A quantitative microbial risk assessment of wastewater treatment plant blending: case study in San Francisco Bay

2016 ◽  
Vol 2 (1) ◽  
pp. 134-145 ◽  
Author(s):  
Edmund Y. Seto ◽  
Jon Konnan ◽  
Adam W. Olivieri ◽  
Richard E. Danielson ◽  
Donald M. D. Gray
Keyword(s):  
Risk Assessment ◽  
San Francisco ◽  
Treatment Plant ◽  
Extreme Rainfall ◽  
Quantitative Microbial Risk Assessment ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Microbial Risk Assessment ◽  
Microbial Risk

Quantitative Microbial Risk Assessment (QMRA) to assess health risk associated with increasing extreme rainfall events and the practice of wastewater blending.

QMRA (quantitative microbial risk assessment) and HACCP (hazard analysis and critical control points) for management of pathogens in wastewater and sewage sludge treatment and reuse

2004 ◽  
Vol 50 (2) ◽  
pp. 23-30 ◽  
Author(s):  
T. Westrell ◽  
C. Schönning ◽  
T.A. Stenström ◽  
N.J. Ashbolt
Keyword(s):  
Risk Assessment ◽  
Hazard Analysis ◽  
Treatment Plant ◽  
Storage Site ◽  
Control Points ◽  
Quantitative Microbial Risk Assessment ◽  
Worst Case ◽  
Microbial Risk Assessment ◽  
Microbial Risk ◽  
Critical Control Points

Hazard Analysis and Critical Control Points (HACCP) was applied for identifying and controlling exposure to pathogenic microorganisms encountered during normal sludge and wastewater handling at a 12,500 m3/d treatment plant utilising tertiary wastewater treatment and mesophilic sludge digestion. The hazardous scenarios considered were human exposure during treatment, handling, soil application and crop consumption, and exposure via water at the wetland-area and recreational swimming. A quantitative microbial risk assessment (QMRA), including rotavirus, adenovirus, haemorrhagic E. coli, Salmonella, Giardia and Cryptosporidium, was performed in order to prioritise pathogen hazards for control purposes. Human exposures were treated as individual risks but also related to the endemic situation in the general population. The highest individual health risk from a single exposure was via aerosols for workers at the belt press for sludge dewatering (virus infection risk = 1). The largest impact on the community would arise if children ingested sludge at the unprotected storage site, although in the worst-case situation the largest number of infections would arise through vegetables fertilised with sludge and eaten raw (not allowed in Sweden). Acceptable risk for various hazardous scenarios, treatment and/or reuse strategies could be tested in the model.

Scenario-based quantitative microbial risk assessment to evaluate the robustness of a drinking water treatment plant

2016 ◽  
Vol 51 (2) ◽  
pp. 81-96 ◽  
Author(s):  
Mohamed A. Hamouda ◽  
William B. Anderson ◽  
Michele I. Van Dyke ◽  
Ian P. Douglas ◽  
Stéphanie D. McFadyen ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Drinking Water ◽  
Water Treatment ◽  
Treatment Plant ◽  
Water Treatment Plant ◽  
Operating Conditions ◽  
Quantitative Microbial Risk Assessment ◽  
Microbial Risk Assessment ◽  
Microbial Risk ◽  
Treatment Processes

While traditional application of quantitative microbial risk assessment (QMRA) models usually stops at analyzing the microbial risk under typical operating conditions, this paper proposes the use of scenario-based risk assessment to predict the impact of potential challenges on the expected risk. This study used a QMRA model developed by Health Canada to compare 14 scenarios created to assess the increase in risk due to potential treatment failures and unexpected variations in water quality and operating parameters of a water treatment plant. Under regular operating conditions, the annual risk of illness was found to be substantially lower than the acceptable limit. Scenario-based QMRA was shown to be useful in demonstrating which hypothetical treatment failures would be the most critical, resulting in an increased risk of illness. The analysis demonstrated that scenarios incorporating considerable failure in treatment processes resulted in risk levels surpassing the acceptable limit. This reiterates the importance of robust treatment processes and the multi-barrier approach voiced in drinking water safety studies. Knowing the probability of failure, and the risk involved, allows designers and operators to make effective plans for response to treatment failures and/or recovery actions involving potential exposures. This ensures the appropriate allocation of financial and human resources.

scholarly journals Improved methods for modelling drinking water treatment in quantitative microbial risk assessment; a case study of Campylobacter reduction by filtration and ozonation

2008 ◽  
Vol 6 (3) ◽  
pp. 301-314 ◽  
Author(s):  
P. W. M. H. Smeets ◽  
Y. J. Dullemont ◽  
P. H. A. J. M. Van Gelder ◽  
J. C. Van Dijk ◽  
G. J. Medema
Keyword(s):  
Risk Assessment ◽  
Drinking Water ◽  
Rare Events ◽  
Monitoring Data ◽  
Average Risk ◽  
Quantitative Microbial Risk Assessment ◽  
Risk Of Infection ◽  
Microbial Risk Assessment ◽  
Microbial Risk

Quantitative microbial risk assessment (QMRA) is increasingly applied to estimate drinking water safety. In QMRA the risk of infection is calculated from pathogen concentrations in drinking water, water consumption and dose response relations. Pathogen concentrations in drinking water are generally low and monitoring provides little information for QMRA. Therefore pathogen concentrations are monitored in the raw water and reduction of pathogens by treatment is modelled stochastically with Monte Carlo simulations. The method was tested in a case study with Campylobacter monitoring data of rapid sand filtration and ozonation processes. This study showed that the currently applied method did not predict the monitoring data used for validation. Consequently the risk of infection was over estimated by one order of magnitude. An improved method for model validation was developed. It combines non-parametric bootstrapping with statistical extrapolation to rare events. Evaluation of the treatment model was improved by presenting monitoring data and modelling results in CCDF graphs, which focus on the occurrence of rare events. Apart from calculating the yearly average risk of infection, the model results were presented in FN curves. This allowed for evaluation of both the distribution of risk and the uncertainty associated with the assessment.

scholarly journals Quantitative microbial risk assessment used to evaluate seasonal wastewater treatment limits: case study in Vacaville, CA

2017 ◽  
Vol 18 (3) ◽  
pp. 910-925 ◽  
Author(s):  
Edmund Seto ◽  
Adam W. Olivieri ◽  
Richard E. Danielson
Keyword(s):  
Risk Assessment ◽  
Wastewater Treatment ◽  
Treatment Plant ◽  
Enteric Viruses ◽  
Secondary Effluent ◽  
Quantitative Microbial Risk Assessment ◽  
Microbial Risk Assessment ◽  
Microbial Risk ◽  
Annual Risk ◽  
Receiving Water

Abstract A quantitative microbial risk assessment (QMRA) was conducted to support renewal of the City of Vacaville wastewater discharge permit and seasonal (summer) filtration requirements. Influent and final disinfected effluent from the city's wastewater treatment plant, as well as 11 receiving water stations, were monitored for indicator organisms (i.e. total and fecal coliforms, Escherichia coli, Enterococcus, male-specific bacteriophage (MS2), and the Bacteroidales) and several pathogens (i.e. Giardia cysts, Cryptosporidium oocysts, infectious Cryptosporidium, and Norovirus GI and GII). QMRA annualized risks of infection for selected pathogens enteric viruses, Giardia and Cryptosporidium. Estimated median annualized risk for recreational exposure in either disinfected secondary and/or filtered disinfected secondary effluent is on the order of 1.1 × 10−3 per person per year (pppy) for enteric viruses and would be roughly one order of magnitude lower if local receiving water dilution of the treatment plant effluent was taken into account. Estimated median annual risk for recreation exposure in disinfected secondary effluent is 1.8 × 10−3 pppy for Cryptosporidium and 1 log10 less with filtration during the summer months. The estimated median annual risk for landscape exposure (e.g. golfing) to secondary disinfected effluent is 7.6 × 10−7 pppy for enteric viruses. Estimated median annualized risk is 1.7 × 10−7 pppy for enteric viruses and 3.0 × 10−5 to 3.6 × 10−6 pppy for parasites for use of secondary disinfected effluent with irrigated agriculture. Estimated annualized risks for recreational exposure to the local receiving waters were approximately 10 to 1,000 times greater than direct recreational exposure to the final filtered and disinfected effluent. All risk estimates associated with exposure to final treated plant effluent (i.e. secondary filtered and disinfected) were close to or lower than the California level of acceptable annual risk of infection of 10−4 pppy for recreational exposure. Risk estimates provide further evidence to support the use of seasonal treatment limits requiring summer filtration for public health protection.

scholarly journals Incorporating parameter uncertainty into Quantitative Microbial Risk Assessment (QMRA)

2011 ◽  
Vol 9 (1) ◽  
pp. 10-26 ◽  
Author(s):  
Margaret Donald ◽  
Kerrie Mengersen ◽  
Simon Toze ◽  
Jatinder P.S. Sidhu ◽  
Angus Cook
Keyword(s):  
Risk Assessment ◽  
Monte Carlo ◽  
Parameter Uncertainty ◽  
Statistical Models ◽  
Risk Assessments ◽  
Quantitative Microbial Risk Assessment ◽  
Microbial Risk Assessment ◽  
Microbial Risk ◽  
Sources Of Uncertainty

Modern statistical models and computational methods can now incorporate uncertainty of the parameters used in Quantitative Microbial Risk Assessments (QMRA). Many QMRAs use Monte Carlo methods, but work from fixed estimates for means, variances and other parameters. We illustrate the ease of estimating all parameters contemporaneously with the risk assessment, incorporating all the parameter uncertainty arising from the experiments from which these parameters are estimated. A Bayesian approach is adopted, using Markov Chain Monte Carlo Gibbs sampling (MCMC) via the freely available software, WinBUGS. The method and its ease of implementation are illustrated by a case study that involves incorporating three disparate datasets into an MCMC framework. The probabilities of infection when the uncertainty associated with parameter estimation is incorporated into a QMRA are shown to be considerably more variable over various dose ranges than the analogous probabilities obtained when constants from the literature are simply ‘plugged’ in as is done in most QMRAs. Neglecting these sources of uncertainty may lead to erroneous decisions for public health and risk management.

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