Risk-Based Approach To Evaluate the Public Health Benefit of

Investigations were carried out between 1996 and 2002 to provide insight regarding the potential public health benefit that may be provided by year-ro...
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Environ. Sci. Technol. 2003, 37, 1882-1891

Risk-Based Approach To Evaluate the Public Health Benefit of Additional Wastewater Treatment JEFFREY A. SOLLER* AND ADAM W. OLIVIERI Eisenberg, Olivieri, and Associates, 1410 Jackson Street, Oakland, California 94612 JAMES CROOK Norwell, Massachusetts 02061 ROBERT C. COOPER BioVir Laboratories, Benicia, California 94510 GEORGE TCHOBANOGLOUS University of California, Davis, California 95616 REBECCA T. PARKIN George Washington University, Washington, DC 20052 ROBERT C. SPEAR AND JOSEPH N. S. EISENBERG University of California, Berkeley, California 94720

The City of Stockton, CA operates a wastewater treatment facility that discharges tertiary treated effluent during the summer and secondary treated effluent during the winter to the San Joaquin River. Investigations were carried out between 1996 and 2002 to provide insight regarding the potential public health benefit that may be provided by year-round tertiary treatment. A hydraulic model of the San Joaquin River and a dynamic disease transmission model integrated a wide array of disparate data to estimate the level of viral gastroenteritis in the population under the two treatment scenarios. The results of the investigation suggest that risk of viral gastroenteritis attributable to the treatment facility under the existing treatment scheme is several orders of magnitude below the 8-14 illnesses per 1000 recreation events considered tolerable by U.S. EPA, and winter tertiary treatment would further reduce the existing risk by approximately 15-50%. The methodologies employed herein are applicable to other watersheds where additional water treatment is being considered to address public health concerns from recreation in receiving waters.

Introduction The City of Stockton, CA operates a wastewater treatment facility that discharges treated effluent to the San Joaquin River (river). Annual operation of the facility has included secondary treatment via stabilization ponds followed by filtration (tertiary treatment) and chlorine disinfection between May and October and secondary treatment with chlorine disinfection the rest of the year (Figure 1). Designated * Corresponding author phone: (510)832-2852; fax: (510)832-2856; e-mail: [email protected]. 1882

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beneficial uses of the river include water contact and noncontact recreation, agricultural supply, and municipal and domestic use (1). California water quality regulations state that “waters of the state shall be regulated to attain the highest water quality which is reasonable, considering all demands being made and to be made on those waters and the total values involved, beneficial and detrimental, economic and social, tangible and intangible” (2). During the discharge permit renewal process, the California Regional Water Quality Control Board asserted that the winter season discharge “has a reasonable potential to cause a health risk for contact recreation in the San Joaquin River near the vicinity of the discharge” (3). This investigation was initiated to determine if the addition of tertiary treatment to the facility’s operation during the winter season would substantially reduce the risk the public faces via recreation in the 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. EPA considers water quality standards for freshwater to be protective if a state’s criteria are based on an illness rate equal to or less than 14 illnesses per 1000 recreation events (4, 5). Quantifying the public health risk associated with ingestion of waterborne pathogens is an evolving field. In the past, the majority of quantitative microbial risk assessment investigations were carried out by employing chemical style static models to calculate the probability of infection or disease resulting from an exposure event from specific pathogenic agents (6-10). That approach presumes that little net error is made by neglecting the processes unique to the transmission of infectious diseases, such as protection from infection (immunity) and the potential for subsequent person-to-person transmission of infection. Although modeling the transmission of infectious diseases as a dynamic process may require substantially more data than do static models, dynamic models can be configured to account for population dynamics (11) and protection from infection due to prior exposures (12). Nevertheless, few researchers have employed dynamic models to characterize the risk to human health associated with waterborne pathogens (11-15). In this investigation, existing hydraulic (16) and disease transmission models (14, 17, 18) were extended to characterize quantitatively the incremental benefit that tertiary treatment would provide over that provided by secondary treatment during the winter season at the City of Stockton wastewater treatment facility. For this investigation, incremental benefit is defined as a relative comparison of viral gastroenteritis attributable to the treatment facility for the treatment scenarios investigated.

Experimental Methods The methodology for this investigation was to estimate via numerical simulation the difference in viral gastroenteritis in the population attributable to the wastewater treatment facility under various treatment conditions, based on a model enteric virus. Numerical adaptation of the hydraulic and disease transmission models required extensive data collection and numerical implementation efforts. Link between Water Quality and Public Health. One important aspect of this investigation was to identify an appropriate link between water quality data and risk to public health. Based on research conducted over the last 20 years, it is reasonable to infer that the primary risk from recreational exposure to waterborne pathogens is associated with gastroenteritis from viral contamination (19-25). Recreation in fecally contaminated receiving waters may also cause other 10.1021/es025774p CCC: $25.00

 2003 American Chemical Society Published on Web 03/22/2003

FIGURE 1. Process flow schematic for the City of Stockton Treatment Facility. adverse health outcomes such as acute febrile respiratory illness (26), general respiratory illness, ear infections (26), eye ailments, skin rashes (27), and other less common although more serious health outcomes. Although the cumulative risk faced by recreators is a function of all of the pathogens present in the receiving water and the potential subsequent health outcomes, this investigation in a manner consistent with State and federal regulatory guidelines focuses on the risk of gastroenteritis. The recovery and detection of animal viruses in water is technically difficult, time-consuming, and expensive, and further the epidemiologically important viruses are still difficult to reliably quantify in water (28). Bacterial indicator organisms are therefore often used as a surrogate measure of microbiological water quality in ambient waters. In fact, a number of investigations have found correlations between bacterial densities and gastroenteritis in recreational waters (24, 29-31), and U.S. EPA’s recommended water quality criteria for recreational waters are specified in terms of bacterial indicator densities (4, 5). The utility of bacterial organisms as quantitative indicators of viral pathogens in ambient waters has however been questioned (28, 32-39). Because the thrust of this investigation was to determine if incremental water treatment would result in a substantially reduced risk to public health, a numerical simulation study was undertaken focusing on enteric viruses as the etiological agent of interest and risk of gastroenteritis as the endpoint. In this assessment, a combination of conservative and realistic assumptions was employed so that the results of the assessment would be health protective yet practical (40). A model enteric virus was employed to characterize conservatively and representatively the risk to public health that may be associated with exposure to the epidemiologically important enteric viruses via recreational activities in the river. For the purposes of this assessment, it was assumed that the model virus possessed the clinical features of rotavirus, as rotavirus is the most infectious virus for which human dose response data are available (41). It was also assumed that coliphage that infect male (F+) Escherichia coli present equal to or greater environmental persistence as the various enteropathogenic viruses of public health concern (28, 42, 43). These coliphage will herein be referred to as MS coliphage. Sensitivity analysis was employed to examine how potentially important attributes of the model virus and the environmental exposure may impact the overall findings of the investigation. Field Data Collection. The field data collection included an exposure assessment, an assessment of virus inactivation in San Joaquin River water, and an estimation of virus removal efficiency at the treatment facility under secondary and tertiary treatment. The exposure assessment estimated the number of people recreating in the river in the vicinity of the discharge (44). Data based on aerial photographs and interviews with 23 state 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 distribution of recreational activity; (4) the increase in recreation during the weekends and holidays; (5) the baseline number of boats in the study area; (6) the overall spatial distribution of recreators in the study area; and (7) the size of the population that may participate in recreational activities. Virus inactivation experiments were carried out by BioVir laboratories (Benicia, California) in duplicate for MS coliphage and attenuated polio virus in San Joaquin River water. One-liter samples of river water were seeded with known concentrations of MS coliphage and poliovirus. One milliliter grab samples were subsequently collected from the 1 L samples over an 8-day period and were analyzed for polio virus and MS coliphage concentrations. Virus concentration was plotted versus time based on the experimental data, and virus inactivation rates were estimated as the time required for 90% inactivation (T90). Virus reduction across the treatment facility was investigated so that virus concentrations discharged to the river could be estimated. MS coliphages were employed in place of human enteric viruses for this component because they are present in large numbers in raw wastewater (28), are considerably less time-consuming and costly than enteric viruses to enumerate, and exhibit similar or greater resistance to conventional wastewater treatment as viruses of public health concern (28, 42, 43). Data collection was composed of 24-h composite samples of the influent wastewater (n ) 48), chlorinated secondary effluent (n ) 24), and chlorinated tertiary effluent (n ) 24). Microbiological analyses were carried out via a plaque assay methodology (EPA Method 1602, draft modified) by BioVir laboratories. The microbiological analyses were used to characterize the performance of the treatment facility during secondary and tertiary operations. Statistical distributions describing the expected concentrations of MS coliphage in the facility’s influent and chlorinated effluents were generated via the method of maximum likelihood using Crystal Ball software (Decisioneering, Inc.). Skewness was used as the significant criteria to identify appropriate distributional forms (45). Distributions describing the expected secondary and tertiary treatment efficiencies with respect to virus reduction were then generated using Monte Carlo techniques, as follows: 15 000 influent and effluent concentration values were generated from the distributions described above, treatment reduction efficiencies were computed from paired concentration values, and distributions of removal were fit to the computed treatment reduction efficiency data via the method of maximum likelihood. Removal efficiencies could not be computed directly from the experimental data, because the median detention time in the oxidation ponds is approximately 60 days, thus the field data could not be treated as paired data. Modeling Approach. Numerical modeling consisted of integrating two distinct simulation models: a hydraulic model (16, 46) and a disease transmission model (14, 17, 18). The hydraulic modeling was carried out to simulate river water VOL. 37, NO. 9, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 2. Disease transmission variables and parameters. quality near the discharge. A near field submodel simulated the facility discharge plume and computed the initial dilution of viruses discharged in the river. A far field submodel accounted for the dynamic variability in the receiving water by simulating the hydrodynamic and water quality conditions in the study area. The hydraulic model was successfully calibrated against surface elevations, a die mixing study, and observed physical water quality data (46). Seven years of hydrologic and climatic conditions were simulated (19881994) representing river flow and microbiological water quality for critically dry to above normal hydrologic conditions. Disease transmission modeling was carried out to estimate the community enteric virus disease incidence, as represented by the model virus. Although a variety of disease outcomes is associated with enteric viruses, gastroenteritis was the focus of this investigation. The disease transmission model development did include a component for more serious disease outcomes from exposure to viruses (Figure 2); however, sufficient quantitative data were not found to allow the characterization of these comparatively uncommon outcomes. Mathematically, the movement of the population between epidemiological states was modeled as a series of ordinary differential equations (47, 48). A list of the model parameters is presented in Table 1 along with a summary of the mathematical details describing the movement of the population from one time step to the next. A more detailed discussion of the model and examples illustrating the dynamic behavior of the model are provided in the Supporting Information. The model is composed of 5 state variables, 11 model parameters, and 3 intermediate parameters that are used to pass data to the hydraulic model and 1 input parameter that was used to accept data from the hydraulic model (Figure 2). State variables track the number of individuals in each of the relevant epidemiological states: X - individuals susceptible to infection; Y - infectious but asymptomatic individuals; D - infectious and symptomatic individuals; C - postsymptomatic and infectious individuals; and Z - postsymptomatic and noninfectious individuals (with partial protection from infection). The 11 model parameters define the rate of movement of individuals between epidemiological states, the endemic transmission rate of viruses in the community, and the infection rate in the community due to ingestion of viruses from the San Joaquin River. 1884

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To define the 11 model parameters, 25 pieces of data were required. Therefore, sampling parameters were established (Table 2), each with bounds sufficient to account for the variability and uncertainty of values identified through an extensive literature survey and/or site specific data. The most important health protective assumptions employed in assigning parameter ranges were that all participants in recreational activities ingested river water, the model virus exhibited the clinical features including infectivity of rotavirus and environmental persistence and treatment resistance similar to MS coliphage, and microbiological data reported below detectable limits were assumed to be present at that limit. Simulation Approach. Exposure to enteric viruses was assumed to occur from the following sources: exposure to viruses through every day activities not including recreation in the river (background), exposure to viruses through recreation in the river from all sources except the treatment facility, and exposure to viruses through recreation in the river attributable to the treatment facility when operating in secondary and tertiary modes. To account for this range of exposures, four scenarios were simulated numerically (exposure conditions): (1) background, (2) background plus recreation with no treatment plant discharge, (3) background, recreation, and secondary effluent discharge, and (4) background, recreation, and tertiary effluent discharge. One thousand simulations, each representing a 6-month time period were carried out by randomly sampling the parameter distributions (Table 2). During each simulation, each of the exposure conditions described above was run. The disease transmission model tracked the number of individuals in each epidemiological state, the total number of recreation events, and the incidence of gastroenteritis (total number of people entering state D). Output from this set of simulations is the difference in risk attributable to the discharge per recreation event under the two treatment conditions. In this investigation, risk attributable to the discharge per recreation event is defined as the risk of gastroenteritis per recreation event with the treatment facility discharging (exposure scenarios 3 and 4, above) minus the risk per recreation event with no discharge (scenario 2, above). Risk per recreation event is defined as the total number of estimated cases of gastroenteritis divided by the number of recreation events during the simulation period.

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

a

Sampled parameters are defined in Table 2.

Sensitivity Analysis. Univariate sensitivity analyses were carried out to investigate how the results from the modeling were impacted by the input parameter values and to determine which parameters most strongly influenced disease incidence in the population under study. The methodology employed was to search for important trends in the data by plotting the predicted gastroenteritis incidence against the sampled parameter values.

Results Discussed below are the results for this investigation including the exposure assessment results, virus inactivation results, virus removal efficiency results, simulation results, and sensitivity analyses. Exposure Assessment Results. The field data were integrated 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. The exposure variables shown in Table 2 scale the exposure level relative to a weekday in October: m - increased exposure during different months; d - increased exposure during weekends; h - increased exposure during holidays; pb number of people on boats; hour - exposure over the course of a day (described as a modified triangular distribution with maximum 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 susceptible state (assumed to be equal to the estimated total population for the study area). Exposure assessment data are used to generate time series profiles of recreational activities in the river. A representative exposure profile is presented in Figure 3. Virus Inactivation Results. The virus inactivation experiment resulted in a total of 10 analyses. The virus inactivation

FIGURE 3. Number of recreators during a representative simulation, May-October 1993. rates calculated from these results corresponded to T90 values of 0.75 days for attenuated polio virus and 3.2 days for MS coliphage in San Joaquin River water. These results seem to be consistent with those reported by Sinton et al. (49) for bacteriophage survival in natural waters. Virus Removal Efficiency Results. The microbiological monitoring of the treatment facility found 20 of 24 secondary effluent samples and nine of 24 tertiary effluent samples to be below the method detection limit of 0.05 plaque forming units per mL (Figure 4). This counterintuitive result is likely due to the fact that the influent coliphage levels were lower by a factor of approximately 10 during the time period that the facility was operating in secondary mode. Based on the observed data, the MS coliphage removal efficiency of the treatment facility was approximated as a normal distribution in log units, where log reduction is defined as base 10 logarithm of the influent divided by the effluent concentration. The expected mean removal efficiency of the facility when operating with tertiary treatment is 4.56 logs with a VOL. 37, NO. 9, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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TABLE 2. Summary of Parameters Sampled during Monte Carlo Simulations

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parameter type biological

description

rangea

FT FP γ

incubation period fraction of state Y that moves to state D rate of movement from state Z to state X

σ  δ

h-1 h-1 h-1

2.9e-6-4.0e-6

h-1

5.7e-7-1.7e-6 1e5-1e10 (log-uniform) 1.5e-6-4.6e-6 0.3-2.3 0.15-0.42 0.18-0.3 1e-3-1.0 (log-uniform)

h-1 virus/h

βo βBp RBp ζ epratio

rate of movement from state D to state C rate of movement from state C to state Z fraction in state D who die due to disease per day rate of migration of new susceptibles into population death rate - other causes rate of virus shedding per infectious swimmer background transmission rate dose response parameter dose response parameter rate of virus die-off ratio of enteric virus to bacteriophage

24-72 0.1-0.4 3.8e-5-1 (log-uniform) 3.8e-3-2.1 e-2 6.0e-3-2.0e-3 0

Xo βI baseline pb

initial number of individuals in state X rate of recreational water ingestion baseline number of exposed individuals number of people per boat

m

exposure factor by month

d

weekend exposure factor

h

holiday exposure factor

hour

hourly (interday) exposure factor

CE

bacteriophage concentration in raw wastewater tertiary operation coliphage reduction secondary operation coliphage reduction existing effluent flow potential future effluent flow

parameter

a υ λf

community

water quality

TE -3° TE-2° Q

units hours percent h-1

h-1 unitless unitless day-1 unitless

basis

reference

1-3 day incubation 10-40% infected develop symptoms 0-3 yr duration of immunity, accounts for no immunity to reported immunity 2-11 day duration of symptoms 1-3 week duration of shedding 0% U.S fatality rate insufficient data for other outcomes global birth rate

(54-56) (57, 58) (59, 60)

global death rate site specific data based on ambient wq and hydraulic modeling 1-4% seroconversion per year MLE 95% confidence interval MLE 95% confidence interval T90 of 3-5 days, site specific, see text conservative range for reported and site specific data

(65) (44)

(61, 62) (63, 64) (65)

(44, 66, 67) (9, 63) (9, 63) (44) (42, 44, 68-75)

2.8e6 0-0.05 26-263 1-4 (weekdays) 1-5 (weekends) 1.14-2.64 (May) 1.5-3.16 (June) 1.5-3.32 (July) 1.5-3.89 (Aug) 1.14-2.3 (Sept) 1 (weekdays) 2-5 (weekends) 1 (nonholiday) 2-3 (holiday) 0-1b

individuals L/h individuals individuals/ boat unitless

site specific data, see text 0-50 mL/hour site specific data, see text site specific data see text site specific data (note: Oct-April assumed ) 1.0) see text

(44) (76) (44) (44)

unitless

site specific data see text site specific data see text site specific data see text

(44)

1e4-5e4

pfu/100 mL

(44)

N(4.6, 0.75)c

log reduction

N(4.18, 0.39)c

log reduction

1.0e8-1.5e8 2.1 e8

L/day

site specific data see text site specific data see text site specific data see text site specific data see text

unitless unitless

(44)

(44) (44)

(53) (53) (44)

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.

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

FIGURE 5. Simulation results: number of cases of viral gastroenteritis for a 6-month time period under four exposure scenarios. standard deviation of 0.76 logs, and the expected mean removal efficiency when the facility is operating with secondary treatment is 4.17 logs with a standard deviation of 0.39 logs. Based on the proportion of data below detectable limits, these removal distributions likely underestimate the true removal effectiveness and variability of treatment. Simulation Results. Representative output from the disease transmission model is presented in boxplot format in Figure 5 for simulations in which tertiary treatment was employed with effluent flow of 1.0e8-1.5e8 L/d (existing operations). The results presented in Figure 5 represent the number of gastroenteritis cases during a 6-month period attributable 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 cases in the community that occurred due to background exposure was approximately 1000 times higher than those in the community attributable to recreation in the river with or without the discharge. This result is likely due to the fact that a small proportion of the population was recreating compared to the total size of the population. Also of note are the upper tails of the distributions presented in Figure 5. These comparatively high levels of viral gastroenteritis generally occurred 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 virus particles shed by recreators would lead to decreased variability in the simulation results.

FIGURE 6. Simulation results: relation between viral gastroenteritis and shedding. The rate of risk that EPA considers acceptable for recreation in freshwater is approximately 0.014 illnesses per recreation event. Using that metric for comparison, the risk of viral gastroenteritis attributable to the discharge per recreation event is illustrated in Figures 7 and 8 which present the relation between treatment efficiency and risk attributable to the treatment facility per recreation event for existing flows during critically dry hydrologic conditions with tertiary and secondary treatment, respectively. As illustrated, the marginal risk attributable to the facility from recreating in the river decreases as treatment efficiency improves. Cumulative probability plots for the risk attributable to the discharge per recreation event are presented for tertiary and secondary treatment with flows of 1.0e8-1.5e8 L/d in Figure 9. Simulation results for flows of 2.0e8 L/d are presented in the Supporting Information. Based on the simulation results, the median level of risk attributable to the discharge per recreation event for the conditions investigated ranges between 2 × 10-6 and 6 × 10-6 (Table 3). Further, 95th percentile values ranged between 2 × 10-4 and 6 × 10-4. Interestingly, the predicted 95th percentile value for tertiary treatment at a flow of 2.0e8 L/d was slightly higher than that for secondary treatment. This unexpected result most likely derives from the fact that the observed variability in tertiary treatment was greater than that for secondary treatment and appears to be an artifact of assuming that the microbiological data reported below the detection limit are present at that limit. Based on the analysis presented herein and summarized in Table 3, the VOL. 37, NO. 9, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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

is 4 or less avoided cases per 100 000 recreation events at the 95th percentile level. Similar results were obtained for above normal hydrologic conditions (not shown). Sensitivity Analysis Results. Graphical analyses were carried out to investigate how sensitive the results were to the input parameter values and which parameters most strongly influenced the predicted incidence of viral gastroenteritis. Figures showing representative results of the sensitivity analyses are presented in the Supporting Information. The two parameters most strongly influencing the background level of viral gastroenteritis incidence in the community are the percent of infected individuals that develop symptoms (FP) and the background transmission rate (Β0). Other parameters did not substantially impact the background level of viral gastroenteritis incidence. The parameter that appears to most strongly influence the level of viral gastroenteritis incidence in the community attributable to recreation is the rate of virus shedding per infectious swimmer (λF). Treatment efficiency was the only parameter to substantially impact the viral gastroenteritis incidence attributable to the treatment facility (Figures 7 and 8), although the ratio between the model virus and MS coliphage did impact the viral gastroenteritis incidence at the upper end of the sampled range.

Discussion

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

FIGURE 9. Cumulative probability plot for risk of viral gastroenteritis attributable to discharge per swim event for tertiary and secondary treatment with 1.0e8 to 1.5e8 L/d flows. median reduction of viral gastroenteritis from tertiary treatment during the winter season is estimated to be on the order of 1 avoided case per 1 000 000 recreation events and 1888

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In this investigation, a wide array of disparate data was integrated to address the complex problem of assessing risk from exposure to recreational water with different degrees of wastewater treatment. The benefit to public health of additional treatment was evaluated with respect to a model enteric virus and was expressed as the relative risk associated with the two treatment conditions. The endpoint investigated was gastroenteritis. The interpretation of the results presented in the previous section should be tempered by the scope of the investigation, the inherent uncertainty in parameter values, and limitations of the field data collection efforts. The primary advantage of employing a simulation based methodology was the ability to evaluate the potential benefits associated with a proposed management option. It should however be understood that during any recreation event, an individual may be exposed to a number of different pathogens derived from a number of different sources. Although, it is not practical to estimate the cumulative risk from recreating via a simulation study such as this one, it is nevertheless feasible to frame an investigation in a manner such that practical risk management decisions may be considered. It is with this perspective that this investigation was designed and implemented. Issues related to the interpretation of the results of this investigation are discussed below. Comparison with U.S. EPA’s Criteria. The thrust of this investigation was to characterize the relative public health benefit associated with additional wastewater treatment rather than to estimate the cumulative risk associated with recreating in the river. Nevertheless, comparing the relative level of viral gastroenteritis attributable to the treatment facility to that considered acceptable by EPA for recreation in freshwater provides a valuable perspective on the results presented herein. Specifically, given the conservative assumptions employed in this investigation, it is reasonable to infer that the risk of viral gastroenteritis from recreation in the river attributable to current operations at the treatment facility is considerably below the risk level considered acceptable by U.S. EPA. Winter tertiary treatment would further reduce the existing risk of viral gastroenteritis attributable to the treatment facility by approximately 1550%. Variability and Uncertainty. From the sensitivity analyses, it was found that the two parameters most strongly related to the incidence of gastroenteritis attributable to recreating

TABLE 3. Incremental Benefit of Tertiary Operation of the Treatment Facility during the Winter Season with Respect to Recreational Exposures climatic conditions

flow

treatment

treatment efficiency distribution

median riska

incremental benefitb

95th %ile riska

incremental benefitc,d

critically dry

1.0e8-1.5e8 L/d

tertiary secondary tertiary secondary

N(4.6, 0.75)e N(4.18,0.39)e N(4.6,0.75)e N(4.18,0.39)e

2E-06 4E-06 5E-06 6E-06

2E-06

2E-04 3E-04 6E-04 4E-04

4E-05

2.0e8 L/d

1E-06

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 benefit at 2.0e8L/d. e Normal distribution with mean equal to first value in parentheses, and standard deviation equal to second value.

in the river under current conditions are the rate of virus shedding from swimmers and the virus removal efficiency of the treatment facility. Although a substantial effort was undertaken to characterize the treatment efficiency of the facility, the accuracy of that effort was limited because some of the effluent concentrations under both conditions were reported to be below detectable limits. The distributions of treatment efficiency reported herein likely underpredict the true treatment efficiencies of secondary and tertiary treatment at the facility. The relative difference in treatment efficiency between secondary and tertiary treatment reported herein for MS coliphage is slightly less than that reported previously in the literature for both poliovirus and MS coliphage (45, 50), possibly due to the observations reported below detectable limits. The uncertainty in relative effectiveness of treatment between the conditions studied does not, however, preclude gaining insight from the investigation when considered in conjunction with the simulation results. Given that the effectiveness of treatment is based on assuming that data below the detection limit are present at that limit, the risk reported herein for current winter operation represents a reasonable upper bound. Reducing the uncertainty in treatment effectiveness could refine the associated estimate of relative risk, but given the estimated magnitude of risk attributable to the treatment facility relative to that considered acceptable by EPA, it seems unlikely that reducing the uncertainty in treatment efficiency would substantially alter the estimated benefits of tertiary treatment relative to the levels of risk that are currently considered tolerable for recreational activities in freshwaters. Similarly, uncertainty in the rate of viral shedding by individual recreationists could potentially be decreased through further research; however, it seems unlikely that the relative results of this investigation would have been substantially altered had this model parameter been defined more narrowly. Another parameter with substantial uncertainty was the ratio of enteric virus to coliphage. In this investigation, the sampled values for this parameter encompassed limited site specific data and the range of data reported in the literature. The most appropriate range of values for this parameter is undoubtedly site specific. The uncertainty associated with the parameter range employed herein could be decreased through future research which could lead to less uncertain estimates of the public health risk associated with recreating in receiving waters. Similarly, if direct measurements of virus concentrations become available in the future, the associated estimates of risk to public health and the benefit of additional wastewater treatment using the approach demonstrated here will be improved. Limitations. Given the complexity of assessing the risk from exposure to recreational water, a number of methodological assumptions were required. The limitations of the analyses presented herein generally stem from those assumptions. One fundamental limitation is that the results are applicable only for parameter values within the range of those investigated. Although a substantial amount of care was taken to characterize the parameter values in a health

protective manner, it is possible that parameter values could be refined or changed based on future research. The health outcome associated with infection and disease in this investigation was gastroenteritis. There are a number of other more serious disease outcomes that are also associated with enteric viruses and characterizing the risk associated only with gastroenteritis likely underestimates the true cumulative risk to public health. Characterizing other endpoints more serious than gastroenteritis was beyond the scope of this investigation; however, the likelihood for such health outcomes are important, should be considered during the risk management process, and should be the focus of future research. A related limitation of this investigation is that microbial risk assessment methodologies do not characterize the cumulative risk associated with all pathogens potentially present in an environment. Indicator organisms have been used for years because of the inability to take a universally defensible agent-by-agent approach. The same problem carries over to risk assessment, which is inherently agent specific, in that it is not practical to carry out separate assessments for all pathogens that may be present. So we chose a variant of that theme and chose an outcome, viral gastroenteritis, and synthesized a model organism that captured the salient features of viruses commonly known to be in water and cause this outcome. The resulting analysis must be interpreted within this indicator context and extrapolation to other organisms, even viruses with dissimilar properties, must be done with caution. Finally, it should be noted that storm events and their associated urban runoff were not specifically modeled in this investigation. For the purposes of characterizing the incremental benefit of additional winter treatment it was assumed that recreation would be extremely limited during the winter season during a storm event in this location. Vulnerable or Highly Susceptible Subpopulations. Under the 1996 Amendments to the Safe Drinking Water Act, the U.S. EPA must consider susceptible subpopulations in its health risk assessments. With respect to microbial exposures, limited consideration has been given to the degree to which individuals may differ in the completeness of protection offered by their immune systems (51). In this investigation, an attempt was made to quantitatively characterize risk to highly susceptible or vulnerable subpopulations via recreational activities. However, sufficient data were not available to enable the characterization of risk for those subpopulations separately from the population at-large (52, 53). This finding is consistent with other recently published work (77). Risk Management. In this investigation, quantitative microbial risk assessment techniques were used to provide insight regarding the potential public health benefits associated with tertiary treatment over that provided by secondary treatment during the winter season at the City of Stockton, CA wastewater treatment facility. In summary, the pertinent risk management issue is balancing the public health benefit of additional treatment at this facility with the associated costs and deciding if the additional treatment is VOL. 37, NO. 9, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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warranted during the winter season. This investigation was carried out to provide risk managers with the technical information needed to consider this important issue. The methodologies described herein are applicable to other watersheds where the benefit of additional water treatment with respect to public health concerns must be addressed.

Acknowledgments Funding for this investigation was provided by the City of Stockton, Municipal Utilities Department. Jon Konnan, Roanne Ross, and Edmund Seto contributed substantially to the initial phase of the investigation. The authors thank Don Dodge, Tim Anderson, and Morris Allen for their support of this investigation and Robert Hultquist, Steven Book, Richard Sakaji, and Carl Lischeske for their constructive input and review. Finally, the authors acknowledge the anonymous reviewers for the critical review of the manuscript.

Supporting Information Available Description of and output from disease transmission model (Appendix S1) and representative resuslts from sensitivity analysis (Appendix S2). This material is available free of charge via the Internet at http://pubs.acs.org.

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Received for review May 8, 2002. Revised manuscript received January 3, 2003. Accepted February 4, 2003. ES025774P

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