DOI: 10.1016/j.watres.2019.01.041
Scopus记录号: 2-s2.0-85061346758
论文题名: Data fitting approach more critical than exposure scenarios and treatment of censored data for quantitative microbial risk assessment
作者: Poma H.R. ; Kundu A. ; Wuertz S. ; Rajal V.B.
刊名: Water Research
ISSN: 431354
出版年: 2019
起始页码: 45
结束页码: 53
语种: 英语
英文关键词: Censored data
; Enteric virus
; Quantitative microbial risk assessment
; Recreational water
; Waterborne disease
Scopus关键词: Distribution functions
; Health risks
; Polymerase chain reaction
; Risk perception
; Viruses
; Censored data
; Enteric virus
; Quantitative microbial risk assessment
; Recreational water
; Water-borne disease
; Risk assessment
; water
; concentration (composition)
; data assimilation
; detection method
; disease prevalence
; methodology
; microbial activity
; quantitative analysis
; risk assessment
; river pollution
; scenario analysis
; spatial distribution
; ultrafiltration
; virus
; water treatment
; Argentina
; Article
; controlled study
; data analysis
; Enterovirus
; Enterovirus infection
; health hazard
; infection risk
; infectious dose
; limit of detection
; nonhuman
; Norovirus
; Norovirus genogroup II
; norovirus infection
; polymerase chain reaction
; priority journal
; quantitative analysis
; risk assessment
; statistical analysis
; ultrafiltration
; virus detection
; water borne disease
; water contamination
; water sampling
; Arenales River
; Argentina
; Salta [Argentina]
; Enterovirus
; Norovirus
英文摘要: 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. © 2019 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/121963
Appears in Collections: 气候变化事实与影响
There are no files associated with this item.
作者单位: 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, United States; Singapore Centre for Environmental Life Sciences Engineering (SCELSE), Nanyang Technological University637551, Singapore; School of Civil and Environmental Engineering, Nanyang Technological University639798, Singapore; Facultad de Ingeniería, UNSa, Salta, Argentina
Recommended Citation:
Poma H.R.,Kundu A.,Wuertz S.,et al. Data fitting approach more critical than exposure scenarios and treatment of censored data for quantitative microbial risk assessment[J]. Water Research,2019-01-01