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Urban areas are built environments containing substantial amounts of impervious surfaces (e.g., streets, sidewalks, roof tops). These areas often include elaborately engineered drainage networks designed to collect, transport, and discharge untreated stormwater into local surface waters. When left uncontrolled, these discharges may contain unsafe levels of fecal waste from sources such as sanitary sewage and wildlife even under dry weather conditions. This study evaluates paired measurements of host-associated genetic markers (log 10 copies per reaction) indicative of human (HF183/BacR287 and HumM2), ruminant (Rum2Bac), canine (DG3), and avian (GFD) fecal sources, 12-hour cumulative precipitation (mm), four catchment land use metrics determined by global information system (GIS) mapping, and Escherichia coli (MPN/100 ml) from seven municipal separate storm sewer system outfall locations situated at the southern portion of the Anacostia River Watershed (District of Columbia, U.S.A.). A total of 231 discharge samples were collected twice per month (n = 24 sampling days) and after rain events (n = 9) over a 13-month period. Approximately 50 % of samples (n = 116) were impaired, exceeding the local E. coli single sample maximum of 2.613 log 10 MPN/100 ml. Genetic quality controls indicated the absence of amplification inhibition in 97.8 % of samples, however 14.7 % (n = 34) samples showed bias in DNA recovery. Of eligible samples, quantifiable levels were observed for avian (84.1 %), human (57.4 % for HF183/BacR287 and 40 % for HumM2), canine (46.7 %), and ruminant (15.9 %) host-associated genetic markers. Potential links between paired measurements are explored with a recently developed Bayesian qPCR censored data analysis approach. Findings indicate that human, pet, and urban wildlife all contribute to storm outfall discharge water quality in the District of Columbia, but pollutant source contributions vary based on 'wet' and 'dry' conditions and catchment land use, demonstrating that genetic-based fecal source identification methods combined with GIS land use mapping can complement routine E. coli monitoring to improve stormwater management in urban areas., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Numerous qPCR-based methods are available to estimate the concentration of fecal pollution sources in surface waters. However, qPCR fecal source identification data sets often include a high proportion of non-detections (reactions failing to attain a prespecified minimal signal intensity for detection) and measurements below the assay lower limit of quantification (minimal signal intensity required to estimate target concentration), making it challenging to interpret results in a quantitative manner while accounting for error. In response, a Bayesian statistic based Fecal Score (FS) approach was developed that estimates the weighted average concentration of a fecal source identification genetic marker across a defined group of samples, mathematically incorporating qPCR measurements from all samples. Yet, implementation is technically demanding and computationally intensive requiring specialized training, the use of expert software, and access to high performance computing. To address these limitations, this study reports a novel approximation model for FS determination based on a frequentist approach. The performance of the Bayesian and Frequentist models are compared using fecal source identification qPCR data representative of different 'censored' data scenarios from a recently published study focusing on the impact of stormwater discharge in urban streams. In addition, data set eligibility recommendations for the responsible use of these models are presented. Findings indicate that the Frequentist model can generate similar average concentrations and uncertainty estimates for FS, compared to the original Bayesian approach. The Frequentist model should make calculations less computationally and technically intensive, allowing for the development of easier to use data analysis tools for fecal source identification applications., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Surface water quality quantitative polymerase chain reaction (qPCR) technologies are expanding from a subject of research to routine environmental and public health laboratory testing. Readily available, reliable reference material is needed to interpret qPCR measurements, particularly across laboratories. Standard Reference Material® 2917 (NIST SRM® 2917) is a DNA plasmid construct that functions with multiple water quality qPCR assays allowing for estimation of total fecal pollution and identification of key fecal sources. This study investigates SRM 2917 interlaboratory performance based on repeated measures of 12 qPCR assays by 14 laboratories (n = 1008 instrument runs). Using a Bayesian approach, single-instrument run data are combined to generate assay-specific global calibration models allowing for characterization of within- and between-lab variability. Comparable data sets generated by two additional laboratories are used to assess new SRM 2917 data acceptance metrics. SRM 2917 allows for reproducible single-instrument run calibration models across laboratories, regardless of qPCR assay. In addition, global models offer multiple data acceptance metric options that future users can employ to minimize variability, improve comparability of data across laboratories, and increase confidence in qPCR measurements., Competing Interests: Declaration of Competing Interest None., (Copyright © 2022. Published by Elsevier Ltd.)

Fecal pollution remains a significant challenge for recreational water quality management worldwide. In response, there is a growing interest in the use of real-time quantitative PCR (qPCR) methods to achieve same-day notification of recreational water quality and associated public health risk as well as to characterize fecal pollution sources for targeted mitigation. However, successful widespread implementation of these technologies requires the development of and access to a high-quality standard control material. Here, we report a single laboratory qPCR performance assessment of the National Institute of Standards and Technology Standard Reference Material 2917 (NIST SRM® 2917), a linearized plasmid DNA construct that functions with 13 recreational water quality qPCR assays. Performance experiments indicate the generation of standard curves with amplification efficiencies ranging from 0.95 ± 0.006 to 0.99 ± 0.008 and coefficient of determination values (R 2 ) ≥ 0.980. Regardless of qPCR assay, variability in repeated measurements at each dilution level were very low (quantification threshold standard deviations ≤ 0.657) and exhibited a heteroscedastic trend characteristic of qPCR standard curves. The influence of a yeast carrier tRNA added to the standard control material buffer was also investigated. Findings demonstrated that NIST SRM® 2917 functions with all qPCR methods and suggests that the future use of this control material by scientists and water quality managers should help reduce variability in concentration estimates and make results more consistent between laboratories., (Published by Elsevier Ltd.)

Somatic and F+ coliphage methods are under consideration as potential routine surface water quality monitoring tools to identify unsafe levels of fecal pollution in recreational waters. However, little is known about the cooccurrence of these virus-based fecal indicators and host-associated genetic markers used to prioritize key pollution sources for remediation. In this study, paired measurements of cultivated coliphage (somatic and F+) and bacterial (E. coli and enterococci) general fecal indicators and genetic markers indicative of human (HF183/BacR287 and HumM2), ruminant (Rum2Bac), canine (DG3), and avian (GFD) fecal pollution sources were assessed in 365 water samples collected from six Great Lakes Basin beach and river sites over a 15-week recreational season. Water samples were organized into groups based on defined viral and bacterial fecal indicator water quality thresholds and average log 10 host-associated genetic marker fecal score ratios were estimated to compare pollutant source inferences based on variable routine water quality monitoring practices. Eligible log 10 fecal score ratios ranged from -0.051 (F+ coliphage, GFD) to 2.08 (enterococci, Rum2Bac). Using a fecal score ratio approach, findings suggest that general fecal indicator selection for routine water quality monitoring can influence the interpretation of host-associated genetic marker measurements, in some cases, prioritizing different pollutant sources for remediation. Variable trends were also observed between Great Lake beach and river sites suggesting disparate management practices may be useful for each water type., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021. Published by Elsevier Ltd.)

Though Lake Michigan beaches in Chicago are not impacted by stormwater or wastewater outfalls, several of those beaches often exceed USEPA Beach Action Values (BAVs). We investigated the role of microbial source tracking (MST) as a complement to routine beach monitoring at Chicago beaches. In summer 2016, water samples from nine Chicago beaches were analyzed for E. coli by culture and enterococci by qPCR. A total of 195 archived samples were then tested for human (HF183/BacR287, HumM2), canine (DG3, DG37), and avian (GFD) microbial source tracking (MST) markers. Associations between MST and general fecal indicator bacteria (FIB) measures were evaluated and stratified based on wet and dry weather definitions. Among the 195 samples, HF183/BacR287 was quantifiable in 4%, HumM2 in 1%, DG3 in 6%, DG37 in 2%, and GFD in 23%. The one beach with a dog area was far more likely to have DG3 present in the quantifiable range than other beaches. Exceedance of general FIB BAVs increased the odds of human, dog and avian marker detection. MST marker weighted-average fecal scores for DG3 was 2.4 times, DG37 was 2.1 times, and GFD was 1.6 times higher during wet compared to dry weather conditions. HF183/BacR287 weighted-average fecal scores were not associated with precipitation. Associations between FIB BAV exceedance and MST marker detection were generally stronger in wet weather. Incorporating MST testing into routine beach water monitoring can provide information that beach managers can use when developing protection plans for beaches not impacted by point sources., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: The authors have no financial stake in the products described or in this research. The employer of two of the authors (AS and SD), the University of Illinois at Chicago, is contracted by the Chicago Park District to conduct beach monitoring, and that monitoring is managed by those authors. The beaches managed by the Chicago Park District are the subject of the manuscript., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

There is interest in the application of rapid quantitative polymerase chain reaction (qPCR) methods for recreational freshwater quality monitoring of the fecal indicator bacteria Escherichia coli (E. coli). In this study we determined the performance of 21 laboratories in meeting proposed, standardized data quality acceptance (QA) criteria and the variability of target gene copy estimates from these laboratories in analyses of 18 shared surface water samples by a draft qPCR method developed by the U.S. Environmental Protection Agency (EPA) for E. coli. The participating laboratories ranged from academic and government laboratories with more extensive qPCR experience to "new" water quality and public health laboratories with relatively little previous experience in most cases. Failures to meet QA criteria for the method were observed in 24% of the total 376 test sample analyses. Of these failures, 39% came from two of the "new" laboratories. Likely factors contributing to QA failures included deviations in recommended procedures for the storage and preparation of reference and control materials. A master standard curve calibration model was also found to give lower overall variability in log 10 target gene copy estimates than the delta-delta Ct (ΔΔCt) calibration model used in previous EPA qPCR methods. However, differences between the mean estimates from the two models were not significant and variability between laboratories was the greatest contributor to overall method variability in either case. Study findings demonstrate the technical feasibility of multiple laboratories implementing this or other qPCR water quality monitoring methods with similar data quality acceptance criteria but suggest that additional practice and/or assistance may be valuable, even for some more generally experienced qPCR laboratories. Special attention should be placed on providing and following explicit guidance on the preparation, storage and handling of reference and control materials., (Published by Elsevier Ltd.)

There is growing interest in the application of rapid quantitative polymerase chain reaction (qPCR) and other PCR-based methods for recreational water quality monitoring and management programs. This interest has strengthened given the publication of U.S. Environmental Protection Agency (EPA)-validated qPCR methods for enterococci fecal indicator bacteria (FIB) and has extended to similar methods for Escherichia coli (E. coli) FIB. Implementation of qPCR-based methods in monitoring programs can be facilitated by confidence in the quality of the data produced by these methods. Data quality can be determined through the establishment of a series of specifications that should reflect good laboratory practice. Ideally, these specifications will also account for the typical variability of data coming from multiple users of the method. This study developed proposed standardized data quality acceptance criteria that were established for important calibration model parameters and/or controls from a new qPCR method for E. coli (EPA Draft Method C) based upon data that was generated by 21 laboratories. Each laboratory followed a standardized protocol utilizing the same prescribed reagents and reference and control materials. After removal of outliers, statistical modeling based on a hierarchical Bayesian method was used to establish metrics for assay standard curve slope, intercept and lower limit of quantification that included between-laboratory, replicate testing within laboratory, and random error variability. A nested analysis of variance (ANOVA) was used to establish metrics for calibrator/positive control, negative control, and replicate sample analysis data. These data acceptance criteria should help those who may evaluate the technical quality of future findings from the method, as well as those who might use the method in the future. Furthermore, these benchmarks and the approaches described for determining them may be helpful to method users seeking to establish comparable laboratory-specific criteria if changes in the reference and/or control materials must be made., (Published by Elsevier Ltd.)

There is a growing interest for the use of coliphage as an alternative indicator to assess fecal pollution in recreational waters. Coliphage are a group of viruses that infect Escherichia coli and are considered as potential surrogates to infer the likely presence of enteric viral pathogens. We report the use of a dead-end hollow fiber ultrafiltration single agar layer method to enumerate F+ and somatic coliphage from surface waters collected from three Great Lake areas. At each location, three sites (two beaches; one river) were sampled five days a week over the 2015 beach season (n = 609 total samples). In addition, culturable E. coli and enterococci concentrations, as well as 16 water quality and recreational area parameters were assessed such as rainfall, turbidity, dissolved oxygen, pH, and ultra violet absorbance. Overall, somatic coliphage levels ranged from non-detectable to 4.39 log 10 plaque forming units per liter and were consistently higher compared to F+ (non-detectable to 3.15 log 10  PFU/L), regardless of sampling site. Coliphage concentrations weakly correlated with cultivated fecal indicator bacteria levels (E. coli and enterococci) at 75% of beach sites tested in study (r = 0.28 to 0.40). In addition, ultraviolet light absorption and water temperature were closely associated with coliphage concentrations, but not fecal indicator bacteria levels suggesting different persistence trends in Great Lake waters between indicator types (bacteria versus virus). Finally, implications for coliphage water quality management and future research directions are discussed., (Published by Elsevier Ltd.)

Human fecal pollution of recreational waters remains a public health concern worldwide. As a result, there is a growing interest in the application of human-associated fecal source identification quantitative real-time PCR (qPCR) technologies for water quality research and management. However, there are currently no standardized approaches for field implementation and interpretation of qPCR data. In this study, a standardized HF183/BacR287 qPCR method was combined with a water sampling strategy and a novel Bayesian weighted average approach to establish a human fecal contamination score (HFS) that can be used to prioritize sampling sites for remediation based on measured human waste levels. The HFS was then used to investigate 975 study design scenarios utilizing different combinations of sites with varying sampling intensities (daily to once per week) and number of qPCR replicates per sample (2-14 replicates). Findings demonstrate that site prioritization with HFS is feasible and that both sampling intensity and number of qPCR replicates influence reliability of HFS estimates. The novel data analysis strategy presented here provides a prescribed approach for the implementation and interpretation of human-associated HF183/BacR287 qPCR data with the goal of site prioritization based on human fecal pollution levels. In addition, information is provided for future users to customize study designs for optimal HFS performance., (Published by Elsevier Ltd.)

Understanding the decomposition of microorganisms associated with different human fecal pollution types is necessary for proper implementation of many water quality management practices, as well as predicting associated public health risks. Here, the decomposition of select cultivated and molecular indicators of fecal pollution originating from fresh human feces, septage, and primary effluent sewage in a subtropical marine environment was assessed over a six day period with an emphasis on the influence of ambient sunlight and indigenous microbiota. Ambient water mixed with each fecal pollution type was placed in dialysis bags and incubated in situ in a submersible aquatic mesocosm. Genetic and cultivated fecal indicators including fecal indicator bacteria (enterococci, E. coli, and Bacteroidales), coliphage (somatic and F+), Bacteroides fragilis phage (GB-124), and human-associated genetic indicators (HF183/BacR287 and HumM2) were measured in each sample. Simple linear regression assessing treatment trends in each pollution type over time showed significant decay (p ≤ 0.05) in most treatments for feces and sewage (27/28 and 32/40, respectively), compared to septage (6/26). A two-way analysis of variance of log 10 reduction values for sewage and feces experiments indicated that treatments differentially impact survival of cultivated bacteria, cultivated phage, and genetic indicators. Findings suggest that sunlight is critical for phage decay, and indigenous microbiota play a lesser role. For bacterial cultivated and genetic indicators, the influence of indigenous microbiota varied by pollution type. This study offers new insights on the decomposition of common human fecal pollution types in a subtropical marine environment with important implications for water quality management applications., (Published by Elsevier Ltd.)

Urban runoff can carry a variety of pollutants into recreational beaches, often including bacterial pathogens and indicators of fecal contamination. To develop complete recreational criteria and risk assessments, it is necessary to understand conditions under which human contamination could be present at beaches solely impacted by urban runoff. Accurately estimating risk requires understanding sources, concentrations, and transport mechanisms of microbial contaminants in these environments. By applying microbial source tracking methods and empirical modeling, we assessed the presence and level of human contamination at urban runoff impacted recreational beaches. We also identified environmental parameters and pollution sources that can influence the concentration and transport of culturable and molecular fecal indicator bacteria (FIB) in systems impacted solely by urban runoff. Water samples and physico-chemical parameters were collected from shoreline locations from three South Carolina (SC) beaches (five locations per beach) and two Florida (FL) beaches (three locations per beach). Each SC beach was directly impacted by swashes or tidal creeks receiving stormwater runoff from the urbanized area and therefore were designated as swash drain associated (SDA) beaches, while FL beaches were designated as non-swash drain associated (NSDA). Sampling in swash drains (SD; three sites per SD) directly impacting each SC beach was also conducted. Results indicate that although culturable (enterococci) and real-time quantitative polymerase chain reaction (qPCR) (EC23S857, Entero1, and GenBac3) FIB concentrations were, on average, higher at SD locations, SDA beaches did not have consistently higher molecular FIB signals compared to NSDA beaches. Both human-associated markers (HF183 and HumM2) were concomitantly found only at SDA beaches. Bacteroidales species-specific qPCR markers (BsteriF1 and BuniF2) identified differences in the Bacteroidales community, depending on beach type. The marker for general Bacteroidales was most abundant at SD locations and exhibited a high correlation with both culturable and other molecular markers. Combining molecular information with predictive modeling allowed us to identify both alongshore movement of currents and SD outflow as significant influences on the concentration of molecular and culturable indicators in the bathing zone. Data also suggests that combining methodologies is a useful and cost effective approach to help understand transport dynamics of fecal contamination and identify potential sources of contamination at marine beaches., (Published by Elsevier Ltd.)

The characteristics of fecal sources, and the ways in which they are measured, can profoundly influence the interpretation of which sources are contaminating a body of water. Although feces from various hosts are known to differ in mass and composition, it is not well understood how those differences compare across fecal sources and how differences depend on characterization methods. This study investigated how nine different fecal characterization methods provide different measures of fecal concentration in water, and how results varied across twelve different fecal pollution sources. Sources investigated included chicken, cow, deer, dog, goose, gull, horse, human, pig, pigeon, septage and sewage. A composite fecal slurry was prepared for each source by mixing feces from 6 to 22 individual samples with artificial freshwater. Fecal concentrations were estimated by physical (wet fecal mass added and total DNA mass extracted), culture-based (Escherichia coli and enterococci by membrane filtration and defined substrate), and quantitative real-time PCR (Bacteroidales, E. coli, and enterococci) characterization methods. The characteristics of each composite fecal slurry and the relationships between physical, culture-based and qPCR-based characteristics varied within and among different fecal sources. An in silico exercise was performed to assess how different characterization methods can impact identification of the dominant fecal pollution source in a mixed source sample. A comparison of simulated 10:90 mixtures based on enterococci by defined substrate predicted a source reversal in 27% of all possible combinations, while mixtures based on E. coli membrane filtration resulted in a reversal 29% of the time. This potential for disagreement in minor or dominant source identification based on different methods of measurement represents an important challenge for water quality managers and researchers., (Published by Elsevier Ltd.)

The State of California has mandated the preparation of a guidance document on the application of fecal source identification methods for recreational water quality management. California contains the fifth highest population of cattle in the United States, making the inclusion of cow-associated methods a logical choice. Because the performance of these methods has been shown to change based on geography and/or local animal feeding practices, laboratory comparisons are needed to determine which assays are best suited for implementation. We describe the performance characterization of two end-point PCR assays (CF128 and CF193) and five real-time quantitative PCR (qPCR) assays (Rum2Bac, BacR, BacCow, CowM2, and CowM3) reported to be associated with either ruminant or cattle feces. Each assay was tested against a blinded set of 38 reference challenge filters (19 duplicate samples) containing fecal pollution from 12 different sources suspected to impact water quality. The abundance of each host-associated genetic marker was measured for qPCR-based assays in both target and non-target animals and compared to quantities of total DNA mass, wet mass of fecal material, as well as Bacteroidales, and enterococci determined by 16S rRNA qPCR and culture-based approaches (enterococci only). Ruminant- and cow-associated genetic markers were detected in all filters containing a cattle fecal source. However, some assays cross-reacted with non-target pollution sources. A large amount of variability was evident across laboratories when protocols were not fixed suggesting that protocol standardization will be necessary for widespread implementation. Finally, performance metrics indicate that the cattle-associated CowM2 qPCR method combined with either the BacR or Rum2Bac ruminant-associated methods are most suitable for implementation., (Published by Elsevier Ltd.)

Quantitative polymerase chain reaction (qPCR) is increasingly being used for the quantitative detection of fecal indicator bacteria in beach water. QPCR allows for same-day health warnings, and its application is being considered as an option for recreational water quality testing in the United States (USEPA, 2011. EPA-OW-2011-0466, FRL-9609-3, Notice of Availability of Draft Recreational Water Quality Criteria and Request for Scientific Views). However, transition of qPCR from a research tool to routine water quality testing requires information on how various method variations affect target enumeration. Here we compared qPCR performance and enumeration of enterococci in spiked and environmental water samples using three qPCR platforms (Applied Biosystem StepOnePlus™, the BioRad iQ™5 and the Cepheid SmartCycler(®) II), two reference materials (lyophilized cells and frozen cells on filters) and two comparative CT quantification models (ΔCT and ΔΔCT). Reference materials exerted the biggest influence, consistently affecting results by approximately 0.5 log(10) unit. Platform had the smallest effect, generally exerting <0.1 log(10) unit difference in final results. Quantification model led to small differences (0.04-0.2 log(10) unit) in this study with relatively uninhibited samples, but has the potential to cause as much as 8-fold (0.9 log(10) unit) difference in potentially inhibitory samples. Our findings indicate the need for a certified and centralized source of reference materials and additional studies to assess applicability of the quantification models in analyses of PCR inhibitory samples., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Real-time PCR absolute quantification applications are becoming more common in the recreational and drinking water quality industries. Many methods rely on the use of standard curves to make estimates of DNA target concentrations in unknown samples. Traditional absolute quantification approaches dictate that a standard curve must accompany each experimental run. However, the generation of a standard curve for each qPCR experiment set-up can be expensive and time consuming, especially for studies with large numbers of unknown samples. As a result, many researchers have adopted a master calibration strategy where a single curve is derived from DNA standard measurements generated from multiple instrument runs. However, a master curve can inflate uncertainty associated with intercept and slope parameters and decrease the accuracy of unknown sample DNA target concentration estimates. Here we report two alternative strategies termed 'pooled' and 'mixed' for the generation of calibration equations from absolute standard curves which can help reduce the cost and time of laboratory testing, as well as the uncertainty in calibration model parameter estimates. In this study, four different strategies for generating calibration models were compared based on a series of repeated experiments for two different qPCR assays using a Monte Carlo Markov Chain method. The hierarchical Bayesian approach allowed for the comparison of uncertainty in intercept and slope model parameters and the optimization of experiment design. Data suggests that the 'pooled' model can reduce uncertainty in both slope and intercept parameter estimates compared to the traditional single curve approach. In addition, the 'mixed' model achieved uncertainty estimates similar to the 'single' model while increasing the number of available reaction wells per instrument run., (Published by Elsevier Ltd.)

Cryptosporidium parvum oocysts are prevalent in surface water and ground water under the influence of surface water, and are difficult to inactivate using free chlorine, the most common disinfectant currently used for treating drinking water. In contrast, it has been shown that ozone is a more effective disinfectant than chlorine. US EPA is currently evaluating a treatment rule, which addresses the control of C. parvum oocysts in drinking water. The use of Ct (average disinfectant concentration multiplied by characteristic contact time) values is being considered as one of the options for demonstrating adequate control of this microbial contaminant. The purpose of this study is to incorporate the variability in inactivation kinetics among different lots of oocysts and to develop a statistical model for Ct based on first-order delayed Chick--Watson inactivation kinetics. A Bayesian approach is used to estimate the delayed Chick--Watson kinetic parameters. A log-linear regression analysis is then used to represent the effect of temperature on the resulting kinetic parameters. The overall model developed in this study provides an approach for water utilities and regulatory agencies to decide on the level of safety needed when developing treatment requirements for the inactivation of C. parvum oocysts with ozone as part of broader risk assessment considerations.

The main objective of this paper is to use Bayesian methods to estimate the kinetic parameters for the inactivation kinetics of Cryptosporidium parvum oocysts with chlorine dioxide or ozone which are characterized by the delayed Chick-Watson model, i.e., a lag phase or shoulder followed by pseudo-first-order rate of inactivation. As the length of the lag phase (CT(lag)) is not known, Bayesian statistics provides a more accurate approach than traditional statistical methods to fitting the delayed Chick-Watson kinetics. Markov Chain Monte Carlo method is used to estimate CT(lag) and first-order rate constant values. This method is also used to estimate the minimum CT requirement (with safety factor) for 99% inactivation of C. parvum oocysts.

Cryptosporidium parvum, a protozoan parasite, has been implicated in a number of waterborne disease outbreaks and is difficult to inactivate using free chlorine. It appears, however, to be inactivated more easily by other oxidants such as chlorine dioxide or ozone. A major element of the EPA (US EPA) strategy for controlling C. parvum (oocysts) in drinking water is the possible use of Ct (concentration of disinfectant in mg/L times time in minutes) values. To support this strategy a Ct equation, based on first-order kinetics, is proposed to provide guidance to drinking water utilities for the application of chlorine dioxide for controlling C. parvum oocysts. The equation is based on standard statistical techniques using available bench scale data. It can predict mean inactivation levels as well as a statistically conservative upper bound Ct value. This upper bound could be used to insure an appropriate safety factor for the protection of public health.