Comparative predictive modelling of the occurrence of faecal indicator bacteria in a drinking water source in Norway

Hadi, Muhamad Sukri; Hameed, Ibrahim A.; Seidu, Razak

doi:10.1016/j.scitotenv.2018.02.140

Cited by 27 publications

(8 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Knowledge of the variability of indicator concentrations at fine time scales of minutes, hours, and days is needed to evaluate the uncertainty of the results from a one‐off grab sample intended to represent the daily or weekly values. Such variability was studied by Muirhead and Meenken (2018) for baseflow conditions in three New Zealand rivers in summer and winter seasons. The variability of E. coli concentrations at the above three time scales increased with time scale and exceeded the laboratory replication variability at all scales.…”

Section: Spatiotemporal Variability Of Microbial Water Qualitymentioning

confidence: 99%

“…The dependence of the variability and uncertainty in microbial concentrations on the temporal scale of data collection (e.g., Muirhead and Meenken, 2018) creates a conundrum for evaluating the performance of environmental microbial fate and transport simulations. In such simulations, both model calibration and validation commonly do not account for the uncertainty of measured values at the scale of simulated values.…”

Section: Watershed‐scale Modelingmentioning

confidence: 99%

“…Microbial water quality databases may combine data collected in conditions with different dominant controls. In such cases, different predictors will be influential in different parts of the database, and classification and regression tree-based data analysis methods may be of interest (Mohammed et al, 2018). Also, an improvement in the interpretation of the observed concentration variations can be expected if the spatiotemporal nature of microbial water quality is explicitly taken into account, such as with the use of empirical orthogonal functions (Hong et al, 2018a).…”

Section: Predictors Of Spatiotemporal Variationsmentioning

confidence: 99%

See 2 more Smart Citations

Microbial Water Quality: Monitoring and Modeling

et al. 2018

View full text Add to dashboard Cite

Microbial water quality lies in the nexus of human, animal, and environmental health. Multidisciplinary efforts are under way to understand how microbial water quality can be monitored, predicted, and managed. This special collection of papers in the Journal of Environmental Quality was inspired by the idea of creating a special section containing the panoramic view of advances and challenges in the arena of microbial water quality research. It addresses various facets of health‐related microorganism release, transport, and survival in the environment. The papers analyze the spatiotemporal variability of microbial water quality, selection of predictors of the spatiotemporal variations, the role of bottom sediments and biofilms, correlations between concentrations of indicator and pathogenic organisms and the role for risk assessment techniques, use of molecular markers, subsurface microbial transport as related to microbial water quality, antibiotic resistance, real‐time monitoring and nowcasting, watershed scale modeling, and monitoring design. Both authors and editors represent international experience in the field. The findings underscore the challenges of observing and understanding microbial water quality; they also suggest promising research directions for improving the knowledge base needed to protect and improve our water sources. Core Ideas Environmental factors are known to control microbial water quality in natural systems. This collection of papers presents a panoramic view of microbial water quality research. With this special section, the editors hope to stimulate interdisciplinary efforts in the field.

show abstract

Section: Spatiotemporal Variability Of Microbial Water Qualitymentioning

confidence: 99%

Section: Watershed‐scale Modelingmentioning

confidence: 99%

Section: Predictors Of Spatiotemporal Variationsmentioning

confidence: 99%

See 1 more Smart Citation

Microbial Water Quality: Monitoring and Modeling

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Bacteria concentrations may be used for mathematical modeling with the view of predicting bacteriological contamination of natural water [19]. However, modeling cannot anticipate low concentrations of fecal contamination whereby water quality monitoring remains a priority in assessing intestinal infection health risks and waterborne disease risks [36].…”

Section: Research Articlementioning

confidence: 99%

Monitoring of drinking water from the karst springs of the Ljuberadja-Niš water supply system (Serbia)

Stevanović¹,

Krstić

Stojanović

et al. 2020

SN Appl. Sci.

View full text Add to dashboard Cite

The Ljuberadja-Niš water supply system is one of the main components of the Niš water supply system. This system consists of a series of natural, karst springs of groundwater (Krupac, Mokra, Divljana, Ljuberadja), with the capacity of 800-1450 L/s. The most important factors in a healthy drinking water supply are the proper choice and protection of water springs. Considering that water composition may sometimes have an adverse effect on consumer health, production processes in some industries and also on facilities and water company equipment, the aim of this paper was to examine water composition and to monitor its quality by analyzing the physical, chemical, and microbiological indicators. Water quality is tested in accordance with the corresponding directives and regulations of national and international authorities. The analysis of drinking water encompassed the appointment of the following parameters (quality indicators): physical parameters: temperature, turbidity, pH value and electrical conductivity of water; chemical parameters: chloride, ammonia, nitrite, nitrate, sulfate, calcium, magnesium, iron, manganese, aluminum and chromium in the form of Cr 6+ and Cr 3+ ions; microbiological parameters: culturable organisms, total and fecal coliform bacteria, intestinal enterococci, sulfite reducing clostridia, Pseudomonas aeruginosa and Proteus strain. Monitoring has confirmed the hygienic and health adequacy of the water distributed to cosumers.

show abstract

“…The model is designed to prevent overfitting by calculating prediction errors from "out-of-bag samples" (one-third of training data) for each tree, thus obviating the need for additional testing of the model accuracy. Further description of the random forest machine learning and its appropriateness for use in predicting E. coli in raw water can be found in the work of Mohammed et al (2018). In this study, weekly measurements of turbidity and color in the water sources from 2009 -2015 were used to train and test the RF models for predicting E. coli in the water sources.…”

Section: Prediction Of Future E Coli With Random Forest Modelsmentioning

confidence: 99%

Climate-driven QMRA model for selected water supply systems in Norway accounting for raw water sources and treatment processes

Hadi

Seidu

2019

Science of The Total Environment

Self Cite

View full text Add to dashboard Cite

Formulating effective management intervention measures for water supply systems requires investigation of potential long-term impacts. This study applies an integrated multiple regression, random forest regression, and quantitative microbial risk assessment (QMRA) modelling approach to assess the effect of climate-driven precipitation on pathogen infection risks in three drinking water treatment plants (WTPs) in Norway. Pathogen removal efficacies of treatment steps were calculated using process models. The results indicate that while the WTPs investigated generally meet the current water safety guidelines, risks of Norovirus and Cryptosporidium infection may be of concern in the future. The pathogen infections attributable to current projections of average precipitation in the study locations may be low. However, the pathogen increases in the drinking water sources due to the occurrence of extreme precipitation events in the catchments could substantially increase the risks of pathogen infections. In addition, without optimal operation of the UV disinfection steps in the WTPs, both the present and potential future infection risks could be high. Therefore, the QMRA models demonstrated the need for improved optimization of key treatment steps in the WTPs, as well as implementation of stringent regulations in protecting raw water sources in the country. The variety of models applied and the pathogen: E. coli used in the study introduce some uncertainties in the results, thus, management decisions that will be based on the results should consider these limitations.Nevertheless, the integration of predictive models with QMRA as applied in this study could be a useful method for climate impact assessment in the water supply industry.

show abstract

Comparative predictive modelling of the occurrence of faecal indicator bacteria in a drinking water source in Norway

Cited by 27 publications

References 46 publications

Microbial Water Quality: Monitoring and Modeling

Microbial Water Quality: Monitoring and Modeling

Monitoring of drinking water from the karst springs of the Ljuberadja-Niš water supply system (Serbia)

Climate-driven QMRA model for selected water supply systems in Norway accounting for raw water sources and treatment processes

Contact Info

Product

Resources

About