过超滤浓缩，并通过定量 PCR 分析 NoV GII 和 EV 的浓度。在 65 个样本中，EV 和 NoV GII 的检出率分别为 61 个和 59 个（低于检测限，SLOD）。我们假设，由于数据处理和拟合现有分布函数的方式不同，有限数量的环境样本将导致针对人类健康风险的不同结论。进行了定量微生物风险评估（QMRA），并使用以下方法计算感染风险：（a）两种方法来找到最适合数据集的分布（方法 H 和 R），（b）四种不同的暴露场景（儿童和成人的主要接触，以及通过喷雾吸入/摄入和手到口接触的次要接触），以及（c）处理删失数据的五种替代方法。NoV GII 的感染风险（在大多数情况下超过了美国环保署规定的可接受值）远高于 EV（在几乎所有推荐范围内的场景中），主要是因为 NoV 的感染剂量较低。用于拟合监测数据的方法对于具有大量未检出值的这些数据集至关重要，导致风险的估计值非常不同。方法 R 导致的预测风险高于方法 H。关于处理删失数据的替代方法，用平均或中位数 SLOD 等唯一值代替未检出值来简化计算，会导致丢失每个样本的特定特征的信息。此外，平均 SLOD 受到极端值的影响很大（由于降水或点源污染等事件）。相反，使用 SLOD 或 SLOD 的一半可以捕获每个样本的独特性，因为它们考虑了包括浓缩过程和使用的检测方法在内的样本历史。最后，用零替换未检出值是不现实的，因为阴性结果将与 SLOD 相关联，而 SLOD 可能会随着更有效和敏感的方法的发展而发生变化；因此，这种方法将导致对健康风险的低估。我们的研究结果表明，在大多数情况下，半 SLOD 方法适用于 QMRA 建模。
