Characterization of prediction uncertainty for the full PBPK/ PD model is difficult because there are both model form uncertainties as well as parameter uncertainties. For example, there was discussion relating to the situation where the compartment specific data suggest one value for a parameter ( e. g., dermal permeability coefficient), but the parameter value had to be dramatically changed from this data derived value in order to get a better global model fit. At least one Panel member felt that this is exactly the kind of situation that would justify the need to reexamine not just the particular assigned parameter value but also the form of the compartment model. Contradictions such as these lead to increased scientific understanding and better final models. This also brought up the issue of how new information is used to modify the model and/ or model parameters. There seems to be a need for protocols to help decide when new data improve on existing data. At least one Panel member suggested that human data are preferred over animal data, in vivo data preferred over in vitro data and that direct observations be preferred over derived values. Incoming new data also could be investigated with a series of models of increasing complexity ( again, in a manner similar to what is done in model building in multiple regressions).

Some Panel members belief that a combination of model simplification ( judicial use of Occam's Razor) and formal parameter estimation via traditional least squares or Bayesian methods ( e. g. formal true Bayesian or hierarchical Bayesian updating techniques, Gelman et al 1996; Bois, 2000) will result in a model whose predictions are easier to evaluate. Many would appreciate seeing how various model simplification approaches would affect the final model fit, e. g. stripping off parts of a model, combining compartments or eliminating compartments.

For a given model formulation and where possible, parameter estimates should be accompanied by estimated standard errors and model predictions presented with associated prediction intervals. It was noted that the confidence intervals presented in Table C2 ( p. 103) and illustrated in Figure C1 ( p. 102) ( draft document " Assessment of carbaryl exposure following turf application using a physiologically based pharmacokinetic model") are confusing; reporting lower limits that are negative and plotting only the upper limits. It was not clear if one or two sided intervals are used, nor was it clear which are desired.

Also, it was noticed that much of the data used for parameter estimation is censored in some way. Traditional least squares methodology is not directly applicable in this case, and how censored values are handled can have a dramatic effect on estimates 58 of 113 and overall model fit. The more formal statistical estimation approaches may require censored data likelihoods or weighted likelihoods which are quite difficult to handle properly. The problem is multiplied when one is attempting to handle multiple datasets in the process.

Goodness of fit Goodness of fit of the model is highly related to the complexity of the model and parameter estimation. For this reason, the discussion on goodness of fit was interspersed throughout the Panel deliberations.

The applicability of statistical goodness of fit methods to assess the adequacy of model predictions is limited by the complexity of the PBPK/ PD model, the type of data, the limited amount of data available to fit the model, and the aims of the modeling. Several views were expressed among the Panel members on the degree to which statistical approaches can and should be used.

Some Panel members preferred a more qualitative assessment of model goodnessof fit, offering that adequacy might be judged by answers to the following questions.

  Does the model capture the main points of the biological processes?   Does the model adequately describe available outcomes data for important dose levels and time ranges?   What can be learned by fitting the model to each of the available data sets?   Which changes to model parameters are needed to improve the fit in a specific key compartment and do these changes result in deterioration of the fit in other compartments?

A number of suggestions were made for more quantitative ways to assess model goodness of fit.

  If least squares methodology is used to estimate model parameters, the residual mean square can be used to assess model fit. Modifications to this approach include the Akaike Information Criterion ( AIC) which joins the estimated mean square error to the number of parameters actually estimated in the model.   Evaluate model complexity by building a set of nested sub models by systematically removing or simplifying compartments. Again, residual mean squares, AIC or other criterion can then be compared, either informally or via formal statistical tests where available, to assess the significance of differences between full and reduced models.   Implement dynamic graphics which would allow visualization of the changes in model fit produced when one or a small number of parameters change their values continuously ( movie type visualization of model sensitivity). 59 of 113   Utilize some form of the Press statistic ( ( ) 2 Pr   =

ess i i i g y y ) to evaluate the goodness of model predictions to data not used to calibrate the model.   Utilize other criteria, such as the minimum absolute distance between observed and predicted values ( MAD,   =

MAD i i i g y y ), or curve comparison statistics like an Anderson Darling distance to measure goodness of fit.   Consider weighting some times more than others in the goodness of fit criteria and evaluate how this changes model fits ( e. g., a weighted Press statistic, ( ) 2   =

WP i i i i g w y y ).

  If a likelihood function can be created, then likelihood goodness of fit statistics and associated tests become available.

Establishing a quantitative criterion for assessing level of model fit is important in determining at what point one achieves acceptable model fit. What is needed to set this value is the sampling distribution of the goodness of fit statistic. It is unlikely that this will be analytically available and hence some form of Monte Carlo simulation study will be needed to develop this distribution. Goodness of fit statistics are available to assess not just the overall goodness of fit of the model to observed outcomes, but can be used to identify where lack of fit occurs e. g. does the model underestimate brain concentrations, overestimate blood levels, etc.

The entire discussion by the Panel emphasized the need in this study to identify the critical end points for the model. It was not clear whether it was more important for the model to correctly predict peak concentrations, times at which peak concentrations were reached, peak or times for specific compartments ( brain or blood) or some other aggregate measure of prediction fit ( as mentioned above). Some Panel members were not certain that fits on the temporal scale were best. Other Panel members were concerned with whether model fits should be made on untransformed concentration values or log transformed concentrations. Model fits to untransformed concentrations tends to allow large deviations, typically at higher concentrations to dominate the goodness of fit statistic. Assuming multiplicative errors and natural log scaling allows better fits for low doses and helps avoid situations where parameter estimates are inappropriately estimated as negative.

There was some discussion about choice of the settings for the human adult model. Typically such models are designed for 20 year old males assuming 6% body fat. The Agency may wish to utilize NHANES data or the findings of the Price et al ( 2003) study to define a more realistic typical individual.

Sensitivity analysis The ERDEM PBPK/ PD model is a complex system that involves many 60 of 113 interdependent functional relationships. Estimates or assigned values for many parameters of this system have varying degrees of current empirical support and all are subject to a degree of natural variability as well as " reducible" uncertainty for specific applications. There was general consensus among the Panel of the need to perform a sensitivity analysis for the PBPK/ PD model. A limited sensitivity analysis was done but a more extensive analysis needs to be performed. In general, it was felt that there may be only a few parameters that correlate with model predictions. A common theme of the discussion was that, in general, researchers work very diligently to build the best model they are capable of doing and then maintain skepticism in the model outputs. A sensitivity analysis is one way this is done.

Sensitivity analyses typically include an assessment of the effect of parameter uncertainty on model predictions. This is typically assessed by applying prior distributions to parameters and assessing the degree to which changes in parameter values correlate with changes in model predictions. With highly parameterized models this is a very difficult task. But it was felt that a proper uncertainty analysis allows users of the model, and eventually regulatory assessors, the ability to place model predictions in their proper context.

In terms of propagating uncertainties to endpoint predictions, the EPA should put more attention to errors caused by systematic model misspecification in the PBPK/ PD model. This includes concerns with over parameterization. The uncertainty ( and variability) in model predictions depends heavily on the selected model. If the model is incorrect, depending on the degree of error, the uncertainty in the final model predictions also will be incorrect.

In assessing uncertainty, it seems reasonable to include terms and pathways in the model that may have small contributions to the overall uncertainty, simply because their inclusion usually doesn't make much difference and leaving them out begs the question of what effect they might have had. A process to evaluate reductions in the dimensionality and complexity of the model is needed to support calibration of the models. Some Panel members felt that the dimensionality of the model should be solely the responsibility of the model builder, but others would like to see an analysis of how model form changes affect predictions and changes sensitivity.

The Panel also discussed the idea of letting the model form itself be uncertain. For example, the model developer could generate a suite of models, all of which are acceptable but which illustrate the range of understanding of the form of the underlying processes and expert opinion on how compartments might interact. Some models might be highly defined, consisting of many compartments and parameters, whereas others could be very simple, based on purely empirical relations. It also may be possible to assign a weight to each model proportional to the likelihood that the particular model properly describes the PBPK/ PD system. As in a Monte Carlo sampling procedure, one could then generate model predictions of endpoints from each of the models, and from multiple samples of the 61 of 113 parameter uncertainties for the respective model. This would result in a collection of model predictions based on uncertainty in both parameters and model specification and the resulting prediction distributions or prediction bands would better convey overall uncertainty. This information complements the measures of goodness of fit resulting from formal parameter estimation.

Not all Panel members wanted to go quite as far as the full parameter and model uncertainty analysis discussed in the previous paragraph. One member preferred a wellstructured statistical analysis to a post hoc uncertainty analysis; allocating 90% effort to parameter estimation and only 10% to assessing uncertainty. Others felt that it was crucially important that the underlying uncertainty be assessed in explicit probabilistic simulations. One Panel member mentioned that a Bayesian Network approach ( Borsuk et al., 2001, 2004) might be useful but another Panel member stated that this approach has been examined and found unacceptable for use with dynamic system models such as these PBPK/ PD models.

There was discussion that advances in computational algorithms and computer technologies have eliminated many of the computational barriers to full uncertainty analysis. What is required is careful scientific specification of the model structure and its implied conditional, probabilistic relationships. Specification of prior probabilities for parameter values requires good empirical data or carefully elicited expert judgment. It does not remove the need to test how individual model components and changes in model specification will influence model outputs. Nor does it eliminate the need for sensitivity analyses to the model outputs.

Other related comments made during the Panel discussions include:

  The models discussed do not seem to explicitly incorporate inter individual variability.   The animal data typically used in calibration are aggregated and assumed homogeneous.   Model fits might be more useful if they are targeted at the midpoint or center of the response distribution rather than at the 99.9th percentile as seemed to be done in the report to the Panel.   The judgment of those model fits would be substantially improved if individual response values were included on plots and not only summary data ( means).   Related to the quality of the mathematical and statistical analysis is the capability of the interface between the model application and stand alone statistical analysis packages. The amount of data that can be created during a simulation run needs a robust statistical analysis package with modern tools of statistical diagnostics and regression methods.   After creating Figures 7 19, the report talks about typical or expected ranges for a subset of the rate parameters as summarized in Table 13 ( p. 48). It would have been informative to overlay the most extreme predicted curves onto one or more 62 of 113 of Figures 7 19. This would provide a " bound" on the prediction, allowing us to see if the observations at least fit within high low predicted patterns. This is not exactly a confidence band but something more than the " point estimate" presented in original graphs.

Issue 2.4: Risk Metric Historically, EPA has calculated margins of exposure ( MOE) in its risk assessments for the N methyl carbamate pesticides. These MOEs are calculated by dividing environmental exposure concentrations by a point of departure identified from toxicity studies. These points of departure are typically no observed adverseeffect levels ( NOAELs) or benchmark dose estimates ( BMDs). For the N methyl carbamates, these NOAELs or BMD estimates are generally based on peak cholinesterase inhibition. The use of PPBK/ PD models provides the opportunity to consider toxicological endpoints other than peak cholinesterase inhibition. Some potential toxicological endpoints include 1) peak concentration of the pesticide ( or key metabolite) at the site of action; 2) total pesticide ( or key metabolite) at the site of action over a period of time ( e. g. area under curve); 3) peak cholinesterase inhibition; 4) inhibition at or above a pre defined level of inhibition ( e. g. BMD10); 4) duration of time for inhibition at or above a pre defined level of inhibition. The current report explicitly provides the peak concentration of carbaryl and peak cholinesterase inhibition; although the other metrics are easily accessible from a model developed in the ERDEM platform.

Question 2.4 Given the toxicological characteristics of carbaryl and other N methyl carbamate pesticides, please comment on the degree to which these toxicological endpoints are appropriate for purposes of developing a risk assessment.

Panel Response PBPK/ PD modeling has the advantage over the conventional NOEL and MOE approach in incorporating the current understanding of mechanism of toxicity in describing the relationship between components of risk assessment, e. g., toxicity, exposure, and risk. The unique challenge of modeling the relationship between toxicity and exposure for carbaryl and other carbamate pesticides is that the pesticide and its active metabolite( s) are rapidly released after binding to cholinesterase.

The Panel generally agreed that, as far as enzyme binding is concerned, the inhibition of cholinesterase activity is the ultimate metric for expressing toxicity and risk. This could include both considerations of the peak inhibition level, the duration of sustained inhibition as expressed in the area under the curve ( AUC), and the duration 63 of 113 above a pre defined inhibition level. On the other hand, the profiles of carbamate and its active metabolites at the site of action are pertinent metrics of exposure. These metrics are important for improving the understanding of both short and long term exposures ( i. e., prolonged duration or repeated exposures) and can provide a linkage to the biological effects and the biomonitoring data in humans.

Thus, all of the five metrics suggested by the Agency are of interest and each of them can potentially be a valid metric for risk assessment, although some expressions may require further clarification. At this early stage of developing the PBPK/ PD model for carbaryl and possibly extending its application to other N methyl carbamates and their cumulative exposure, the Agency is encouraged to explore the merit of each of these endpoints. In fact, multiple expressions of some of these metrics can enhance a logical and clear presentation of risk assessment and facilitate the understanding and communication between the exposure and risk assessors and between risk assessors and risk managers.

Specific comments for each metric are provided below with respect to their pertinence in risk assessment and consideration of time factor in exposure and toxicity.

1) peak concentration of the pesticide ( or key metabolite) at the site of action.

The peak concentrations of pesticide or its key metabolites at the site of action are among the primary variables presented by the ERDEM model. The concentration of the parent compound is the key expression of exposure for evaluating the toxicity of carbaryl. This and the next metric ( i. e., AUC) are the ultimate parameters of pharmacokinetic events after exposure, interfacing with the pharmacodynamic and toxicity metrics. When properly modeled, the peak concentration informs pathway saturation and the various degrees of probability of states of the carbaryl, e. g., free circulating, bound to cholinesterase, metabolized as released from the enzyme complex. The peak level metric also supplies information on the speed at which the pesticide is taken up, the time scale over which effects are likely to occur, and the maximum level of effect likely to occur.

The peak level also could serve as a pointer to compound specific toxicities that lie outside the common mechanism and is especially relevant in an acute setting. One reason is that short term adaptations reduce or eliminate physiological disturbances from minor to moderate levels of cholinesterase inhibition. When moderate levels of inhibition are sustained, several compensatory mechanisms come into play almost immediately, including reduction of acetylcholine release as well as receptor desensitization and, later on, down regulation. Synaptic homeostasis is quickly restored. Hence, the AUC ( see discussions under the next metric), is unlikely to be a good measure of acute toxicity.

2) total pesticide ( or key metabolite) at the site of action over a period of time ( e. g. area under curve) 64 of 113 The AUC metric includes the magnitude of effect implicitly, but also duration of effect. The concentration of the pesticide or its active metabolite in and of itself is irrelevant to the characterization of risk in isolation from the extent of enzyme inhibition. Thus, comments regarding this metric are not limited to the AUC of pesticide or its active metabolites but also the metric of AUC of cholinesterase inhibition.

An important consideration is that the compensatory responses that might be taken by the organism in response to perturbation in acetylcholine signal strength are not necessarily benign. If sustained adjustments are made in particular synapses in the postsynaptic sensitivity or the presynaptic extent of acetylcholine release, these may well have subtle long term consequences for the transmission of signals that are important parts of neuron/ neuron and neuron/ muscle communication. Thus, the toxicological response to long sustained exposures could be very different from the acute exposure scenarios. Total AUC of inhibition or total time above a pre defined level of inhibition ( metric 5) can be important. In these cases, compensatory mechanisms may fail, or the consequences of the compensations may themselves be adverse. Therefore, models developed for the ERDEM platform should calculate and report these metrics.

The different toxicological implications between the peak cholinesterase inhibition ( next metric) versus the AUC of inhibition are simply illustrated: in a poisoning event, 90% inhibition over 20 minutes is a more serious effect than 10% inhibition over 180 minutes. While the AUC metric would give the same results for the above two scenarios, the health outcome is likely to be substantially different. On the other hand, for non acute poisoning events, e. g., 10% inhibition, the integrated AUC approach would be appropriate. It may be, for example, that long term, low level cholinesterase inhibition gives rise to specific health outcomes of interest. Further, repeated assaults may offer additional effects. The scenarios outlined in the document presented to the Panel  adult applicator and child playing on treated turf offer examples that could give rise to similar integrated effects with entirely different profiles.

3) Peak cholinesterase inhibition As described above, peak inhibition of cholinesterase activity is the most important endpoint for evaluating acute toxicity with carbamate anticholinesterases. It is the most direct measure of adverse effect mediated through the common mechanism of toxicity for carbamates. Thus, for short term effects, peak inhibition is a good first judgment for the dosimeter that is most likely to be predictive of toxicity.

The expression of peak cholinesterase inhibition at a given dose is comparable to the current toxicity data that defines the threshold as the no observed effect level ( NOEL) based on the peak inhibition measured at the assumed peak time of effects. Thus, the PBPK/ PD model can be used to estimate the peak inhibition after human exposure and compared to the level defined as adverse when exceeded, i. e., threshold cholinesterase inhibition. Depending on the extent that a PBPK/ PD model may address the inter species 65 of 113 and inter individual variation in sensitivity ( e. g., two individuals exposed to identical level of carbaryl may show different levels of inhibition due to differential susceptibility), uncertainty factor( s) may be modeled or applied in this comparison.

The similarity of this metric to the conventional NOEL or BMD has the added advantage of facilitating risk communication while transitioning into the mechanistic approach. Modeling peak inhibition also allows the possibility of correlating this metric to other related neurological endpoints ( e. g., neurobehavioral and clinical signs).

An apparent unknown is what dosimeter is closest to a causal determinant of the subsequent pharmacodynamic processes   from overt symptoms in adults to putative marginal strengthening of signaling along some pathways rather than others during development to more subtle adaptations of the synaptic responsiveness to later cholinergic stimuli. This is where we really need progress in basic science to show how cells set and reset their set points for responsiveness in relation to the degree and duration of cholinesterase inhibition. Thus, for current assessments, both peak inhibition and the AUC ( the amount of inhibition times time) should be used as dose metrics for assessing MOE and risks of response.

4) Inhibition at or above a pre defined level of inhibition ( e. g. BMD10)

One Panel member commented that this metric requires some clarification. Simplistically, a PBPK/ PD model can define the dose that results in a pre defined peak level of cholinesterase inhibition, equivalent to the BMD ( e. g., 10% inhibition in the brain). In a conventional approach, this metric may then be used to generate the relative potency factor for acute toxicity of multiple carbamates; if it is determined that peak inhibition is the valid metric for expressing acute toxicity. The main drawback is that this expression does not implicitly account for the time factor as the next metric ( i. e., duration of time for inhibition at or above a pre defined level of inhibition). Instead, this metric suggests a dichotomous variable  exposure resulting in inhibition above a fixed level. Thus, this metric may be less useful than knowing how long such an inhibition occurred. Clearly there is a difference in inhibition above 10% that lasted for five seconds versus a similar inhibition, due to chronic exposure, that lasts for many days.

5) Duration of time for inhibition at or above a pre defined level of inhibition.

Being able to take into account the duration/ time factor is one satisfying feature of this metric. Assuming that the pre defined level is selected with some intrinsic meaning ( e. g., a health outcome of interest), the duration of the insult experienced by the body represents a significant measure. Short duration insults are likely to produce less effect than long term sustained insult of the same magnitude. The selection of this metric presupposes that a pre defined level is below some frank effect level. Otherwise, the effect can be defined by other means. Furthermore, the duration should be relative to the expected clearance rate. For example, it can be asked: is this elevated level maintained for 66 of 113 longer than the lifetime of carbaryl in the body and thus may be indicative of an ongoing, albeit low level, exposure? 67 of 113 REFERENCES Bois, F. ( 2000) Statistical Analysis of Fisher et al. PBPK Model of Trichloroethylene Kinetics. Env. Health Perspectives 8, Supplement 2: 275 282.

Borsuk, M. E., C. A. Stow, and K. H. Reckhow ( 2004). A Bayesian network of eutrophication models for synthesis, prediction, and uncertainty analysis. Ecological Modelling 173: 219 239.

Borsuk, M. E., C. A. Stow, D. Higdon, and K. H. Reckhow ( 2001). A Bayesian hierarchical model to predict benthic oxygen demand from organic matter loading in estuaries and coastal zones. Ecological Modelling 143: 165 181.

Gelman, A., F. Bois, and J. Jiang ( 1996). Physiological Pharmacokinetic Analysis Using Population Modeling and Informative Prior Distributions. J. of Am. Stat. Association 91( 436): 1400 1412.

Ginsberg, G. L., Pepelko, W. E., Goble, R. L., and Hattis, D. B. ( 1996). Comparison of Contact Site Cancer Potency Across Dose Routes: Case Study with Epichlorohydrin. Risk Analysis 16: 667 681.

Ginsberg, G., Hattis, D., Russ, A., and Sonawane, B. ( 2004). Physiologically based pharmacokinetic ( PBPK) modeling of caffeine and theophylline in neonates and adults: Implications for assessing children's risks from environmental agents. Journal of Toxicology and Environmental Health 67: 297 329.

Price, P. S., Connolly, R. B., Chaisson, C. F., Gross, E. A., Young, J. S., Mathis, E. T., Tedder, D. R. ( 2003). Modeling interindividual variation in physiological factors used in PBPK models of humans. Critical Reviews in Toxicology 33: 469 503.

Walker, K., Hattis, D., Russ, A., and Ginsberg, G. ( 2004). Physiologically Based Toxicokinetic Modeling for Acrylamide   Risk Implications of Polymorphisms and Developmental Changes in Selected Metabolic Enzymes. Report from the George Perkins Marsh Institute, Clark University, and the Connecticut Department of Public Health to the U. S. Environmental Protection Agency under Cooperative Agreement # 827195 0, December 2004. 68 of 113 SAP Minutes No. 2005 01 A Set of Scientific Issues Being Considered by the Environmental Protection Agency Regarding:

N METHYL CARBAMATE CUMULATIVE RISK ASSESSMENT: PILOT CUMULATIVE ANALYSIS SESSION 3: DRINKING WATER EXPOSURE ANALYSIS February 17, 2005 FIFRA Scientific Advisory Panel Meeting held at the Holiday Inn National Airport Arlington, VA Mr. Joseph E. Bailey Steven G. Heeringa, Ph. D. Designated Federal Official FIFRA SAP Session Chair FIFRA Scientific Advisory Panel FIFRA Scientific Advisory Panel Date: April 15, 2005 Date: April 15, 2005 69 of 113 Federal Insecticide, Fungicide and Rodenticide Act Scientific Advisory Panel Meeting February 17, 2005 N methyl Carbamate Cumulative Risk Assessment: Pilot Cumulative Analysis Session 3: Drinking Water Exposure Analysis PARTICIPANTS FIFRA SAP Session Chair Steven G. Heeringa, Ph. D., Research Scientist & Director for Statistical Design, Institute for Social Research, University of Michigan, Ann Arbor, MI Designated Federal Official Joseph E. Bailey, FIFRA Scientific Advisory Panel Staff, Office of Science Coordination and Policy, EPA FIFRA Scientific Advisory Panel Members Stuart Handwerger, M. D., Professor of Pediatrics, University of Cincinnati Children's Hospital Medical Center, Cincinnati, OH Gary E. Isom, Ph. D., Professor of Toxicology, School of Pharmacy & Pharmacal Sciences, Purdue University, West Lafayette, IN Kenneth M. Portier, Ph. D., Associate Professor, Statistics, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL FQPA Science Review Board Members:

John Adgate, Ph. D., Assistant Professor, Division of Environmental Health Sciences, University of Minnesota, School of Public Health, Minneapolis, MN George B. Corcoran, Ph. D., Professor & Chairman, Department of Pharmaceutical Sciences, Eugene Applebaum College of Pharmacy & Health Sciences, Wayne State University, Detroit, MI Lutz Edler, Ph. D., Head, Biostatistics Unit C060, German Cancer Research Center, Heidelberg, Germany 70 of 113 Bernard Engel, Ph. D., Professor, Agricultural & Biological Engineering, Purdue University, West Lafayette, IN Scott Ferson, Ph. D., Senior Scientist, Applied Biomathematics, Setauket, NY Lawrence J. Fischer, Ph. D., Director, Center for Integrative Toxicology, Michigan State University, East Lansing, MI Natalie Freeman, Ph. D., Associate Professor, Department of Physiological Sciences, College of Veterinary Medicine, University of Florida, Gainesville, FL James P. Kehrer, Ph. D., Director, Center for Molecular & Cellular Toxicology, College of Pharmacy, The University of Texas at Austin, Austin, TX Chensheng Lu, Ph. D., Assistant Professor, Department of Environmental & Occupational Health, Rollins School of Public Health, Emory University, Atlanta, GA Peter D. M. Macdonald, D. Phil., Professor of Mathematics & Statistics, McMaster University, Hamilton, Ontario, Canada David MacIntosh, Sc. D., Senior Associate, Environmental Health & Engineering, Inc., Newton, MA Robert W. Malone, Ph. D., Agricultural Engineer, USDA ARS, National Soil Tilth Laboratory, Ames, IA Christopher J. Portier, Ph. D., Director, Environmental Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, NC Nu may Ruby Reed, Ph. D., D. A. B. T., Staff Toxicologist, Department of Pesticide Regulation, California Environmental Protection Agency, Sacramento, CA P. Barry Ryan, Ph. D., Professor, Environmental & Occupational Health, Rollins School of Public Health, Emory University, Atlanta, GA Michael D. Sohn, Ph. D., Scientist, Environmental Energy Technologies Division, Lawrence Berkeley National Laboratory, University of California, Berkeley, CA Tammo S. Steenhuis, Ph. D., Professor of Watershed Management, Cornell University, Ithaca, NY Michael D. Wheeler, Ph. D., Assistant Professor, Departments of Pharmacology & Medicine, University of North Carolina, Skipper Bowles Center for Alcohol Studies, Chapel Hill, NC 71 of 113 INTRODUCTION In Session 3 of this meeting, the FIFRA SAP met to consider and review the N methyl carbamate pesticide cumulative risk assessment: pilot cumulative analysis, specifically issues related to drinking water exposure assessment. OPP solicited comment from the SAP on the use of existing ground water models to provide a pilot ground water exposure assessment for the N methyl carbamate pesticides. Session 3 included presentations by Mr. Nelson Thurman and Dr. Dirk Young ( Environmental Fate and Effects Division, Office of Pesticide Programs) and Dr. Tom Nolan ( U. S. Geological Survey) pertaining to the use of ground water exposure models and transport models to help predict drinking water exposure estimates for the N methyl carbamate pesticides. Dr. Steven Bradbury ( Director, Environmental Fate and Effects Division, Office of Pesticide Programs) gave opening remarks for Session 3. 72 of 113 SUMMARY OF PANEL DISCUSSION AND RECOMMENDATIONS The Agency solicited comments from the Panel on assessing regional groundwater vulnerability and predicting pesticide concentrations in groundwater for estimating exposures to N methyl carbamate pesticides.

Data that can be used to assess nationwide groundwater vulnerability are quite limited. Therefore an obvious approach for assessing groundwater vulnerability to pesticides does not exist. EPA proposes to use a shallow groundwater vulnerability assessment that was developed for nitrates. However, the nitrate assessment may not be appropriate for estimating shallow groundwater vulnerability to pesticides. If the proposed approach is to be used, it should be compared with observed groundwater contamination data. Alternatively, an approach utilizing the nationwide State Soil Geographic ( STATSGO) Data Base and the pesticide leaching models should be considered.

To predict concentrations of N methyl carbamate pesticides in groundwater, the proposed approach appears to be conservative and health protective for most reasonable scenarios. In all but the most unusual cases, screens in the wells will draw from a large cross section of the aquifer, perhaps some depth below the groundwater table. This water will be more dilute than the concentration of leachate in the vadose zone, and additional degradation may occur before people drink the groundwater. Also, the level of detail incorporated in the model and the modeling decisions is consistent with or a bit more conservative than the assessments for other routes of cumulative exposure to people.

The Office of Pesticide Programs ( OPP) presented illustrative applications of three groundwater models  Root Zone Water Quality Model ( RZWQM), Pesticide Root Zone Model ( PRZM), and Leaching Estimation and CHemistry Pesticides model ( LEACHP)  that it proposes to use. The three groundwater models are very similar. However, in response to specific questions brought to the Panel, important considerations include the following:

  Management practices and pesticide application. The models must be able to address the management practices and pesticide application practices ( e. g., pesticide incorporation, management practices that impact hydrology) that are important to N methyl carbamate and other pesticide movement to shallow groundwater. Urban uses of N methyl carbamate pesticides could be important, but it is unclear whether the models proposed could address such uses.   Degradation product formation. The model( s) should simulate formation and movement of degradation products if they are found to significantly contribute to risk. 73 of 113 Preferential flow. Macropore or preferential flow may be essential to accurately predict transport of N methyl carbamate pesticides and other pesticides to shallow groundwater.   Hydrology and rainfall characteristics. Most models predict the water balance accurately. Correctly characterizing rainfall amount and intensity may be especially important when applying RZWQM, because it will not simulate macropore flow unless the rainfall is sufficiently intense.   Pesticide fate. Some of the more sophisticated pesticide fate routines in models should possibly be avoided. Both the pesticide degradation rate and the adsorption strength are highly variable between fields and may explain the poor performance of the models during evaluation efforts.   Tile drainage. If N methyl carbamate pesticides are applied to areas that are tile drained, the selected model should simulate tile drainage for accurate loading estimates to surface water. However, tile drainage consideration may not be necessary in the model for the purposes of the cumulative risk assessment, especially if this is a screening level tool.   Model calibration, sensitivity, testing, and ease of use. Model calibration may be necessary. If so, the ability to easily calibrate the model will be important. Other important considerations include the transparency in the model's calculations, the data requirements, post analysis of the model output, internal documentation of code, ability to modify code, ability to interface the code with third party graphical user interfaces ( GUI), and ability to link to statistical analysis packages and geographical information system ( GIS) packages.

Surface water and groundwater behave quite differently. Generally, groundwater travels slowly and thus spatial averaging is a poor assumption. The pesticide concentration in each well depends on its immediate surroundings and cannot be represented by an average concentration. Because wells have screens allowing water into them, temporal averaging should be considered.

To account for persistence of pesticides in groundwater, the background levels could be estimated with models, and decline in residues over time ( estimated based on long term trends in monitoring) should be considered. The background document clearly identified that carbamate transformation can be pH specific. Also, the transformations of some metabolites may be affected by concentration and anaerobic conditions. 74 of 113 PANEL DELIBERATIONS AND RESPONSE TO THE CHARGE The specific issues to be addressed by the Panel are keyed to the Agency's background documents, references and charge questions.

Question 1 Selection of regional vulnerable ground water sites:

In both the organophosphate ( OP) cumulative assessment and this carbamate cumulative assessment, OPP identified regional drinking water exposure sites for surface water sources of drinking water that would represent one of the more vulnerable surface watersheds in each region. The process, which was deemed a valid approach by the 2002 SAP ( USEPA, 2002), identified those areas where areas of high combined cumulative pesticide use coincided with drinking water sources which were particularly vulnerable to runoff. This served as a regional screening assessment in that if the regional cumulative risk assessment finds that exposure in water is not a significant contributor to the overall exposure in that area, it will not be a significant contributor in other areas in the region.

For ground water sources of drinking water, OPP is proposing to use a similar approach ( described in Section C. 2. of the Drinking Water section of the case study).

National coverages of the vulnerability of aquifers to pesticide contamination are limited in availability, and generally refer to vulnerability of the overlying soils and surficial geology to leaching. Does the SAP believe that the sources on relative ground water vulnerability identified in the case study provide an adequate screening level assessment of the potential for contamination of shallow aquifers to pesticide contamination? If these vulnerability assumptions are inadequate, what can be done to improve the approach?

Panel Response In the oral presentation, EPA staff indicated that the vulnerability assessment conducted for groundwater to date was not as systematic as that done for surface water. The Panel agreed that a more systematic assessment would be desirable.

As indicated by the EPA, the national coverages of the vulnerability of aquifers to pesticide contamination and related data are limited. The vulnerability assessments that are available are limited in the types of data considered in their creation and in the scales of the data used ( e. g., the nationwide DRASTIC assessments). There are some state and 75 of 113 regional groundwater vulnerability assessments, such as those conducted in Indiana ( Cooper et al., 1997). The results of these regional assessments point out the importance of the scale of underlying data used in their creation ( Cooper et al., 1997). The DRASTIC groundwater vulnerability estimates for Indiana created with 1: 250,000 scale GIS data resulted in significantly different estimates of vulnerability than those created using courser nationally derived data. Further, Cooper et al. ( 1997) demonstrated the value of testing the vulnerability maps with observed groundwater quality data. Therefore, there are challenges in identification of groundwater vulnerability nationally.

To assess groundwater vulnerability, EPA used the method of Nolan et al. ( 2002) and three criteria to identify the most vulnerable regional drinking water exposure sites. The first two criteria were areas where carbamate pesticides are applied and areas where groundwater is used for drinking. The third criterion was to identify the areas most vulnerable to groundwater contamination.

The vulnerability assessment approach does not address off label and improper uses of pesticides. Agricultural pesticide applications might generally show fairly high adherence to prescribed uses and application rates, but residential applications might show considerably less fidelity to label uses. One of the pesticides being considered has residential applications. It is conceivable that EPA's presumption that usage follows label guidance might seriously underestimate vulnerabilities in some suburban areas.

The Nolan et al. ( 2002) method to assess groundwater vulnerability was developed for nitrate, which may transport through the vadose zone or an aquifer differently than pesticides. For example, vulnerable areas for pesticides may largely be a function of soil organic carbon and macropore flow, which may not be as large a factor for nitrate transport to shallow groundwater. The method used by Nolan et al. ( 2002) also considers human population which may be unique to groundwater vulnerability to nitrate. The model coefficient associated with the depth to groundwater also may have the wrong sign on it in this model if it is used for N methyl carbamate pesticides. Therefore, areas may be vulnerable to pesticide leaching to shallow groundwater that are not identified by the Nolan et al. ( 2002) approach; the vulnerability GIS coverage that has been used may not be the most appropriate approach for identifying vulnerability of groundwater to carbamates and other pesticides.

If the Nolan et al. ( 2002) vulnerability approach is to be used for identifying areas vulnerable to pesticides reaching shallow groundwater, it should be compared to existing data on known contaminated aquifers, both current and past, to assess its quality. The vulnerability model should agree well with field observations. Where there is disagreement, the EPA should investigate to understand why they differ.

If one examines the next step proposed by EPA in modeling groundwater vulnerability, there is possibly a disconnect between the proposed groundwater vulnerability approach and the modeling approach. For example, the groundwater 76 of 113 vulnerability screening GIS map was created considering confined as well as unconfined aquifers. However, the modeling approach only considers very shallow unconfined aquifers. The use of STATSGO ( a nationwide soil GIS dataset) may be a more appropriate mechanism for vulnerability screening. The use of STATSGO combined with the models would allow the most vulnerable settings to be identified. Also, using STATSGO would be consistent with the proposed approach for modeling pesticide exposure in shallow groundwater, since soil properties play an important role in parameterizing the models, while aquifer properties are not considered.

Question 2 Use of leaching models for ground water exposure assessments:

The Agency is basing its ground water exposure assessment on private rural wells drawing its drinking water from an unconfined aquifer. The estimated exposure in drinking water from these wells is based on the concentration estimated at the top of this aquifer.

The three models that the Agency is considering for use in the cumulative assessment and in refined ground water exposure in aggregate ( individual chemical) assessments   LEACHP, PRZM, and RZWQM   are leaching models that predict pesticide concentrations in water at some depth below the surface. As a result, the Agency is using estimated pesticide concentrations in the vadose zone to represent concentrations in shallow ground water. As such, estimates would represent potential drinking water exposure from wells drawing from shallow, unconfined aquifers.

Is this approach a reasonable, health protective approach for use in both cumulative and aggregate drinking water exposure assessments? If this approach produces an exceedance of essentially safe exposure levels, in what manner could a better estimate of exposure to pesticides in water be derived from existing data and modeling approaches?

Panel Response For most reasonable scenarios, the proposed approach appears to be conservative and health protective. In most cases, the screens of wells are some depth below the groundwater table, allowing time for additional degradation before people drink the groundwater. The level of detail in the assessment and the assumptions are consistent or a bit more conservative than the assumptions made for exposure from other pathways. However, there are several cases where caution is needed and the approach may not be health protective. 77 of 113 If the pesticide is not entirely in the dissolved phase ( as assumed in these models) and greatly in excess of normal agricultural use rate for example due to either a large accidental spill, an unusual surface runoff event or cleaning of tanks close to the well, the pesticide might reach a shallow unconfined aquifer in concentrations far in excess of model predictions.

The use of an aquifer model combined with the estimates of pesticides reaching shallow groundwater without calibration for local conditions is cautioned. Calibrations with experimental data would likely provide better estimates of actual pesticide levels reaching drinking water. One of the challenges will be adequately representing the aquifers because data will be limited and site specific conditions could be essential for accurately estimating pesticide concentration.

Data used for model validation might not represent the " real" concentration leaching into the aquifer. Caution should be used when estimating pesticide transport to shallow groundwater based on suction lysimeter and soil sample data. If macropores are present in the soil, pesticides can move from the soil surface to shallow groundwater with little evidence of this movement in the unsaturated zone ( Malone et al., 2000). The best locations for pesticide samplers are in the capillary fringe of the groundwater or above textural interface layers. Wick pan samplers are preferred above gravity pan samplers. Tile lines give the overall best integrated sample ( Boll et al., 1992; Shalit et al., 1995; Boll et al., 1997).

The Agency's tiered approach is supported. Tiered approach is commonly used in risk assessment as a good use of limited resources by first looking into the worst case scenario using conservative assumptions. This initial tier of analysis is less time consuming and data intensive. A refining tier of analysis for a more realistic scenario is performed if the worst case scenario is deemed unrealistic and shows exceedance of safe exposure level.

The Panel raised several questions during the Agency presentation concerning how the models will be evaluated for the closeness of their prediction to the realistic situations. The Panel recommended a more rigorous review of the chosen models for N methyl carbamate pesticides, specifically for their use in cumulative risk assessment. When the worst case scenario exposure prediction exceeds the Agency pre determined safe level, and that prediction is deemed a gross over estimation, the input parameters, including the default concordance parameters ( e. g., in RZWQM), should be reviewed specific for the cumulative assessment group of the N methyl carbamate chemicals. Attempts should be made to compare model results to available monitoring data. 78 of 113 Question 3 Addressing the cumulative risk assessment needs:

The Agency has considered analyses of the capabilities of three ground water models for use in the carbamate cumulative exposure assessment and in individual chemical ( aggregate) assessments. The major areas of evaluation   hydrology, management practices, pesticide transport processes, and ease of use of the model   are described in the background document Drinking Water Exposure Assessment: Ground Water Model Evaluation. For the cumulative assessment, OPP will compare results of all three models with each other and with available monitoring. The ultimate evaluation goal for the models is how well each meets the requirement of providing reasonable, health protective estimates of pesticide residues for use in aggregate and cumulative drinking water exposure assessments.

In the SAP's estimation, how well do the three ground water models OPP proposes to use   RZWQM, PRZM, and LEACHP   compare in addressing hydrology? Macropore flow? Rainfall characteristics? Management practices? Pesticide fate and transport? Formation and movement of transformation products? Can the panel recommend other criteria that should be considered in evaluating the effectiveness of these models for estimating drinking water exposure for regulatory purposes?

Panel Response The Panel's deliberations on each of the parameters are discussed in the following sections, along with recommendations for additional parameters that should be considered in the models.

Management Practices and Pesticide Application It is necessary to identify which processes are most important for accurately modeling N methyl carbamate pesticides. For example, incorporation of applied pesticides into the soil should probably be considered because this is a recommended agricultural practice in conjunction with the application of some N methyl carbamate pesticides. Another important factor to consider is the amount of pesticide applied. The quantity used can usually be estimated based on sales records and on label instructions. The management practices ( such as Integrated Pest Management) can greatly affect the amount of pesticide applied and is, therefore, an important consideration in modeling for this purpose.

Management practices usually have little effect on the water balance with the exception of those that include a winter cover crop ( Walter et al., 1979). In these cases, 79 of 113 there is less percolation during the early spring. Therefore, management practices that include cover crops should be considered in the simulation of the hydrology.

Another concern is that agricultural uses may not be the only source of N methyl carbamate pesticides, especially in suburban settings, yet these settings may still be drawing water from surficial aquifer wells. Non agricultural use may be small in its overall contribution, but such uses may be important and could even dominate in selected situations. It may be important for the models to allow for consideration of nonagricultural sources .

Degradation Product Formation N methyl carbamate pesticides in the environment are relatively labile and readily undergo hydrolysis giving rise to the amino acid and the alcohol form of the leaving group, e. g, 1 naphthol. The leaving group component may itself be somewhat toxic. There are likely to be other products as well. These should be traceable as part of the modeling. Therefore, the model should simulate formation and movement of degradation products. For example, metabolites of aldicarb such as aldicarb sulfoxide and aldicarb sulfone may be more important than the parent compound ( Kraft and Helmke, 1991; Smelt et al., 1995). This component must be thoroughly tested because there have been mass balance problems with transformation products in model testing.

Preferential Flow Macropore flow is likely essential to accurately model carbamate transport to shallow groundwater. Even on loamy sand ( Ritter et al., 1996), macropore flow can be an important process. Specifically, N methyl carbamate pesticides such as carbofuran, can be transported to shallow groundwater via preferential flow ( Isensee et al., 1990; Kladivko et al, 1999). Model codes including a description of preferential flow processes required less calibration efforts to meet the FOCUS model performance criteria on a sandy loam soil than those without such description ( Thorsen et al., 1998).

The methods to simulate preferential flow are vastly different between the models. This should not be a surprise since the theory for simulating preferential flow is still under development and not understood well. Of interest here, most aquifers are overlain by sandy soils. Also, most N methyl carbamate pesticides are applied on these sandy soils. On these types of soils, unstable fingered flow and fingered flow are important. Although RZWQM is the most sophisticated model in simulating preferential flow, it does not consider fingered flow ( see Figure 2). To model fingered flow, the maximum intensity of a storm might determine the number of fingered flow paths ( Selker et al., 1996). RZWQM clearly stands out in its sophistication to model macropore flow and is likely superior above the other models, but none of the models address the unstable fingered flow phenomena in sandy soils. At the present time, preferential flow including funnel flow in sandy soils could be included by model developers, since the initial theory has been 80 of 113 developed and could be tested in field situations ( Steenhuis et al., 2001; Kim et al., 2005). EPA should be looking for these types of models for inclusion in the risk assessment.

Model users should be aware that it is simplistic to divide up the medium into one preferential flow region and a matrix component ( Steenhuis et al., 1990). In reality, there is a continuum between the fastest flow path and the slowest flow paths. The experimental and theoretical work of Kung et al. ( 2000, 2005) and Gish et al. ( 2004) confirmed this concept.

In structured soil ( e. g., silty, loamy, and clayey soil), macropore characteristics for RZWQM are easily parameterized. Little is known for sandy soils though and work is needed to parameterize RZWQM for sandy soils.

Hydrology and Rainfall Characteristics Most models predict the water balance quite accurately, including the ones proposed for use by the Agency. Accurate determination of daily rainfall amount is essential and may be one of the most crucial issues concerning accurate simulation of water balance ( Wagenet and Hutson, 1996). The rainfall rate is also important, especially if macropore flow is identified as an important process to include in the model. Skopp et al. ( 1981) varied how water was added to a column, which affected the rate of preferential flow, and consequently the breakthrough curves were completely different ( also see Skopp and Gardner, 1992). Steenhuis et al. ( 1990) developed a preferential flow model whereby flow was directly related to the rate of application.

Correctly characterizing rainfall intensity may be especially important when applying RZWQM, because it will not simulate macropore flow unless the rainfall is sufficiently intense. That is, 24 hour or hourly rainfall may not be sufficiently intense to result in macropore flow with RZWQM. Pesticides may move to shallow groundwater by macropore flow in the sandy soils if heavy rainfall occurs shortly after pesticide application ( Ritter et al., 1996). Subsequent rainfall events may be less important because the first storm after application can move solutes into the soil matrix, thereby reducing the potential for transport in macropores ( Shipitalo et al., 1990). Therefore, efforts should be applied to develop breakpoint rainfall input files for the first few storms after pesticide application.

Pesticide Fate Some of the more sophisticated pesticide fate routines in models should possibly be avoided. For example, the RZWQM irreversible binding routine did not provide accurate simulations of metribuzin in percolate, suggesting this concept may need to be explored further ( Malone et al., 2004). Also, estimating parameters for kinetic sorption may be difficult. However, if most pesticide is transported through percolate shortly after application, irreversible binding and kinetic sorption may be less important than short term 81 of 113 half life and equilibrium sorption.

Both the degradation rate and the adsorption strength are highly variable between fields. The variability of degradation rates between sites is one of the main problems for simulating the pesticide concentration in the aquifers with confidence, and this might be the main reason that the pesticide concentration in the USGS model evaluations ( included as background documents) are so poorly simulated. Pivetz and Steenhuis ( 1995) and Pivetz et al. ( 1996) concluded that the degradation rate may vary significantly in time. Reasonable estimates can only be obtained if prior experiments have been carried out at the site. Because simple parameters such as the linear adsorption model and the first order degradation rate model are difficult to obtain, it will likely be too difficult to apply more complicated formulations of these relationships nationally.

Enhanced or accelerated degradation has been reported in N methyl carbamate pesticides such as carbofuran ( Cogger et al., 1998; Getzin and Shanks, 1990) and aldicarb ( Suett and Jukes, 1988; Smelt et al., 1987). Enhanced degradation, defined as an increase in pesticide degradation with each application, may lead to higher or more frequent applications because insecticidal efficacy will be reduced.

Tile Drainage If N methyl carbamate pesticides are applied to areas that are tile drained, then the selected model should simulate tile drainage for accurate loading estimates to surface water. Recent research suggests that the pesticide isoxaflutole and its metabolite RPA 202248 are transported to the edge of the field through subsurface drains at about 1 m at rates similar to runoff ( personal communications, Garey A. Fox, Assistant Professor, University of Mississippi). However, tile drainage may not be necessary in the model for the purposes of the cumulative risk assessment, especially if this is a screening level tool.

Model Calibration, Sensitivity, Testing, and Ease of Use The use of any model of pesticide contamination from agricultural use is likely to give only an estimation of the contamination found in the surficial aquifer. The use of the PRZM system is well established with respect to EPA usage. This suggests that its use in this context may be supported above and beyond other ostensibly better, but perhaps more complicated and difficult to use models. Further, parameterization of the other models may be difficult since data may not be available for scenarios of interest.

Experience with LeachN, the nitrogen movement implementation of the Leach model, demonstrates the difficulty of getting these models to predict observed levels of materials, even with intensive measurement of soil characteristics, initial conditions and control of soil nutrient additions. EPA may wish to create their own tool for this purpose that they understand and can distribute and control. 82 of 113 The sensitivity of the models to key parameters that are likely to be changed in application of the models should be well understood. Also, the sensitivity of key parameters relative to the group of models being considered would provide insight into the most applicable model or models for various situations. Perhaps probabilistic or nonlinear analysis of the model output might be the best approach to analyzing the parameter interdependencies.

The ease of model calibration and the ability to calibrate each model should be considered. Models often must be calibrated to perform adequately. Therefore, calibrations will be important considerations when using the groundwater models to estimate pesticides reaching shallow groundwater.

The ability to automate the use of each model so that it can readily utilize data from databases and GIS to facilitate automated model runs will likely be important for future applications of the models. Models that require the use of a model specific interface may not work very well or at all for such purposes.

Other criteria to take into consideration are: transparency in the model's calculations, the data requirements, post analysis of the model output, internal documentation of code, ability to modify code, ability to interface the code with thirdparty GUIs, and ability to link to statistical analysis packages and GIS packages. In analyzing a full cumulative risk assessment, users and reviewers must have models that are simple to evaluate during all portions of the risk assessment.

For model testing, as complete a dataset as possible should be used. Some of the discrepancies between model and data may be resolved by averaging samples taken over a broader area. The models have much in common, so discrepancies between models must be explicable in each case in terms of known differences in model assumptions. The dataset should include soil samples, runoff, and percolate, because neglecting one of these dissipation pathways may provide an incomplete picture of model performance ( Malone et al., 1999). When preferential flow occurs, soil samples and suction lysimeters may not adequately characterize chemical movement to shallow groundwater, and percolate samples may be necessary ( Malone et al., 2000).

The effects of water purification processes on carbamate levels also should potentially be considered. If such processes routinely eliminate most N methyl carbamate pesticides from drinking water, this modeling would seem to be superfluous except for those drinking untreated water. Estimation of these purification effects would thus seem critical, particularly new filtration technologies and the effects of chlorination and chloramines disinfectant technology. At the current time, the potential for significant overestimation of carbamate levels from these models seems to exist. These factors, together with the low level of exposure to N methyl carbamate pesticides from drinking 83 of 113 water relative to other exposure sources, suggest this modeling may be of value for only a subset of the public.

Question 4 Estimating cumulative carbamate exposures in ground water:

Available monitoring data, primarily from the USGS NAWQA program, indicate that more than one carbamate in the cumulative action group may occur together in ground water ( see the drinking water exposure section of the case study). Co occurrence in ground water results when more than one carbamate is used at different times on the same crop, on different crops in rotation on the same fields, or on different crops grown on adjacent fields.

For surface water sources of drinking water, OPP adjusted estimated pesticide concentrations in the modeled reservoir by percent crop area and percent crop treated in the watershed to reflect the dilution of untreated areas on the total pesticide load reaching the reservoir ( see the description of the surface water exposure assessment in the analysis methods of the Drinking Water section of the case study).

Given that less mixing of water from different fields is expected in shallow aquifers, should OPP use similar adjustments to ground water concentrations estimated from the leaching models? If not, what recommendations does the Panel have to account for the potential contributions from different fields treated with carbamates?

Panel Response Surface water and groundwater behave quite differently. Surface water travels relatively rapidly from any location in the watershed to the outlet, and the Agency's adjustment for surface water edge of field concentration by percent crop area and percent crop treated in the watershed is reasonable. Thus, the assumption is made that pesticide concentration in surface drinking water can be represented by the average spatial concentration of the runoff.

The assumption of taking spatial averages in the watershed is not valid for groundwater. Generally, groundwater travels slowly, usually less than 100 m/ year. The pesticide concentration in each well depends on its surroundings and cannot be represented by an average concentration. Thus, the people drinking water from the wells are exposed to different concentrations. This is important for the risk analysis where highest concentrations are often of interest. 84 of 113 The different concentrations for different wells are schematically depicted in Figure 1. Pesticides are applied to a field as indicated. The pesticides travel in a distinct path to the river through the ground water. Figure 1 illustrates that only the shallow well downstream from the field contains pesticide, while the other wells are pesticide free. The drinking water well in Figure 1 samples water from a larger volume and will be affected by more than one field.

The screens of shallow wells are typically a minimum of 4 feet long and sample pesticides over these depths. This means that the daily input at the top of the aquifer is averaged over a certain time period. To help describe this better, Figure 2 depicts a close up view of the situation near the pesticide field from Figure 1. Only the water that fell on top of the field is shown in the groundwater depicted. It can be seen that the oldest water is the deepest, while the water which just arrived is near the surface of the aquifer. While the water is moving downward, it is, at the same time, moving sideways toward the river. The line in the groundwater in Figure 2 is a streamline. In this case water that falls at the upper edge of the field follows this path.

Although spatial averages cannot be taken for wells, temporal averaging can be done. It is not difficult to calculate a reasonable time period over which the pesticide concentrations from the vadose pesticide model should be averaged. The depth of " recharge layer" per year is simply the recharge per year, r, divided by the saturated moisture content,  s. The depth of water that represents water reaching the aquifer each year, D, can be calculated taking into account the adsorption partition coefficient, k, and the density of the soil,  , as:

The time, T, over which the pesticides arriving to the groundwater should be averaged can then be simply written as:

D S T =

where S is the screen length.

The location of stream lines can be found with the method presented by Gelhar and Williams ( 1974). This method was used in a model called MOUSE ( Steenhuis et al., 1987). The model had many similarities to the models currently used by EPA ( but without preferential flow paths). The details of simulating streamlines are given in these publications. The method consists of two simple equations; one that determines the displacement in the horizontal direction and the other in the vertical direction. D r k s = +     85 of 113 The two surface water modifiers, percentage of crop area ( PCA) and percentage of crop treated ( PCT), can reasonably be used in ground water prediction also. However, the persistence of N methyl carbamate pesticides in ground water would require considerations of multiple years of pesticide use. This is different from focusing the surface water scenarios on spikes associated with runoff and its relatively fast decline to low or nondetectable levels after runoff events. Considerations also can be given for time of pesticide use and ground water movement. In terms of PCA, the pattern of change in land use over time should be accounted for ( e. g., to consider pesticide use at sites that were once agricultural). When available, multiple years of data on PCT also may be useful to modify the default assumption of 100% PCT.

Figure 1. Schematic of flow paths in a valley with a river. Only the shallow well downstream of the field is affected by the field in which the pesticides are applied. 86 of 113 Figure 2. Simple schematic of ground water movement under a pesticide movement.

Question 5 Several carbamates are known to persist for long periods of time after they reach ground water, particularly slightly acidic to acidic ground water ( see the drinking water exposure section of the case study). This differs from estimates of exposures in surface water, where pesticide concentrations tend to occur as spikes associated with runoff and decline to low or nondetectable levels after runoff events. Pesticide concentrations in acidic ground water are slower to respond to changes in use patterns and mitigation actions than would be expected in surface water.

What recommendations does the Panel have for addressing carbamate persistence in ground water in order to provide a reasonable, health protective estimate of residue levels in shallow ground water sources of drinking water? 87 of 113 Panel Response The Agency is considering three possible approaches:

1) at one extreme, assume no background residues ( drinking water exposures would reflect only what is estimated by modeling), i. e., all residues in groundwater are " fresh";

2) at the other extreme, assume a baseline background concentration ( based on available monitoring), with model estimates as additions and no decline;

3) in between, include the background levels with model estimates, but provide an estimate of decline in residues over time ( estimate based on long term trends in monitoring)

The EPA's 3rd suggested approach from the " background document" for Session 3 appears sensible. However, it is unclear how EPA would obtain the " long term trends in monitoring".

The background document clearly identified that carbamate transformation can be pH specific. Also, the transformations of aldicarb metabolites are concentration specific ( Smelt et al., 1995). That is, higher concentrations degrade more slowly than lower concentrations. At least two peer reviewed sources that studied aldicarb transformation used initial concentrations greater than 150 ug/ L. Some studies of aldicarb degradation suggest that anaerobic conditions increase degradation ( Smelt et al., 1995; Kraft and Helmke, 1992).

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Steenhuis, T. S., Kim, Y. J., Parlange, J. Y., Akhtar, M. S., Richards, B. K., Dung, K. J. S., Gish, T. J., Dekker, L. W., Ritsema, C. J., and Aburime, S. O. ( 2001) An Equation for Describing Solute Transport in Field Soils with Preferential Flow Paths. Preferential Flow, Water Movements and Chemical Transport in the Environment. D. D. Bosch and K. W. King, Editors. ASAE Proceedings. Second International Symposiums. Honolulu, HI. 137 140. 91 of 113 Suett, D. L. and Jukes, A. A. ( 1988) Accelerated degradation of aldicarb and its oxidation precuts in previously treated soils. Crop Protection 7: 147 152.

Thorsen, M., Jorgensen, P. R., Felding, G., Jacobsen, O. H., Spliid, N. H. and Refsgaard, J. C. ( 1998). Evaluation Of A Stepwise Procedure For Comparative Validation Of Pesticide Leaching Models. J. Environ. Qual.. 27( 5): 1183 1193 Wagenet, R. J. and Hutson, J. L. ( 1996) Scale dependency of solute transport modeling/ GIS applications J. Environ. Qual. 25 ( 3): 499 510 Walter, M. F., Steenhuis, T. S. and Haith, D. A. ( 1979) Nonpoint Source Pollution Control by Soil and Water Conservation Practices. Trans. ASAE 22: 834 840. 92 of 113 SAP Minutes No. 2005 01 A Set of Scientific Issues Being Considered by the Environmental Protection Agency Regarding:

N METHYL CARBAMATE CUMULATIVE RISK ASSESSMENT: PILOT CUMULATIVE ANALYSIS SESSION 4: N METHYL CARBAMATE EXPOSURE ASSESSMENT: A PILOT CASE STUDY February 18, 2005 FIFRA Scientific Advisory Panel Meeting held at the Holiday Inn National Airport Arlington, VA Mr. Joseph E. Bailey Steven G. Heeringa, Ph. D. Designated Federal Official FIFRA SAP Session Chair FIFRA Scientific Advisory Panel FIFRA Scientific Advisory Panel Date: April 15, 2005 Date: April 15, 2005 93 of 113 Federal Insecticide, Fungicide and Rodenticide Act Scientific Advisory Panel Meeting February 18, 2005 N methyl Carbamate Cumulative Risk Assessment: Pilot Cumulative Analysis Session 4: N methyl Carbamate Exposure Assessment: A Pilot Case Study PARTICIPANTS FIFRA SAP Session Chair Steven G. Heeringa, Ph. D., Research Scientist & Director for Statistical Design, Institute for Social Research, University of Michigan, Ann Arbor, MI Designated Federal Official Joseph E. Bailey, FIFRA Scientific Advisory Panel Staff, Office of Science Coordination and Policy, EPA FIFRA Scientific Advisory Panel Members Stuart Handwerger, M. D., Professor of Pediatrics, University of Cincinnati Children's Hospital Medical Center, Cincinnati, OH Gary E. Isom, Ph. D., Professor of Toxicology, School of Pharmacy & Pharmacal Sciences, Purdue University, West Lafayette, IN Kenneth M. Portier, Ph. D., Associate Professor, Statistics, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL FQPA Science Review Board Members:

John Adgate, Ph. D., Assistant Professor, Division of Environmental Health Sciences, University of Minnesota, School of Public Health, Minneapolis, MN William Brimijoin, Ph. D., Chair, Pharmacology, Mayo Clinic and Medical School, Rochester, MN George B. Corcoran, Ph. D., Professor & Chairman, Department of Pharmaceutical Sciences, Eugene Applebaum College of Pharmacy & Health Sciences, Wayne State University, Detroit, MI 94 of 113 Lutz Edler, Ph. D., Head, Biostatistics Unit C060, German Cancer Research Center, Heidelberg, Germany Bernard Engel, Ph. D., Professor, Agricultural & Biological Engineering, Purdue University, West Lafayette, IN Scott Ferson, Ph. D., Senior Scientist, Applied Biomathematics, Setauket, NY Lawrence J. Fischer, Ph. D., Director, Center for Integrative Toxicology, Michigan State University, East Lansing, MI Natalie Freeman, Ph. D., Associate Professor, Department of Physiological Sciences, College of Veterinary Medicine, University of Florida, Gainesville, FL Dale Hattis, Ph. D., Research Professor, Center for Technology, Environment & Development ( CENTED), George Perkins Marsh Institute, Clark University, Worcester, MA James P. Kehrer, Ph. D., Director, Center for Molecular & Cellular Toxicology, College of Pharmacy, The University of Texas at Austin, Austin, TX Chensheng Lu, Ph. D., Assistant Professor, Department of Environmental & Occupational Health, Rollins School of Public Health, Emory University, Atlanta, GA Peter D. M. Macdonald, D. Phil., Professor of Mathematics & Statistics, McMaster University, Hamilton, Ontario, Canada David MacIntosh, Sc. D., Senior Associate, Environmental Health & Engineering, Inc., Newton, MA Robert W. Malone, Ph. D., Agricultural Engineer, USDA ARS, National Soil Tilth Laboratory, Ames, IA Christopher J. Portier, Ph. D., Director, Environmental Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, NC Nu may Ruby Reed, Ph. D., D. A. B. T., Staff Toxicologist, Department of Pesticide Regulation, California Environmental Protection Agency, Sacramento, CA P. Barry Ryan, Ph. D., Professor, Environmental & Occupational Health, Rollins School of Public Health, Emory University, Atlanta, GA Tammo S. Steenhuis, Ph. D., Professor of Watershed Management, Cornell University, Ithaca, NY 95 of 113 Michael D. Wheeler, Ph. D., Assistant Professor, Departments of Pharmacology & Medicine, University of North Carolina, Skipper Bowles Center for Alcohol Studies, Chapel Hill, NC 96 of 113 INTRODUCTION In Session 4 of this meeting, the FIFRA SAP met to consider and review the N methyl carbamate pesticide cumulative risk assessment: pilot cumulative analysis, specifically issues related to the N methyl carbamate exposure assessment: a pilot case study. OPP solicited comment from the SAP on a case study which uses the Relative Potency Factor ( RPF) approach to perform a cumulative exposure assessment for a group of 10 N methyl carbamate pesticides that have been previously determined to represent a common assessment group based on the chemicals' similar mechanism of toxicity. For Session 4, Dr. Anna Lowit and Mr. David Miller ( Health Effects Division, Office of Pesticide Programs) provided an introduction and background on the pilot case study. Mr. David Hrdy, Mr. Jeff Evans, Dr. Steve Nako, and Mr. David Miller ( Health Effects Division, Office of Pesticide Programs) provided a detailed presentation on the pilot exposure assessment case study, including discussions on dietary assessment, residential exposure inputs, residential use data and aggregate exposure, cumulative exposure assessment and model comparison. Opening remarks for Session 4 were provided by Dr. Tina Levine ( Acting Director, Health Effects Division, Office of Pesticide Programs). 97 of 113 SUMMARY OF PANEL DISCUSSION AND RECOMMENDATIONS The Panel members agree that the overall case study is a thoughtful and useful analysis. EPA has gained much knowledge from development of the organophosphate ( OP) cumulative risk assessment and the Agency is to be commended on the carefully developed exploratory analysis for the N methyl carbamates, given the varying quality of the datasets that were available. The level of detail is adequate for a case study, but additional effort is needed in the full assessment, especially with regard to non food scenarios. It was felt that uncertainty analyses will help to identify the datasets that should be examined most closely.

The default assumption of eating events close together in time, in the context of the pilot case study for the N methyl carbamate exposure assessment, is a reasonable approach. Allowing for recovery seems difficult given the unknown durations that would have to be assigned to defensibly address this issue. The extent to which eating frequency affects the outcome of this analysis is a testable proposition that could be explored as a distributed analysis. The Panel noted that a supplementary analysis using a stratified sample of high end consumers would help justify the viability of this approach.

The Panel made several recommendations regarding the cumulative N methyl carbamate pesticide case study in terms of the data availability, reliability, limitations, and evaluations for use in representing patterns of pesticide co occurence. In addition, recommendations were made about pesticide residue degradation estimates, residue data availability, and the need to use regional and temporal variability in residues in the exposure scenarios chosen to be assessed by the Agency.

The case study presented to the Panel clearly demonstrated that available data, although limited, could be combined to conduct a cumulative exposure assessment for the N methyl carbamate pesticides. The major concerns of the Panel were related to the age and quality of the data available, and its impact on pesticide use patterns that have been changing. It was recommended that National Health and Nutrition Examination Survey ( NHANES) biomonitoring data be used in combination with PBPK models to serve as a bounding analysis for the cumulative risk assessment. 98 of 113 PANEL DELIBERATIONS AND RESPONSE TO THE CHARGE The specific issues to be addressed by the Panel are keyed to the Agency's background documents, references and charge questions.

Question 1 There are several key principles for conducting a cumulative risk assessment. One such principle concerns the time frame of both the exposure ( e. g., When does exposure occur? What is the exposure duration?) and of the toxic effect ( e. g., What are the time to peak effects and the time to recovery? How quickly is the effect reversed?). Both should be adequately considered when performing a cumulative risk assessment so that an individual's exposure is matched with relevant and appropriate toxicological values in terms of duration and timing. There are several important considerations with respect to the temporal characteristics of the exposures and of the cholinesterase inhibitory effects of N methyl carbamate pesticides in estimating their cumulative risk.

OPP used a Relative Potency Factor ( RPF) approach in this case study which is based on cholinesterase inhibition data from acute dosing studies performed in the rat. A similar approach was used in the organophosphorus pesticide cumulative risk assessment several years ago. This RPF approach expresses toxicity of each chemical in terms of " index chemical equivalents". In this approach, all exposure events within a day are adjusted by their RPFs and summed. The three exposure models ( DEEM/ Calendex, LifeLine, and CARES) used in the case study express exposure as a distribution of 1 day ( 24 hour totals) exposures within a population.

Since AChE inhibition caused by the N methyl carbamate pesticides recovers rapidly ( minutes to hours), it might be important to consider the intra day timing of exposure events. Specifically, if the exposure events within a day are distributed sufficiently far apart in time so that significant recovery of AChE inhibition occurs between any two exposure events, then summing exposures over 24 hours might overestimate the risk associated with AChE inhibition. For example, if an individual consumed 200 mL of apple juice in the early morning, an additional 200 mL during the afternoon, and another 200 mL late at night, this could present a very different risk picture than if the total 600 mL were presumed to be consumed at one time.

The current Food Commodity Intake Database ( FCID) and the DEEM/ Calendex, LifeLine, and CARES models are set up to consider food consumption on a per day ( rather than per eating occasion) manner. Thus, the exposures reported in this case study reflect daily ( 24 hour) exposures. To the extent that a day's eating occasion events leading to high ( total) daily exposure are close together in time, the RPF approach described in the case study provides reasonable 99 of 113 estimates of risk. To the extent that eating exposure events leading to high total daily exposures are widely separated in time such that recovery of AChE inhibition occurs, the risks under the RPF approach in this case study may be overstated and a more sophisticated approach which accounts for intra day eating patterns would be more appropriate.

OPP has investigated the degree to which high end exposures can be attributed to specific eating occasions ( within a day) that occur either closely spaced in time or widely separated by time by looking at the actual individual consumption events as reported in the Continuing Survey of Food Intakes by Individuals ( CSFII). Specifically, OPP has looked at the CSFII   based dietary records for individuals at several locations in the upper end of the exposure distribution to determine the extent to which these daily high end exposures can be attributed to a single eating event, several eating events spaced closely in time ( over several hours), or eating events widely separated by time ( more than several hours). As described in Section IV. H of the case study document, OPP found that that a large fraction of daily records contributing to the upper tail of the food exposure distribution represent single eating occasions. Assuming that subsequent, more detailed and extensive analyses provide confirmation of these preliminary observations and analyses, OPP believes and that it is unlikely that any more sophisticated, temporal based approach which better accounts for temporal separation of eating/ exposure events will result in substantial or significant changes in OPP's risk estimates.

Part A. EPA requests the SAP provide comments on this exploratory analysis with respect to its adequacy and appropriateness. Please also provide suggestions for future, more detailed analyses.

Panel Response The Panel agrees that the overall case study is a thoughtful and useful analysis. EPA has gained much knowledge from development of the organophosphate ( OP) cumulative risk assessment and the Agency is to be commended on the carefully developed exploratory analysis, given the varying quality of the datasets that were available. The level of detail is adequate for a case study, but more effort is needed in the full assessment, especially the non food scenarios. Several additional exposure scenarios should be considered and they are detailed below.

The general framing of Question 1 conveys concern about overestimates of risk. The Panel thinks that EPA should be focusing on identifying areas where overestimates or underestimates might occur, and then treat these specific concerns as points to consider in the variability and uncertainty analysis. Clarity is an important consideration as the Agency moves toward a full case study. 100 of 113 The assessment process is complex, with different models contributing estimates to the overall assessment. It is crucial that a stochastic model that implements a quantitative uncertainty analysis be performed. It is an important addition and a necessary adjunct to the overall cumulative assessment that compares the various pathways of exposure.

Overall data quality is highly variable within the assessment. In general, the food assessment has the highest quality and most abundant data, with lower quality and more sparse datasets for water, incidental ingestion, and dermal pathways. Presentation of model calibrations for these other datasets ( e. g., measurements of tap water in the Spring season in southeast North Carolina, dermal exposure post application studies and model outputs compared to urinary biomonitoring levels observed in the US population) are important in order for the output of the assessment to be an accurate representation of the real world.

Another key analytical issue is the choice of exposure distributions to be used. In particular, the Panel believes that the exposure distributions need careful consideration and should use all available data without artificial truncation of what are typically lognormal exposure distributions. The concern that this may produce unreasonable assessments can be tested empirically if these " high" values are included but subjected to sensitivity and uncertainty analyses in the overall cumulative assessments. Many of the input datasets for the analysis may already artificially truncate the exposure distributions by whom they sample and how the samples are taken. Recruiting by telephone, for example, tends to find established people and likely does not reflect current diversity in race, ethnicity and income in the United States. Truncations, if they are used, should be based on physical limits of concentration.

The Panel believes that EPA should focus on appropriate time scales and populations. The use of a 99.9th percentile is important and should be presented such that it is clear this means approximately 4,000 children in the United States may be at risk given that there are approximately 4 million births in any given year.

Comments on Specific Issues The omission of institutional exposure ( i. e., non residential, non occupational) leaves a serious gap in the calculation of aggregate exposure. Each age group will spend time in schools, offices, shopping malls, recreation and entertainment facilities, etc., and there will be pesticide use in these places. It is understood that suitable data may not be available for these places, yet it is important that EPA acknowledge these data gaps in the model development. This issue was identified by the September 27 29, 2000 SAP " Session V  Aggregate and Cumulative Assessments Using Lifeline    A Case Study Using Three Hypothetical Pesticides" ( p. 31). http:// www. epa. gov/ scipoly/ sap/ 2000/ september/ modelsreport. pdf.

Recall surveys can be of dubious quality. This issue was identified by the April 30 101 of 113  May 1, 2002 SAP " Cumulative and Aggregate Risk Evaluation System ( CARES   ) Model Review" ( p. 22). http:// www. epa. gov/ scipoly/ sap/ 2002/ april/ caresmeetingminutes. pdf The Panel reminded the Agency that some of the survey data available for the pilot case study exposure assessment of the N methyl carbamate pesticides are dated and may not closely reflect some current exposure scenarios or be representative of the current population of the U. S.

Relative potency factors ( i. e., the ratio of the toxic potency of a given chemical to that of the index chemical) are used to convert exposures of all chemicals in the group into exposure equivalents of the index chemical. The current presentation of this approach for the calculation of the cumulative residue seems more complicated than necessary ( e. g. when it is explained how the amount of residue of each chemical is adjusted by multiplying by a Relative Potency Factor ( RPF) to get the equivalent residue of an index chemical). Consulting the Toxic Equivalency Factor Toxic Equivalent ( TEF TEQ) approach for the dioxins and furans, where one determines the TEFs, obtains the TEQ values of each compound in the mixture exposure, and then does the multiplication and the summation, could serve as a good example to communicate this approach. However, this simple multiplication and calculation holds only if it can be shown that the carbamates act according to the principle of simple similar additive action at the chosen endpoint for risk assessment. This means that the isoboles are parallel straight lines as is assumed for the dioxins. This is a crucial assumption underlying all the analyses at this stage.

Several of the contact categories need to be expanded or better justified ( see Estimation of Cumulative Exposure from N methyl Carbamate Pesticides: A Case Study of 10 N methyl Carbamates, Table 10. Specific Exposure Routes and Pathways/ Scenarios). Additional contact routes for children should include dermal/ oral contact with gardens, ornamentals, and trees. Children have been observed having skin contact with vegetation and placing vegetation in their mouths. Whether this is a major source of dermal or non dietary ingestion is unclear; there may not be enough data to make a determination as to its impact on children's exposure.

In certain scenarios, EPA should provide better support to justify the exclusion of specific exposure routes such as " ingestion of soil and mouthing of grass" ( see Estimation of Cumulative Exposure from N methyl Carbamate Pesticides: A Case Study of 10 Nmethyl Carbamates, Section VI  D). EPA states that the reason for not including an exposure route involving oral ingestion of soil and mouthing of grass was justified because of the ' little impact' such routes had on the individual chemical risk assessments. This justification needs better support by further exploring what difference the cumulative effect of several ` little impacts' might make in the cumulative risk assessment.

In terms of clarity of the document, when discussing the residential exposure scenarios, EPA states that " The full range of exposure values  expressed as uniform, log 102 of 113 normal, triangular, or cumulative distributions  are used, where appropriate, rather than relying on point estimates." ( see Estimation of Cumulative Exposure from N methyl Carbamate Pesticides: A Case Study of 10 N methyl Carbamates, Section VI A). While the Panel applauds this move away from point estimates, there needs to be clear descriptions as to why a particular distribution was used rather than another one.

The food assessment is vulnerable in terms of extremes in food consumption, home grown vegetables and home pesticide applications. Furthermore, most of the residues are a single record of composites, and deconvolution of residue data should be considered. The food dataset should not be artificially restricted ( e. g., exclusion of over tolerance values from the USDA Pesticide Data Program ( PDP) dataset).

Part B. Given the results of the initial exploratory analysis, EPA believes that a more sophisticated time based intra day model ( e. g., PBPK in which the timing of intra day eating events is explicitly incorporated) for exposures through the food pathway would not substantially change the assessment of potential risks through this pathway compared to the results produced using the RPF methodology used in the case study in which 24 hour food consumption data is used. Please explain why you agree or disagree.

Panel Response The default assumption of eating events close together in time is a proper approach in the context of this pilot case study for the N methyl carbamate exposure assessment. Allowing for recovery seems difficult given the unknown durations that would have to be assigned to defensibly address this issue. The extent to which eating frequency affects the outcome of this case study analysis is a testable proposition that could be explored as a distributed analysis. The Panel noted that a supplementary analysis using a stratified sample of consumers at the high end of pesticide ingestion would help justify the viability of this approach. However, a supplementary analysis should only be done if the underlying data are adequate to support the analysis. The Panel is skeptical that this proposition is testable with existing datasets. For example, the assessment presents comparison of records for a number of eating occasions and found few, if any, with four or more per day ( see Estimation of Cumulative Exposure from N methyl Carbamate Pesticides: A Case Study of 10 N methyl Carbamates, Section IV H). Food consumption patterns, particularly for fruit juices in some families, is on an ad libitum pattern rather than as a specified number of events per day. The structure of the CSFII surveys would not adequately pick up this type of exposure. Therefore, the concern is that the data do not adequately capture human behavior, and exposures may not be seen because of the quality of the available data and not because the potential for exposure did not exist.

Question 2 A key concept that is unique to cumulative risk assessments is the concept of 103 of 113 co occurrence of residues ( and thus co or simultaneous exposure) to members of the Common Assessment Group ( CAG). Specifically, a cumulative assessment must appropriately consider residues that co occur in time and space since these exposures must by combined and considered jointly. This is true for exposures through the food pathway, the drinking water pathway, and the residential pathway. The USDA's PDP data program uses multi analyte methods and thus simultaneously measures co occurring residues in samples. The drinking water concentrations generated from the PRZM EXAMS model considered regional Nmethyl carbamate pesticide use and usage practices and thus implicitly considered co occuring residues.

Exposures through the residential pathways can also co occur. One of the unique aspects of the N methyl carbamate pesticide cumulative exposure assessment is the use of the Residential Exposure Joint Venture ( REJV) data that provides current information on co occurring use patterns for residential exposure. The US EPA National Home and Garden Pesticide Use Survey ( NHGPUS) can also be used to develop residential use profiles. The PDP, PRZM EXAMS, and REJV/ NHGPUS data were used, to varying degrees, in this case study.

Please comment on the use of the pesticide use/ usage data ( e. g., REJV and NHGPUS) to account for co occurring use patterns in assessing residential exposures.

Panel Response Data for Co occurrence The Residential Exposure Joint Venture ( referred to hereafter as the Joint Venture) and the National Home and Garden Pesticide Use Survey ( referred to hereafter as the Home and Garden Survey) appear to be the two main databases for characterizing the co occurring pesticide use patterns. However, their use in assessing the co occurrence of pesticide use would need to be carefully considered and justified as it is possible that neither database fully captures the diversity of the US population as it presently exists. The overall representativeness of both surveys should be evaluated to ensure they accurately reflect the racial, ethnic, and income diversity that currently exists in the U. S. population.

The Panel appreciates the efforts of the Joint Venture task force that surveyed and collected more updated data on pesticide use patterns. However, without access to this proprietary database, the Panel is unable to provide a proper review. The Panel is also unable at this time, to determine if there might be alternative ways to utilize the data ( e. g., the reports from respondents who provided less than 12 months diaries) or any new dimensions to exposure analysis other than what was presented by the Agency. 104 of 113 The Panel understands that the Agency's effort to evaluate the use of Joint Venture data is on going and encourages a careful review of the validity and reliability of the Joint Venture data, in particular the potential effects of response bias and errors in reporting as indicated by product application combinations that are not plausible.

Additional Comments on Specific Issues Regarding the Joint Venture Database.

As mentioned earlier in the Panel's response to Question 1  Part A, telephone recruiting may not provide the most representative sampling of the population. The dependence on telephone for recruiting may bias the sample to a wealthier than average population and may perhaps also bias against younger users that rely solely on cell phones whose numbers are not readily available. The Agency is encouraged to systematically address the overall issues of telephone recruiting and consider a sub study of nonresponders that includes a telephone or personal interview as well as a validity study of responders that includes personal or telephone interviews.

The demonstration of validity should also include response rates, and the adjustments done to them need to be understandable to the Panel and others who will review the cumulative risk assessment. The extreme loss of response rate after the initial screening points to the need for a careful evaluation of the Joint Venture database. The National Family Opinion ( NFO) adjusted survey results for 6 demographic factors in an attempt to make the data representative of the overall U. S. population. The factors chosen to make such adjustments should be related to the use of pesticides, and specifically, to the N methyl carbamate pesticides.

Issues of statistical inference by region or state are important, as is the number of homes represented by the survey in demographic subgroups. The limitation of sample size becomes a greater issue when the database is divided into more refined geographic locations.

The analysis of the database should include the concordance between the inventory data and application records ( accounting for labeled application method where appropriate).

Some of the data, as presented by the Agency, stood out and may indicate the need for a more careful evaluation. For example, the 69% derived proportion of applications to trees was by a handwand sprayer ( see Estimation of Cumulative Exposure from N methyl Carbamate Pesticides: A Case Study of 10 N methyl Carbamates, Table A. 1.2  Residential Use of Chemical A, REJV/ 1). Also, the 67% usage rate over a year seems low compared to other surveys. Both Adgate et al. ( 2000) and the Home and Garden Survey reported usage rates in the 80 90% range.

The co occurrence information provided by the Joint Venture is unique and 105 of 113 valuable, yet the limitations of those data should be fully explored and stated. For example, the table of conditional probabilities ( see Estimation of Cumulative Exposure from N methyl Carbamate Pesticides: A Case Study of 10 N methyl Carbamates, Table A. 1.8  Chemical A Scenario Co occurance Probability Matrix) for co occurrence of pesticide applications to lawns must be based on a notably small number of observations ( e. g., 1 or 2 homes).

Given the proprietary nature of the Joint Venture dataset, and the need for transparency in Agency decision making, EPA should carefully consider the pros and cons of using this data source and the specific information it can provide. The Agency is encouraged to investigate the reliability of the co occurrence data represented in the Joint Venture, to identify the important limitations, weaknesses, and uncertainties associated with the amount of information therein, and to incorporate this knowledge into the Nmethyl carbamate pesticide cumulative risk assessment ( CRA).

Additional Comments on Specific Issues Regarding Home and Garden Survey The main concern regarding the Home and Garden Survey database is the lack of assurance that the pattern of pesticide use has remained the same since the survey was taken more than 15 years ago. In many respects, the household pesticide use practices have likely changed over the years. For example, one cannot assume that pesticide use practices and types of pesticides used for various applications are the same now as then. If the amount of advertising that is conducted for marketing residential pesticides is effective, there should be at least increased product purchasing if not usage. The demographic distribution of responders in the study may not reflect current demographics and therefore may either underestimate or overestimate usage patterns. In addition, the choice of pesticide may also have changed in the past 15 years. It would be desirable to have an updated Home and Garden Survey in order to have some assurance that the survey results and extracted co occurrences are still valid.

The Joint Venture can be expected to reflect more closely the development of better spraying equipment, improvements in use labeling, and the awareness of domestic pesticide use currently. However, it is unclear how much information from that data source is/ will be used. Thus, at this stage, the Panel strongly recommends that the Agency compare and contrast the results from Home and Garden Survey and the Joint Venture data where possible. The advantages and disadvantages of using information from one or both surveys should be determined. The comparison also could allow a better assessment of how the Home and Garden Survey data can be utilized in Lifeline to imply current use patterns. An Agency effort to provide easy public access to the two parts of the Home and Garden Survey report is also recommended.

Pesticide Residue Data The Panel commented on the approach used for pesticide degradation parameters 106 of 113 to define foliar residue in contact exposure ( see Estimation Of Cumulative Exposure From N Methyl Carbamate Pesticides: A Case Study Of 10 N Methyl Carbamates, Section VIF For example, setting the residue to zero for Chemical A 30 days after its application does not appear to be " conservative" because, based on its half life of 9 days, 10% residue is expected to remain. As for chemical G, a linear degradation rate is highly unusual because almost all environmental processes are first order or quasi first order. The Agency is encouraged to revisit the assumptions about chemical degradation with respect to realism versus conservatism.

The Panel recommends that the regional and temporal patterns of N methyl carbamate pesticide residues be explored across all pathways of exposure ( not just for pathways associated with applications around the residence and for drinking water) and reflected in the exposure analysis estimates. For example, the concentrations of certain organophosphates in food display a temporal pattern with the highest concentrations occurring in the spring and summer months. It seems reasonable to also expect that Nmethyl carbamate pesticides used in domestic agriculture will display a similar pattern. The Agency is encouraged to further explore the potential seasonal patterns of the PDP data that are used in assessing dietary exposures.

Exposure Scenarios The studies used to develop the unit exposures for each application scenario should be made available to the Panel to evaluate their validity and reliability. Many factors influence the results of such tests and the Panel is not currently aware of the conditions during the test. Some of the important factors include: sprayer target geometry; meteorology; spray nozzle type and settings, if adjustable; duration of application events; sampling methods and locations; analytical methods and performances; data reduction and analysis methods.

Ready to use products have higher upper range dermal unit exposure values ( mg exposure per unit of active ingredient applied) than hand wand, etc. Yet, the amount applied with ready to use products is relatively low, so this application technique seems the least important ( at least for many scenarios). However, questions remain regarding how the duration of exposure is considered; e. g., if the exposure is assumed to be instantaneous or spread over the duration of the application event.

The Panel commented on the following specific exposure scenarios that are not addressed by the Agency:

1. Omitting the hose end sprayer application method for ornamental/ tree sites may not represent a " conservative" approach. Not explicitly accounting for use on trees may thus be an important limitation of the Lifeline model, and could lead to underestimates of upper end residential use exposures for products applied to 107 of 113 trees. The Agency is encouraged to explore the sensitivity of their results to this limitation. 2. The Agency should account for pesticide use by property managers or commercial pesticide applicators. 3. Hand to mouth transfer of pesticides by applicators ( adults) due to tobacco smoking is a likely exposure pathway, but it is not included in the pilot exposure case study. Whenever the smoking information for adult applicators is available, this oral ingestion route should be incorporated in the assessment. Dermal and oral exposures are also likely to occur in children age 3 to 5 when N methyl carbamate pesticides are used in home gardens. Studies have indicated that children of parents who use pesticides in their home gardens ( either vegetable or flower) have significantly higher urinary OP metabolite concentrations than children whose parents reported no use of pesticides in their garden ( Lu et al. 2001). These two likely exposure routes should be included in the assessment. 4. The co occurrences considered by the Agency in this current analysis are limited exclusively to pesticide occurrence in media in and around the home. As a result, locations frequented by people outside the home are not considered. For children's exposure, important areas not yet considered might include schools and day care centers where a premium is placed on demonstrable hygiene. The Agency should at least discuss the possibility of exposures outside the home and the associated risks. 5. In the current assessment, exposures associated with household outdoor applications of N methyl carbamate pesticides require contact with the media treated by the applicant ( e. g., lawn, garden and vegetables). Several studies in the scientific literature have demonstrated that common residential use insecticides applied outdoors migrate into the home by being tracked inside ( Lewis et al., 2001; Stout and Leidy, 2000; Nishioka et al., 2001). The Agency should consider the importance of this potential exposure pathway in their continuing assessment of N methyl carbamate pesticides. 6. The present analysis correctly puts a large amount of effort into distinguishing between the age groups. This is important and needed because of the possible higher risk of young children. On the other hand, age is just one factor that can be considered. Others that could be considered include vegetarians versus non   vegetarians or other groups of persons with specific behavioral differences which cause heterogeneity. The simple PK model would allow this distinction since it adjusts for the incorporation of a number of experimental factors. 7. It is not immediately clear how certain pathways were omitted from the garden scenarios. The Agency should consider the tendency of children to mimic adult behaviors.

Question 3 The data sources and methodologies used in the N methyl carbamate case study are similar in many respects to the data sources and methodologies used in the 108 of 113 Cumulative Risk Assessment for the OP pesticides. For example, in both assessments the evaluation of exposure of the food pathway relied to a great extent on the USDA's PDP data, the evaluation of exposure through the water pathway used PRZM EXAMS modeling data, and the evaluation of exposure through the residential pathway used standard SOP algorithms along with label information, professional judgments, literature values, and survey data ( REJV, NHGPUS).

Please comment on the data sources used in the cumulative exposure assessment and on how EPA has considered and incorporated the data. Does the SAP have any suggestions or recommendations regarding additional available data sources that EPA may wish to investigate?

Panel Response The data sources used in the cumulative exposure assessment were those currently available. The Panel raised a number of concerns about the age and quality of the datasets and urged EPA to test the outcome of its assessment against the NHANES carbamate metabolite data as a means of reality testing. Concerns were raised about secular trends in pesticide use, the age of some of the data, how the data are used, how the data are interpreted, and the types of distributions developed from the data.

Sources Used in Assessment The case study presented to the SAP clearly demonstrated that available data, although limited, could be combined to conduct a cumulative exposure assessment for the N methyl carbamate pesticides. All 3 models were built upon the same datasets; therefore, the similar outputs, particularly at the high end exposure profiles from all models, are not surprising. While the improvement of these models is on going, the Agency should begin the process of validating all of the models in terms of adequacy and appropriateness by using a different dataset. The immediately available data for such an exercise are data collected by Bayer from their carbaryl turf application study. Assuming the study took place in the southeast region of North Carolina and therefore took into account the component of drinking water exposure in the model simulation, the outputs from these models can then be used as inputs to the PBPK model for estimating 24 hour average 1 naphthol levels. The modeled 1 naphthol levels can then be compared to 1 naphthol levels reported by Bayer. From here, the Agency could decide which model would provide the most robust and accurate prediction, and if not, what modifications are needed in the CRA models so that the gap between predicted vs. observed levels can be brought closer.

Comments Regarding Specific Issues 1. Secular trends: Several Panel members raised the issue of possible change in use patterns and trends away from using cholinesterase inhibiting pesticides. The Panelists question whether the Agency used any factors to adjust for this potential factor. There 109 of 113 have been significant secular trends in overall pesticide use with shifts away from acetylcholinesterase inhibitors because of their inherent toxicity. While there has not been an outright ban or prohibition of any carbamates, we are likely to see far less household use of such pesticides. At the very least, the mix of carbamates used in residential and agricultural settings has likely shifted. It is unclear in the background documents provided to the Panel what correction factors were used.

2. Age of data: There was considerable concern among the Panel about the age of data collected with the NHGPUS 15 years ago. One cannot assume that pesticide use practices and types of pesticides used for various applications are the same now as then. Also, the demographic distribution of responders in that study may not reflect current demographics and therefore may either underestimate or overestimate usage patterns. In addition, the choice of pesticide also may have changed in the past 15 years. Since NHGPUS was published around 1990, and likely represent data collected around 1988, it is quite dated.

3. Data used: Some of the data used are regionally specific ( water) while other appear not to be regionally specific. The regional use of data meant to be representative of the nation is inappropriate and likely to be error prone. Since the case study is in the Southeast, it is appropriate to try and use data specific to that region. The authors should be clear on the potential hazards of using such data on regions smaller than that for which they are truly representative.

This assessment uses food residue data collected over a nine year period, 1994 through 2002. A more recent and smaller window would be more appropriate. As stated in the document, the primary reason for this choice is to maximize the number of food commodities. With this design, there is a trade off between precision and bias that needs to be addressed. It would be interesting to see how robust the estimates are when early years are deleted.

Support for some of the assumptions may be made by correlating overall exposure with pesticide sales figures, at least if one collects data over a long period of time.

It was also felt that deconvolution methods should be used to impute individual serving exposure values from composite samples. 110 of 113 4. Data Interpretation: Observational data for children contact with pets are from a very small dataset and do not cover activities such as sleeping with pets. It also is based on a single period of observation per child.

5. Data robustness: It is important to get away from professional judgments that are more difficult to evaluate by outside reviewers and sometimes less transparent in how they were developed, and use existing data bases. Robustness of databases is always a concern, particularly when they were not developed specifically to meet the needs of the Agency for use in modeling exercises such as this one.

6. Uncertainty Analysis: It is very important to accompany the assessment with a substantial uncertainty analysis. The model comparisons of the exposure results are presented in Table 15 of the background document, Estimation of Cumulative Exposure from N methyl Carbamate Pesticides: A Case Study of 10 N methyl Carbamates, with six significant digits. It seems unlikely, for a host of reasons, that such precision really attends these numbers. Estimating the 99.9th percentile, for instance, at this precision would require many more replications than were likely deployed in the Monte Carlo simulations. The model uncertainty expressed by the tables suggests only one or two significant digits. There are, perhaps, even fewer after the measurement uncertainties and the sampling error that arises from having few data samples in some of the cells are accounted for.

The Agency attempted to quantify the reliability of the simulation results. It should be pointed out, however, that changing the numerical seed and re running simulations is not a sufficient way to conduct uncertainty analyses. At the very best, this approach can only assess the uncertainty arising from the use of a particular sequence of random numbers. We might call this the ` simulation uncertainty'. This is perhaps the easiest kind of uncertainty to address, but it is likely to be the smallest kind affecting the overall results; or at least can always be made to be the smallest, simply by increasing the number of Monte Carlo replications. The Panel emphasizes that the consequences for the reliability of the results attributable to model uncertainty, sampling uncertainty, and measurement uncertainty in general will likely be much greater, and will probably lead to the results having no significant figures at all. In such a case, an explicit uncertainty analysis is crucial to interpreting the import of the calculations. Measurement uncertainty may be especially hard to estimate because the various model approaches have been based on overlapping and essentially similar datasets.

The Panel voiced concerns about truncation several times. It was felt that the Agency should not truncate a parameterized distribution without a reasoned argument to justify this decision. The same issue arises with empirical distributions. Using an empirical distribution is perhaps the most egregious kind of unjustified truncation. As several Panelists mentioned, higher values are almost certain to be obtained than the original data contained if more samples are collected. It is impossible to know the impact of truncations unless an uncertainty analysis is conducted that addresses the consequences 111 of 113 of ignoring the tails. The Panel encourages EPA to continue to study the model stability and sensitivity.

One Panel member suggested that it is important to assess the appropriateness, sufficiency and precision of the outcome of this cumulative risk assessment for the Nmethyl carbamate pesticides. For example, are N methyl carbamate pesticides in drinking water alone capable of elevating the exposure profile during a certain period of time during a year for all ages of modeled individuals at the 95th percentile? The other interesting scenario about the outcome of this cumulative exposure assessment is that, assuming seasonal contribution of N methyl carbamate cumulative exposure from drinking water is real, and most, if not all, of the N methyl carbamate pesticide residues come from the seasonal agricultural uses in the modeled region, this seasonal effect is not reflected in the food component.

According to data presented in Table 16 of the background document Estimation of Cumulative Exposure from N methyl Carbamate Pesticides: A Case Study of 10 Nmethyl Carbamates, approximately 65% of total N methyl carbamate dietary exposure comes from a single food type, which is citrus fruits, including orange, tangerine, and grapefruit. If the availability of citrus fruits to the modeled individuals is limited only to a certain period of time in a year, say winter months, this seasonal effect obviously does not transmit to the total exposure profile. It was felt that longitudinal correlations across seasons are included in the analyses. There was discussion of the longitudinal nature of the PDP studies and the inclusion of foreign food items and items from other areas of the country ( i. e., Florida versus California oranges).

Diurnal patterns of exposure are very important for biomonitoring studies of exposure and effect and are not really addressed in the Agency assessments. If we measure a parent/ metabolite at one time of day, yet exposures vary across the day, we are likely to get substantially different inferences for exposure and risk depending on when the exposures occurred. A morning exposure may be completely eliminated by late evening. If a urine sample is taken in the late evening, the exposure inferred will be substantially smaller than what might be inferred from a noon urine sample.