DOI: 10.1016/j.watres.2018.06.057
Scopus记录号: 2-s2.0-85053049693
论文题名: On the implementation of reliable early warning systems at European bathing waters using multivariate Bayesian regression modelling
作者: Seis W. ; Zamzow M. ; Caradot N. ; Rouault P.
刊名: Water Research
ISSN: 431354
出版年: 2018
卷: 143 起始页码: 301
结束页码: 312
语种: 英语
英文关键词: Bathing water directive
; Bathing waters
; Bayesian regression modelling
; Early waring
Scopus关键词: Alarm systems
; Classification (of information)
; Laws and legislation
; River pollution
; Bathing water
; Bathing water directives
; Bayesian regression
; Early waring
; Fecal indicator bacteria
; Methodological aspects
; Multivariate Bayesian regressions
; Multivariate regression
; Regression analysis
; bacterium
; bathing water
; Bayesian analysis
; early warning system
; methodology
; model validation
; modeling
; Berlin
; Germany
英文摘要: For ensuring microbial safety, the current European bathing water directive (BWD) (76/160/EEC 2006) demands the implementation of reliable early warning systems for bathing waters, which are known to be subject to short-term pollution. However, the BWD does not provide clearly defined threshold levels above which an early warning system should start warning or informing the population. Statistical regression modelling is a commonly used method for predicting concentrations of fecal indicator bacteria. The present study proposes a methodology for implementing early warning systems based on multivariate regression modelling, which takes into account the probabilistic character of European bathing water legislation for both alert levels and model validation criteria. Our study derives the methodology, demonstrates its implementation based on information and data collected at a river bathing site in Berlin, Germany, and evaluates health impacts as well as methodological aspects in comparison to the current way of long-term classification as outlined in the BWD. © 2018 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/112575
Appears in Collections: 气候减缓与适应
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作者单位: Kompetenzzentrum Wasser Berlin gGmbH, Cicerostraße 24, Berlin, 10709, Germany; Delft University of Technology, Netherlands
Recommended Citation:
Seis W.,Zamzow M.,Caradot N.,et al. On the implementation of reliable early warning systems at European bathing waters using multivariate Bayesian regression modelling[J]. Water Research,2018-01-01,143