Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2019WR025736 |
Understanding and Modeling the Occurrence of E. coli Blooms in Drinking Water Reservoirs | |
Bertone, Edoardo1,2; Kozak, Sonya3; Roiko, Anne1,3 | |
2019-12-10 | |
发表期刊 | WATER RESOURCES RESEARCH
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ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2019 |
卷号 | 55期号:12页码:10518-10526 |
文章类型 | Article |
语种 | 英语 |
国家 | Australia |
英文摘要 | Certain strains of Escherichia coli have been reported to bloom in the environment, resulting in high concentrations in waters in the absence of apparent fecal input or human pathogens, and in turn, undermining its reliability as an indicator of recent fecal contamination. Given the capacity of environmental strains of E. coli to replicate in the environment, the objective of this research work was to investigate whether any of the routinely measured parameters could predict the onset of an E. coli bloom in drinking water reservoirs. Information from historical catchment, weather, and water quality data were extracted for a number of Australian reservoirs that experienced E. coli blooms. Data were preprocessed and analyzed with time series analysis, linear and nonlinear regression, and self-organizing maps. Findings suggest that warm water, dry catchments, algal blooms, and nutrient availability were important factors in increasing the propensity for a bloom. Nutrient availability can be affected by many extrinsic factors that are often not well characterized, such as bushfires and back burning, decomposition of aquatic species, and dust storms. Based on data analysis outputs, a data-driven Bayesian Network model was developed, which, considering the paucity of data for some key input parameters, should only be used to trigger more intensive monitoring programs whenever the predicted risk of a bloom exceeds predetermined key thresholds. Such new data could be fed into the model to continuously improve its accuracy, and to eventually predict and proactively manage future blooms. |
英文关键词 | Bayesian Network Escherichia coli risk assessment water resources management |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000501682200001 |
WOS关键词 | NATURALIZED ESCHERICHIA-COLI ; BAYESIAN NETWORKS ; SURVIVAL ; ENTEROCOCCI ; TEMPERATE ; QUALITY ; PERSISTENCE ; ECOLOGY ; HEALTH ; GROWTH |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/223970 |
专题 | 资源环境科学 |
作者单位 | 1.Griffith Univ, Cities Res Inst, Gold Coast, Qld, Australia; 2.Griffith Univ, Sch Engn & Built Environm, Gold Coast, Qld, Australia; 3.Griffith Univ, Menzies Hlth Inst, Gold Coast, Qld, Australia |
推荐引用方式 GB/T 7714 | Bertone, Edoardo,Kozak, Sonya,Roiko, Anne. Understanding and Modeling the Occurrence of E. coli Blooms in Drinking Water Reservoirs[J]. WATER RESOURCES RESEARCH,2019,55(12):10518-10526. |
APA | Bertone, Edoardo,Kozak, Sonya,&Roiko, Anne.(2019).Understanding and Modeling the Occurrence of E. coli Blooms in Drinking Water Reservoirs.WATER RESOURCES RESEARCH,55(12),10518-10526. |
MLA | Bertone, Edoardo,et al."Understanding and Modeling the Occurrence of E. coli Blooms in Drinking Water Reservoirs".WATER RESOURCES RESEARCH 55.12(2019):10518-10526. |
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