Risk-Based Approach To Evaluatethe Public Health Benefit ofAdditional Wastewater TreatmentJ E F F R E Y A . S O L L E R * A N DA D A M W . O L I V I E R IEisenberg, Olivieri, and Associates, 1410 Jackson Street,Oakland, California 94612

J A M E S C R O O K

Norwell, Massachusetts 02061

R O B E R T C . C O O P E R

BioVir Laboratories, Benicia, California 94510

G E O R G E T C H O B A N O G L O U S

University of California, Davis, California 95616

R E B E C C A T . P A R K I N

George Washington University, Washington, DC 20052

R O B E R T C . S P E A R A N DJ O S E P H N . S . E I S E N B E R G

University of California, Berkeley, California 94720

The City of Stockton, CA operates a wastewater treatmentfacility that discharges tertiary treated effluent duringthe summer and secondary treated effluent during the winterto the San Joaquin River. Investigations were carriedout between 1996 and 2002 to provide insight regardingthe potential public health benefit that may be provided byyear-round tertiary treatment. A hydraulic model of theSan Joaquin River and a dynamic disease transmissionmodel integrated a wide array of disparate data to estimatethe level of viral gastroenteritis in the population underthe two treatment scenarios. The results of the investigationsuggest that risk of viral gastroenteritis attributable tothe treatment facility under the existing treatment schemeis several orders of magnitude below the 8-14 illnessesper 1000 recreation events considered tolerable by U.S. EPA,and winter tertiary treatment would further reduce theexisting risk by approximately 15-50%. The methodologiesemployed herein are applicable to other watershedswhere additional water treatment is being considered toaddress public health concerns from recreation in receivingwaters.

IntroductionThe City of Stockton, CA operates a wastewater treatmentfacility that discharges treated effluent to the San JoaquinRiver (river). Annual operation of the facility has includedsecondary treatment via stabilization ponds followed byfiltration (tertiary treatment) and chlorine disinfectionbetween May and October and secondary treatment withchlorine disinfection the rest of the year (Figure 1). Designated

beneficial uses of the river include water contact andnoncontact recreation, agricultural supply, and municipaland domestic use (1). California water quality regulationsstate that “waters of the state shall be regulated to attain thehighest water quality which is reasonable, considering alldemands being made and to be made on those waters andthe total values involved, beneficial and detrimental, eco-nomic and social, tangible and intangible” (2). During thedischarge permit renewal process, the California RegionalWater Quality Control Board asserted that the winter seasondischarge “has a reasonable potential to cause a health riskfor contact recreation in the San Joaquin River near thevicinity of the discharge” (3). This investigation was initiatedto determine if the addition of tertiary treatment to thefacility’s operation during the winter season would sub-stantially reduce the risk the public faces via recreation inthe river for existing (1.0e8-1.5e8 L/d) and potential future(2.0e8 L/d) effluent flows. As a point of reference, U.S. EPAconsiders water quality standards for freshwater to beprotective if a state’s criteria are based on an illness rateequal to or less than 14 illnesses per 1000 recreation events(4, 5).

Quantifying the public health risk associated with inges-tion of waterborne pathogens is an evolving field. In the past,the majority of quantitative microbial risk assessmentinvestigations were carried out by employing chemical stylestatic models to calculate the probability of infection ordisease resulting from an exposure event from specificpathogenic agents (6-10). That approach presumes that littlenet error is made by neglecting the processes unique to thetransmission of infectious diseases, such as protection frominfection (immunity) and the potential for subsequentperson-to-person transmission of infection.

Although modeling the transmission of infectious diseasesas a dynamic process may require substantially more datathan do static models, dynamic models can be configuredto account for population dynamics (11) and protection frominfection due to prior exposures (12). Nevertheless, fewresearchers have employed dynamic models to characterizethe risk to human health associated with waterbornepathogens (11-15). In this investigation, existing hydraulic(16) and disease transmission models (14, 17, 18) wereextended to characterize quantitatively the incrementalbenefit that tertiary treatment would provide over thatprovided by secondary treatment during the winter seasonat the City of Stockton wastewater treatment facility. For thisinvestigation, incremental benefit is defined as a relativecomparison of viral gastroenteritis attributable to the treat-ment facility for the treatment scenarios investigated.

Experimental MethodsThe methodology for this investigation was to estimate vianumerical simulation the difference in viral gastroenteritisin the population attributable to the wastewater treatmentfacility under various treatment conditions, based on a modelenteric virus. Numerical adaptation of the hydraulic anddisease transmission models required extensive data col-lection and numerical implementation efforts.

Link between Water Quality and Public Health. Oneimportant aspect of this investigation was to identify anappropriate link between water quality data and risk to publichealth. Based on research conducted over the last 20 years,it is reasonable to infer that the primary risk from recreationalexposure to waterborne pathogens is associated with gas-troenteritis from viral contamination (19-25). Recreation infecally contaminated receiving waters may also cause other

* Corresponding author phone: (510)832-2852; fax: (510)832-2856;e-mail: jasoller@eoainc.com.

Environ. Sci. Technol. 2003, 37, 1882-1891

adverse health outcomes such as acute febrile respiratoryillness (26), general respiratory illness, ear infections (26),eye ailments, skin rashes (27), and other less commonalthough more serious health outcomes. Although thecumulative risk faced by recreators is a function of all of thepathogens present in the receiving water and the potentialsubsequent health outcomes, this investigation in a mannerconsistent with State and federal regulatory guidelines focuseson the risk of gastroenteritis.

The recovery and detection of animal viruses in water istechnically difficult, time-consuming, and expensive, andfurther the epidemiologically important viruses are stilldifficult to reliably quantify in water (28). Bacterial indicatororganisms are therefore often used as a surrogate measureof microbiological water quality in ambient waters. In fact,a number of investigations have found correlations betweenbacterial densities and gastroenteritis in recreational waters(24, 29-31), and U.S. EPA’s recommended water qualitycriteria for recreational waters are specified in terms ofbacterial indicator densities (4, 5). The utility of bacterialorganisms as quantitative indicators of viral pathogens inambient waters has however been questioned (28, 32-39).Because the thrust of this investigation was to determine ifincremental water treatment would result in a substantiallyreduced risk to public health, a numerical simulation studywas undertaken focusing on enteric viruses as the etiologicalagent of interest and risk of gastroenteritis as the endpoint.

In this assessment, a combination of conservative andrealistic assumptions was employed so that the results of theassessment would be health protective yet practical (40). Amodel enteric virus was employed to characterize conser-vatively and representatively the risk to public health thatmay be associated with exposure to the epidemiologicallyimportant enteric viruses via recreational activities in theriver. For the purposes of this assessment, it was assumedthat the model virus possessed the clinical features ofrotavirus, as rotavirus is the most infectious virus for whichhuman dose response data are available (41). It was alsoassumed that coliphage that infect male (F+) Escherichia colipresent equal to or greater environmental persistence as thevarious enteropathogenic viruses of public health concern(28, 42, 43). These coliphage will herein be referred to as MScoliphage. Sensitivity analysis was employed to examine howpotentially important attributes of the model virus and theenvironmental exposure may impact the overall findings ofthe investigation.

Field Data Collection. The field data collection includedan exposure assessment, an assessment of virus inactivationin San Joaquin River water, and an estimation of virus removalefficiency at the treatment facility under secondary andtertiary treatment.

The exposure assessment estimated the number of peoplerecreating in the river in the vicinity of the discharge (44).Data based on aerial photographs and interviews with 23state and local agencies were used to establish the following:(1) the spatial boundaries of the study area; (2) the daily

distribution of recreational activity; (3) the seasonal distribu-tion of recreational activity; (4) the increase in recreationduring the weekends and holidays; (5) the baseline numberof boats in the study area; (6) the overall spatial distributionof recreators in the study area; and (7) the size of thepopulation that may participate in recreational activities.

Virus inactivation experiments were carried out by BioVirlaboratories (Benicia, California) in duplicate for MS coli-phage and attenuated polio virus in San Joaquin River water.One-liter samples of river water were seeded with knownconcentrations of MS coliphage and poliovirus. One millilitergrab samples were subsequently collected from the 1 Lsamples over an 8-day period and were analyzed for poliovirus and MS coliphage concentrations. Virus concentrationwas plotted versus time based on the experimental data, andvirus inactivation rates were estimated as the time requiredfor 90% inactivation (T90).

Virus reduction across the treatment facility was inves-tigated so that virus concentrations discharged to the rivercould be estimated. MS coliphages were employed in placeof human enteric viruses for this component because theyare present in large numbers in raw wastewater (28), areconsiderably less time-consuming and costly than entericviruses to enumerate, and exhibit similar or greater resistanceto conventional wastewater treatment as viruses of publichealth concern (28, 42, 43). Data collection was composedof 24-h composite samples of the influent wastewater (n )48), chlorinated secondary effluent (n ) 24), and chlorinatedtertiary effluent (n ) 24). Microbiological analyses werecarried out via a plaque assay methodology (EPA Method1602, draft modified) by BioVir laboratories.

The microbiological analyses were used to characterizethe performance of the treatment facility during secondaryand tertiary operations. Statistical distributions describingthe expected concentrations of MS coliphage in the facility’sinfluent and chlorinated effluents were generated via themethod of maximum likelihood using Crystal Ball software(Decisioneering, Inc.). Skewness was used as the significantcriteria to identify appropriate distributional forms (45).Distributions describing the expected secondary and tertiarytreatment efficiencies with respect to virus reduction werethen generated using Monte Carlo techniques, as follows:15 000 influent and effluent concentration values weregenerated from the distributions described above, treatmentreduction efficiencies were computed from paired concen-tration values, and distributions of removal were fit to thecomputed treatment reduction efficiency data via the methodof maximum likelihood. Removal efficiencies could not becomputed directly from the experimental data, because themedian detention time in the oxidation ponds is ap-proximately 60 days, thus the field data could not be treatedas paired data.

Modeling Approach. Numerical modeling consisted ofintegrating two distinct simulation models: a hydraulic model(16, 46) and a disease transmission model (14, 17, 18). Thehydraulic modeling was carried out to simulate river water

FIGURE 1. Process flow schematic for the City of Stockton Treatment Facility.

quality near the discharge. A near field submodel simulatedthe facility discharge plume and computed the initial dilutionof viruses discharged in the river. A far field submodelaccounted for the dynamic variability in the receiving waterby simulating the hydrodynamic and water quality conditionsin the study area. The hydraulic model was successfullycalibrated against surface elevations, a die mixing study, andobserved physical water quality data (46). Seven years ofhydrologic and climatic conditions were simulated (1988-1994) representing river flow and microbiological waterquality for critically dry to above normal hydrologic condi-tions.

Disease transmission modeling was carried out to estimatethe community enteric virus disease incidence, as repre-sented by the model virus. Although a variety of diseaseoutcomes is associated with enteric viruses, gastroenteritiswas the focus of this investigation. The disease transmissionmodel development did include a component for moreserious disease outcomes from exposure to viruses (Figure2); however, sufficient quantitative data were not found toallow the characterization of these comparatively uncommonoutcomes. Mathematically, the movement of the populationbetween epidemiological states was modeled as a series ofordinary differential equations (47, 48). A list of the modelparameters is presented in Table 1 along with a summary ofthe mathematical details describing the movement of thepopulation from one time step to the next. A more detaileddiscussion of the model and examples illustrating thedynamic behavior of the model are provided in the Sup-porting Information. The model is composed of 5 statevariables, 11 model parameters, and 3 intermediate param-eters that are used to pass data to the hydraulic model and1 input parameter that was used to accept data from thehydraulic model (Figure 2). State variables track the numberof individuals in each of the relevant epidemiological states:X - individuals susceptible to infection; Y - infectious butasymptomatic individuals; D - infectious and symptomaticindividuals; C - postsymptomatic and infectious individuals;and Z - postsymptomatic and noninfectious individuals (withpartial protection from infection). The 11 model parametersdefine the rate of movement of individuals between epide-miological states, the endemic transmission rate of virusesin the community, and the infection rate in the communitydue to ingestion of viruses from the San Joaquin River.

To define the 11 model parameters, 25 pieces of datawere required. Therefore, sampling parameters were estab-lished (Table 2), each with bounds sufficient to account forthe variability and uncertainty of values identified throughan extensive literature survey and/or site specific data. Themost important health protective assumptions employed inassigning parameter ranges were that all participants inrecreational activities ingested river water, the model virusexhibited the clinical features including infectivity of rotavirusand environmental persistence and treatment resistancesimilar to MS coliphage, and microbiological data reportedbelow detectable limits were assumed to be present at thatlimit.

Simulation Approach. Exposure to enteric viruses wasassumed to occur from the following sources: exposure toviruses through every day activities not including recreationin the river (background), exposure to viruses throughrecreation in the river from all sources except the treatmentfacility, and exposure to viruses through recreation in theriver attributable to the treatment facility when operating insecondary and tertiary modes. To account for this range ofexposures, four scenarios were simulated numerically (ex-posure conditions): (1) background, (2) background plusrecreation with no treatment plant discharge, (3) background,recreation, and secondary effluent discharge, and (4) back-ground, recreation, and tertiary effluent discharge.

One thousand simulations, each representing a 6-monthtime period were carried out by randomly sampling theparameter distributions (Table 2). During each simulation,each of the exposure conditions described above was run.The disease transmission model tracked the number ofindividuals in each epidemiological state, the total numberof recreation events, and the incidence of gastroenteritis (totalnumber of people entering state D). Output from this set ofsimulations is the difference in risk attributable to thedischarge per recreation event under the two treatmentconditions. In this investigation, risk attributable to thedischarge per recreation event is defined as the risk ofgastroenteritis per recreation event with the treatment facilitydischarging (exposure scenarios 3 and 4, above) minus therisk per recreation event with no discharge (scenario 2, above).Risk per recreation event is defined as the total number ofestimated cases of gastroenteritis divided by the number ofrecreation events during the simulation period.

FIGURE 2. Disease transmission variables and parameters.

Sensitivity Analysis. Univariate sensitivity analyses werecarried out to investigate how the results from the modelingwere impacted by the input parameter values and todetermine which parameters most strongly influenceddisease incidence in the population under study. Themethodology employed was to search for important trendsin the data by plotting the predicted gastroenteritis incidenceagainst the sampled parameter values.

ResultsDiscussed below are the results for this investigation includingthe exposure assessment results, virus inactivation results,virus removal efficiency results, simulation results, andsensitivity analyses.

Exposure Assessment Results. The field data wereintegrated to characterize the recreational levels in the river.The intensity of recreation varies by month, day of the week,hour of the day, and whether each day is a holiday. Theexposure variables shown in Table 2 scale the exposure levelrelative to a weekday in October: m - increased exposureduring different months; d - increased exposure duringweekends; h - increased exposure during holidays; pb -number of people on boats; hour - exposure over the courseof a day (described as a modified triangular distribution withmaximum values constant from 10 a.m. and 2 p.m.); baseline- number of exposed individuals for a weekday in October;and Xo - number of individuals initially in the susceptiblestate (assumed to be equal to the estimated total populationfor the study area). Exposure assessment data are used togenerate time series profiles of recreational activities inthe river. A representative exposure profile is presented inFigure 3.

Virus Inactivation Results. The virus inactivation experi-ment resulted in a total of 10 analyses. The virus inactivation

rates calculated from these results corresponded to T90 valuesof 0.75 days for attenuated polio virus and 3.2 days for MScoliphage in San Joaquin River water. These results seem tobe consistent with those reported by Sinton et al. (49) forbacteriophage survival in natural waters.

Virus Removal Efficiency Results. The microbiologicalmonitoring of the treatment facility found 20 of 24 secondaryeffluent samples and nine of 24 tertiary effluent samples tobe below the method detection limit of 0.05 plaque formingunits per mL (Figure 4). This counterintuitive result is likelydue to the fact that the influent coliphage levels were lowerby a factor of approximately 10 during the time period thatthe facility was operating in secondary mode. Based on theobserved data, the MS coliphage removal efficiency of thetreatment facility was approximated as a normal distributionin log units, where log reduction is defined as base 10logarithm of the influent divided by the effluent concentra-tion. The expected mean removal efficiency of the facilitywhen operating with tertiary treatment is 4.56 logs with a

TABLE 1. Summary of Variables, Parameters, and Equations Governing Movement of Population between Epidemiological States

a Sampled parameters are defined in Table 2.

FIGURE 3. Number of recreators during a representative simulation,May-October 1993.

TABLE 2. Summary of Parameters Sampled during Monte Carlo Simulations

parametertype parameter description rangea units basis reference

biological FT incubation period 24-72 hours 1-3 day incubation (54-56)FP fraction of state Y that moves to state D 0.1-0.4 percent 10-40% infected develop symptoms (57, 58)γ rate of movement from state Z to state X 3.8e-5-1 h-1 0-3 yr duration of immunity, accounts (59, 60)

(log-uniform) for no immunity to reported immunityσ rate of movement from state D to state C 3.8e-3-2.1 e-2 h-1 2-11 day duration of symptoms (61, 62)ε rate of movement from state C to state Z 6.0e-3-2.0e-3 h-1 1-3 week duration of shedding (63, 64)δ fraction in state D who die due 0 h-1 0% U.S fatality rate insufficient data

to disease per day for other outcomesa rate of migration of new susceptibles 2.9e-6-4.0e-6 h-1 global birth rate (65)

into populationυ death rate - other causes 5.7e-7-1.7e-6 h-1 global death rate (65)λf rate of virus shedding per 1e5-1e10 virus/h site specific data based on ambient (44)

infectious swimmer (log-uniform) wq and hydraulic modelingâo background transmission rate 1.5e-6-4.6e-6 h-1 1-4% seroconversion per year (44, 66, 67)âBp dose response parameter 0.3-2.3 unitless MLE 95% confidence interval (9, 63)RBp dose response parameter 0.15-0.42 unitless MLE 95% confidence interval (9, 63)ú rate of virus die-off 0.18-0.3 day-1 T90 of 3-5 days, site specific, see text (44)epratio ratio of enteric virus to bacteriophage 1e-3-1.0 unitless conservative range for reported (42, 44, 68-75)

(log-uniform) and site specific data

community Xo initial number of individuals in state X 2.8e6 individuals site specific data, see text (44)âI rate of recreational water ingestion 0-0.05 L/h 0-50 mL/hour (76)baseline baseline number of exposed individuals 26-263 individuals site specific data, see text (44)pb number of people per boat 1-4 (weekdays) individuals/ site specific data (44)

1-5 (weekends) boat see textm exposure factor by month 1.14-2.64 (May) unitless site specific data (44)

1.5-3.16 (June) (note: Oct-April1.5-3.32 (July) assumed ) 1.0)1.5-3.89 (Aug) see text1.14-2.3 (Sept)

d weekend exposure factor 1 (weekdays) unitless site specific data (44)2-5 (weekends) see text

h holiday exposure factor 1 (nonholiday) unitless site specific data (44)2-3 (holiday) see text

hour hourly (interday) exposure factor 0-1b unitless site specific data (44)see text

water quality CE bacteriophage concentration in 1e4-5e4 pfu/100 mL site specific data (44)raw wastewater see text

TE -3° tertiary operation N(4.6, 0.75)c log reduction site specific data (53)coliphage reduction see text

TE-2° secondary operation N(4.18, 0.39)c log reduction site specific data (53)coliphage reduction see text

Q existing effluent flow 1.0e8-1.5e8 L/day site specific data (44)potential future effluent flow 2.1 e8 see text

a Ranges represent upper and lower values of uniform distribution, other distributions are specified b Triangular distribution. 0 from 12 a.m. to 6 a.m., increasing linearly to 1 at 10 a.m., 1 from 10 a.m. to 2 p.m.,decreasing linearly to 0 at 8 p.m., 0 from 8 p.m. to 12 a.m. c Normal distribution with mean equal to first value in parentheses, and standard deviation equal to second value.

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standard deviation of 0.76 logs, and the expected meanremoval efficiency when the facility is operating withsecondary treatment is 4.17 logs with a standard deviationof 0.39 logs. Based on the proportion of data below detectablelimits, these removal distributions likely underestimate thetrue removal effectiveness and variability of treatment.

Simulation Results. Representative output from thedisease transmission model is presented in boxplot formatin Figure 5 for simulations in which tertiary treatment wasemployed with effluent flow of 1.0e8-1.5e8 L/d (existingoperations). The results presented in Figure 5 represent thenumber of gastroenteritis cases during a 6-month periodattributable to four different exposure conditions: (1)background; (2) recreation plus tertiary discharge; (3)recreation with no discharge; and (4) tertiary discharge only.As shown, the median number of viral gastroenteritis casesin the community that occurred due to background exposurewas approximately 1000 times higher than those in thecommunity attributable to recreation in the river with orwithout the discharge. This result is likely due to the fact thata small proportion of the population was recreating comparedto the total size of the population. Also of note are the uppertails of the distributions presented in Figure 5. Thesecomparatively high levels of viral gastroenteritis generallyoccurred when the levels of virus shedding from recreators(λF) was in the upper end of the sampled range (Figure 6).Improved information characterizing the levels of virusparticles shed by recreators would lead to decreased vari-ability in the simulation results.

The rate of risk that EPA considers acceptable forrecreation in freshwater is approximately 0.014 illnesses perrecreation event. Using that metric for comparison, the riskof viral gastroenteritis attributable to the discharge perrecreation event is illustrated in Figures 7 and 8 which presentthe relation between treatment efficiency and risk attributableto the treatment facility per recreation event for existing flowsduring critically dry hydrologic conditions with tertiary andsecondary treatment, respectively. As illustrated, the marginalrisk attributable to the facility from recreating in the riverdecreases as treatment efficiency improves. Cumulativeprobability plots for the risk attributable to the discharge perrecreation event are presented for tertiary and secondarytreatment with flows of 1.0e8-1.5e8 L/d in Figure 9. Simulationresults for flows of 2.0e8 L/d are presented in the SupportingInformation.

Based on the simulation results, the median level of riskattributable to the discharge per recreation event for theconditions investigated ranges between 2 × 10-6 and 6 ×10-6 (Table 3). Further, 95th percentile values ranged between2 × 10-4 and 6 × 10-4. Interestingly, the predicted 95thpercentile value for tertiary treatment at a flow of 2.0e8 L/dwas slightly higher than that for secondary treatment. Thisunexpected result most likely derives from the fact that theobserved variability in tertiary treatment was greater thanthat for secondary treatment and appears to be an artifactof assuming that the microbiological data reported belowthe detection limit are present at that limit. Based on theanalysis presented herein and summarized in Table 3, the

FIGURE 4. Coliphage monitoring results for tertiary and secondary treatment operations.

FIGURE 5. Simulation results: number of cases of viral gastro-enteritis for a 6-month time period under four exposure scenarios. FIGURE 6. Simulation results: relation between viral gastroenteritis

and shedding.

median reduction of viral gastroenteritis from tertiary treat-ment during the winter season is estimated to be on theorder of 1 avoided case per 1 000 000 recreation events and

is 4 or less avoided cases per 100 000 recreation events at the95th percentile level. Similar results were obtained for abovenormal hydrologic conditions (not shown).

Sensitivity Analysis Results. Graphical analyses werecarried out to investigate how sensitive the results were tothe input parameter values and which parameters moststrongly influenced the predicted incidence of viral gastro-enteritis. Figures showing representative results of thesensitivity analyses are presented in the Supporting Infor-mation. The two parameters most strongly influencing thebackground level of viral gastroenteritis incidence in thecommunity are the percent of infected individuals thatdevelop symptoms (FP) and the background transmissionrate (Β0). Other parameters did not substantially impact thebackground level of viral gastroenteritis incidence.

The parameter that appears to most strongly influencethe level of viral gastroenteritis incidence in the communityattributable to recreation is the rate of virus shedding perinfectious swimmer (λF). Treatment efficiency was the onlyparameter to substantially impact the viral gastroenteritisincidence attributable to the treatment facility (Figures 7and 8), although the ratio between the model virus and MScoliphage did impact the viral gastroenteritis incidence atthe upper end of the sampled range.

DiscussionIn this investigation, a wide array of disparate data wasintegrated to address the complex problem of assessing riskfrom exposure to recreational water with different degreesof wastewater treatment. The benefit to public health ofadditional treatment was evaluated with respect to a modelenteric virus and was expressed as the relative risk associatedwith the two treatment conditions. The endpoint investigatedwas gastroenteritis. The interpretation of the results presentedin the previous section should be tempered by the scope ofthe investigation, the inherent uncertainty in parametervalues, and limitations of the field data collection efforts.

The primary advantage of employing a simulation basedmethodology was the ability to evaluate the potential benefitsassociated with a proposed management option. It shouldhowever be understood that during any recreation event, anindividual may be exposed to a number of different pathogensderived from a number of different sources. Although, it isnot practical to estimate the cumulative risk from recreatingvia a simulation study such as this one, it is neverthelessfeasible to frame an investigation in a manner such thatpractical risk management decisions may be considered. Itis with this perspective that this investigation was designedand implemented. Issues related to the interpretation of theresults of this investigation are discussed below.

Comparison with U.S. EPA’s Criteria. The thrust of thisinvestigation was to characterize the relative public healthbenefit associated with additional wastewater treatmentrather than to estimate the cumulative risk associated withrecreating in the river. Nevertheless, comparing the relativelevel of viral gastroenteritis attributable to the treatmentfacility to that considered acceptable by EPA for recreationin freshwater provides a valuable perspective on the resultspresented herein. Specifically, given the conservative as-sumptions employed in this investigation, it is reasonable toinfer that the risk of viral gastroenteritis from recreation inthe river attributable to current operations at the treatmentfacility is considerably below the risk level consideredacceptable by U.S. EPA. Winter tertiary treatment wouldfurther reduce the existing risk of viral gastroenteritisattributable to the treatment facility by approximately 15-50%.

Variability and Uncertainty. From the sensitivity analyses,it was found that the two parameters most strongly relatedto the incidence of gastroenteritis attributable to recreating

FIGURE 7. Risk of viral gastroenteritis attributable to discharge perswim event versus treatment efficiency for tertiary treatment.

FIGURE 8. Risk of viral gastroenteritis attributable to discharge perswim event versus treatment efficiency for secondary treatment.

FIGURE 9. Cumulative probability plot for risk of viral gastroenteritisattributable to discharge per swim event for tertiary and secondarytreatment with 1.0e8 to 1.5e8 L/d flows.

in the river under current conditions are the rate of virusshedding from swimmers and the virus removal efficiencyof the treatment facility. Although a substantial effort wasundertaken to characterize the treatment efficiency of thefacility, the accuracy of that effort was limited because someof the effluent concentrations under both conditions werereported to be below detectable limits. The distributions oftreatment efficiency reported herein likely underpredict thetrue treatment efficiencies of secondary and tertiary treatmentat the facility. The relative difference in treatment efficiencybetween secondary and tertiary treatment reported hereinfor MS coliphage is slightly less than that reported previouslyin the literature for both poliovirus and MS coliphage (45,50), possibly due to the observations reported below detect-able limits. The uncertainty in relative effectiveness oftreatment between the conditions studied does not, however,preclude gaining insight from the investigation when con-sidered in conjunction with the simulation results. Giventhat the effectiveness of treatment is based on assuming thatdata below the detection limit are present at that limit, therisk reported herein for current winter operation representsa reasonable upper bound. Reducing the uncertainty intreatment effectiveness could refine the associated estimateof relative risk, but given the estimated magnitude of riskattributable to the treatment facility relative to that consideredacceptable by EPA, it seems unlikely that reducing theuncertainty in treatment efficiency would substantially alterthe estimated benefits of tertiary treatment relative to thelevels of risk that are currently considered tolerable forrecreational activities in freshwaters. Similarly, uncertaintyin the rate of viral shedding by individual recreationists couldpotentially be decreased through further research; however,it seems unlikely that the relative results of this investigationwould have been substantially altered had this modelparameter been defined more narrowly.

Another parameter with substantial uncertainty was theratio of enteric virus to coliphage. In this investigation, thesampled values for this parameter encompassed limited sitespecific data and the range of data reported in the literature.The most appropriate range of values for this parameter isundoubtedly site specific. The uncertainty associated withthe parameter range employed herein could be decreasedthrough future research which could lead to less uncertainestimates of the public health risk associated with recreatingin receiving waters. Similarly, if direct measurements of virusconcentrations become available in the future, the associatedestimates of risk to public health and the benefit of additionalwastewater treatment using the approach demonstrated herewill be improved.

Limitations. Given the complexity of assessing the riskfrom exposure to recreational water, a number of method-ological assumptions were required. The limitations of theanalyses presented herein generally stem from those as-sumptions. One fundamental limitation is that the resultsare applicable only for parameter values within the range ofthose investigated. Although a substantial amount of carewas taken to characterize the parameter values in a health

protective manner, it is possible that parameter values couldbe refined or changed based on future research.

The health outcome associated with infection and diseasein this investigation was gastroenteritis. There are a numberof other more serious disease outcomes that are alsoassociated with enteric viruses and characterizing the riskassociated only with gastroenteritis likely underestimates thetrue cumulative risk to public health. Characterizing otherendpoints more serious than gastroenteritis was beyond thescope of this investigation; however, the likelihood for suchhealth outcomes are important, should be considered duringthe risk management process, and should be the focus offuture research.

A related limitation of this investigation is that microbialrisk assessment methodologies do not characterize thecumulative risk associated with all pathogens potentiallypresent in an environment. Indicator organisms have beenused for years because of the inability to take a universallydefensible agent-by-agent approach. The same problemcarries over to risk assessment, which is inherently agentspecific, in that it is not practical to carry out separateassessments for all pathogens that may be present. So wechose a variant of that theme and chose an outcome, viralgastroenteritis, and synthesized a model organism thatcaptured the salient features of viruses commonly known tobe in water and cause this outcome. The resulting analysismust be interpreted within this indicator context andextrapolation to other organisms, even viruses with dissimilarproperties, must be done with caution.

Finally, it should be noted that storm events and theirassociated urban runoff were not specifically modeled inthis investigation. For the purposes of characterizing theincremental benefit of additional winter treatment it wasassumed that recreation would be extremely limited duringthe winter season during a storm event in this location.

Vulnerable or Highly Susceptible Subpopulations. Underthe 1996 Amendments to the Safe Drinking Water Act, theU.S. EPA must consider susceptible subpopulations in itshealth risk assessments. With respect to microbial exposures,limited consideration has been given to the degree to whichindividuals may differ in the completeness of protectionoffered by their immune systems (51). In this investigation,an attempt was made to quantitatively characterize risk tohighly susceptible or vulnerable subpopulations via recre-ational activities. However, sufficient data were not availableto enable the characterization of risk for those subpopulationsseparately from the population at-large (52, 53). This findingis consistent with other recently published work (77).

Risk Management. In this investigation, quantitativemicrobial risk assessment techniques were used to provideinsight regarding the potential public health benefits as-sociated with tertiary treatment over that provided bysecondary treatment during the winter season at the City ofStockton, CA wastewater treatment facility. In summary, thepertinent risk management issue is balancing the publichealth benefit of additional treatment at this facility with theassociated costs and deciding if the additional treatment is

TABLE 3. Incremental Benefit of Tertiary Operation of the Treatment Facility during the Winter Season with Respect toRecreational Exposures

climaticconditions flow treatment

treatment efficiencydistribution

medianriska

incrementalbenefitb

95th %ileriska

incrementalbenefitc,d

critically dry 1.0e8-1.5e8 L/d tertiary N(4.6, 0.75)e 2E-06 2E-06 2E-04 4E-05secondary N(4.18,0.39)e 4E-06 3E-04

2.0e8 L/d tertiary N(4.6,0.75)e 5E-06 1E-06 6E-04secondary N(4.18,0.39)e 6E-06 4E-04

a Probability of illness. b Based on Median Risk Values. c Based on upper 95 CL Risk Values. d See text regarding 95th %ile incremental benefitat 2.0e8L/d. e Normal distribution with mean equal to first value in parentheses, and standard deviation equal to second value.

warranted during the winter season. This investigation wascarried out to provide risk managers with the technicalinformation needed to consider this important issue. Themethodologies described herein are applicable to otherwatersheds where the benefit of additional water treatmentwith respect to public health concerns must be addressed.

AcknowledgmentsFunding for this investigation was provided by the City ofStockton, Municipal Utilities Department. Jon Konnan,Roanne Ross, and Edmund Seto contributed substantially tothe initial phase of the investigation. The authors thank DonDodge, Tim Anderson, and Morris Allen for their support ofthis investigation and Robert Hultquist, Steven Book, RichardSakaji, and Carl Lischeske for their constructive input andreview. Finally, the authors acknowledge the anonymousreviewers for the critical review of the manuscript.

Supporting Information AvailableDescription of and output from disease transmission model(Appendix S1) and representative resuslts from sensitivityanalysis (Appendix S2). This material is available free of chargevia the Internet at http://pubs.acs.org.

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Received for review May 8, 2002. Revised manuscript re-ceived January 3, 2003. Accepted February 4, 2003.

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