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首页> 外文期刊>Environmental Science & Technology >Enterococci predictions from partial least squares regression models in conjunction with a single-sample standard improve the efficacy of beach management advisories
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Enterococci predictions from partial least squares regression models in conjunction with a single-sample standard improve the efficacy of beach management advisories

机译:偏最小二乘回归模型与单样本标准相结合的肠球菌预测提高了海滩管理咨询的效率

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摘要

Beach health advisories are issued if enterococci (ENT) densities exceed the 30-d geometric mean or singlesample water quality criteria. Current ENT enumeration procedures require 1 day of incubation; therefore, beach managers make policy decisions using 1-day-old data. This is tantamount to using a model that assumes ENT density on day t is equal to ENT density on day t - 1. Research has shown that ENT densities vary over time scales shorterthan a day, calling into question the usefulness of the current model for decision-making. We created Dynamic Partial Least Square Regression (DPLSR) models for ENT at water quality monitoring stations within two adjacent marine recreational sites, Huntington State Beach (HSB) and Huntington City (HCB) Beach, California, using publicly available environmental data and tested whether these models overcome the drawbacks of the current model. The DPLSR models provide a better prediction of ENT than the current models based on comparisons of root mean-square errors of prediction and the numbers of type I and 2 errors. We compared outcomes in terms of predicted illness, swimmers deterred from entering the water, and net benefits to swimmers for hypothetical management scenarios where beach advisories were issued based on (a) the previously collected sample's ENT density in conjunction with the two water quality criteria, and (b) predictions from DPLSR models in conjunction with the singlesample standard. At both HSB and HCB the DPLSR scenario produced a more favorable balance between illness prevention and recreational access. The results call into question the current method of beach management and show that model-informed decision-making and elimination of the geometric mean standard will aid beach managers in achieving more favorable outcomes in terms of illness and access than are presently achieved using 1-day-old measurements, especially at beaches where water quality problems are chronic.
机译:如果肠球菌(ENT)密度超过30天几何平均值或单样本水质标准,则会发布海滩健康建议。当前的ENT枚举程序需要孵育1天;因此,海滩管理员使用1天前的数据制定政策决策。这相当于使用一个模型,假设第t天的ENT密度等于第t-1天的ENT密度。研究表明,ENT密度随时间的变化比一天的时间短,这质疑了当前模型用于决策的有效性。 -制造。我们使用公开的环境数据,在加利福尼亚州亨廷顿州立海滩(HSB)和亨廷顿市(HCB)海滩两个相邻的海洋娱乐场所的水质监测站为耳鼻喉创建了动态偏最小二乘回归(DPLSR)模型,并使用公开的环境数据进行了测试,并测试了这些模型克服了当前模型的缺点。基于预测的均方根误差与I型和2型误差的数目的比较,DPLSR模型提供了比当前模型更好的ENT预测。我们根据预测的疾病,阻止游泳者入水的游泳者和游泳者在假设管理方案下的净收益进行了比较,在这些假设性管理方案中,根据以下条件发布了海滩咨询意见:(a)先前收集的样本的耳鼻喉密度结合两个水质标准, (b)与单样本标准一起从DPLSR模型得出的预测。在HSB和HCB上,DPLSR方案在疾病预防和娱乐场所获得了更有利的平衡。结果令人质疑当前的海滩​​管理方法,并表明,基于模型的决策和消除几何均数标准将比目前使用1天获得的帮助,使海滩管理人员在疾病和可及性方面获得更有利的结果过时的测量,尤其是在水质问题长期存在的海滩上。

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