Proceedings WORKSHOP ON EXPOSURE ASSESSMENT OF HAZARDOUS CHEMICALS Sponsored by: U.S. Environmental Protection Agency Industrial Environmental Research Laboratory Environmental Monitoring and Systems Laboratory Environmental Sciences Research Laboratory Research Triangle Park, North Carolina EPA Contract No. 68-02-3171, Work Assignment 2 Coordinated by: Radian Corporation 1 April 1980 '.0. 3ox 9948 / Austin, Texas 73766 / (512)454-4797 ------- CONTENTS 1. Introduction 1 2. Summary of Recommendations 3 2.1 Emissions Estimating Committee 3 2.2 Atmospheric Modeling. Committee 4 2.3 Population Estimating Committee 4 2.4 Statistics Committee 5 3. Workshop Summary: Emissions Estimating. ... 6 3.1 Workshop Recommendations. 6 3.2 Recommended Procedure for Evaluating and Improving Estimated Emissions Data. . 8 3.3 Obtaining and Documenting Emissions Data 15 3.4 Closing Remarks 17 4. Workshop Summary: Atmospheric Modeling. ... 18 4.1 Recommendations 18 4.2 Critique of Methods Used in Previous Studies 20 4.3 Committee Comments and Discussions. ... 21 4.3.1 Cost Effectiveness 21 4.3.2 Toxicity/Carcinogenicity 22 4.3.3 Documentation 23 4.3.4 Time Resolution of Emissions Data 23 4.3.5 Other Media, Occupational Exposures 24 ------- 5. Workshop Summary: Population Exposure Estimating 26 5.1 Summary of Recommendations 26 5.2 Recommendations for Population Exposure Assessment 28 5.2.1 Population Data 28 5.2.2 Activity Pattern and Time Budget 31 5.2.3 Population Mobility 31 5.2.4 Concentration/Population Data Interface 32 5.2.5 Cut-Off Point 33 5.2.6 Population Exposure Parameter. . . 34 5.3 Uncertainties in Estimating Population Exposure 35 5.4 Research Needs 36 6. Workshop Summary: Statistics 38 6.1 Recommendations 39 6.2 Statement of the Problem 40 6.3 Recommended Approach 43 6.4 The Three Levels of Analysis 44 6.5 Statistical Problems in Exposure Assessments 45 Appendices A Workshop Committees. • 46 B Workshop Attendees 49 C Exposure Assessment Documents Reviewed .... 50 D Individual Comments From Atmospheric Modeling Committee Members 52 E Regular Users of Census Data 67 ii ------- FIGURES FIGURE PAGE 1 Decision Tree for Estimated Emissions Data. . . 10 2 Comparison of Estimate Quality vs. Level of Effort 21 3 Exposure Level Not Within Hazardous Range 41 4 'Exposure Level -Within .Hazardous Range 41 5 Combination of Data to Estimate Hazard 43 Dl Model Selection and Implementation Procedure. . 57 iii ------- TABLES TABLE PAGE 1 Recommended Level of Sophistication in Each of the Six Elements to be Considered in Estimating Population Exposure 29 Dl Regional Annual Average Climatology for Level I Modeling Estimates 54 D2 Average Annual Climatology for Urban Area Sources 55 D3 Potential Sources of Error 61 LV ------- SECTION 1 INTRODUCTION The purpose of this workshop was to review the current methods of conducting an exposure assessment and to suggest any possible improvements. The result of an exposure assessment is an exposure estimate. An exposure estimate is an approximation of the number of people expected to be exposed to various con- centrations of the substance being investigated. It consists of three major portions: (a) estimating the emissions; (b) deter- mining the dilution due to atmospheric transport; and (c) esti- mating the number of people exposed to the various concentration levels. The workshop was divided into four expert working groups, one for each of the above subtopics and a statistics group. Initially, a statistician met with each of the groups. During the last day, the statisticians met as a group to suggest how confidence limits could be placed on the population exposure estimates. This document consists of reports obtained from each expert working group and a summary of recommendations prepared by Radian. The exposure assessment workshop resulted when EPA's Office of Air Quality Planning and Standards (OAQPS) requested that the Office of Research and Development (ORD) review the methods cur- rently used in developing an exposure assessment. Three EPA laboratories, the Environmental Monitoring and Systems Labora- tory - Research Triangle Park (EMSL-RTP), Environmental Sciences ------- Research Laboratory - RTF (ESRL-RTP), and the Industrial Envi- ronmental Research Laboratory - RTF (IERL-RTP), were charged with with performing this evaluation. William Petersen (E'SRL) , David Mage (EMSL), and William Baasel (IERL) were selected to coordi- nate the evaluation. Radian'Corporation was selected by the laboratories to conduct a workshop where non-EPA experts in the field would evaluate the current exposure assessment procedures and make suggestions for their improvement. Three levels of exposure assessment were evaluated by work- ing participants. Level I assessments are essentially screening studies, which assist in identifying chemicals that may warrant further investigation.. Level II studies are used to support a listing decision and source prioritization for regulation under Section 112 of the Clean Air Act. Level III assessments employ more sophisticated modeling techniques and require very accurate emissions and population data. Regulations for new and existing sources of listed chemicals are based on Level III assessments. Level III assessments form the basis for determining the neces- sary degree of emission control. Prior to attending the workshop, each participant was asked to review the thirteen reports listed in Appendix D. With the exception of the report on chloroprene, which was a Level I report, all were Level II reports. At the meeting, the draft report of the modelling section of a Level III report was pro- vided for participants' review. The workshop was held December 11-13, 1979, at the Ramada Inn-North in Durham, North Carolina. ------- SECTION 2 SUMMARY OF RECOMMENDATIONS Some of the major recommendations from each of the work- shop committees are summarized in this section. 2.1 EMISSIONS ESTIMATING COMMITTEE • Complete emissions results should be provided in each exposure assessment report. The emissions data base should in- clude, when possible, the sampling and analytical procedures used to obtain the data. Additionally, the report should in- clude a description of plant process conditions, emission con- trol equipment, and production capacity at the time of sampling. The range and distribution of emissions data should be given. • Industrial groups should be asked to comment on the emissions data presented in each exposure assessment report. This may be done either before the report goes to EPA for review, or during the time in which OAQPS is reviewing the report. Industry review should take place at all three levels of assess- ment. Whenever specific plant data are used in a report the data should be provided to the facility's manager for comment. If the data are verified by plant personnel, less chance of litigation exists. ------- 2.2 ATMOSPHERIC MODELING COMMITTEE • The types of models recommended in the OAOPS Guideline Series, "Guideline on Air Quality Models," should be used in Level II studies. • Level III studies should use the best quality atmo- spheric models currently available. The modeling committee was unable to agree on recommendations for a Level I effort. Section 4 describes committee members1 differing ideas. 2.3 POPULATION ESTIMATING COMMITTEE • MEDLIST (Master Enumeration District List) population data should be supplemented by a population growth factor, U,S, Geological Survey population data, other census population data (e.g., the 4th count population data), and special population survey data. • Human activity information such as time spent indoors/ outdoors and physical activity levels (resting, working, or exercising) should be incorporated in population exposure esti- mating. • Population mobility, such as daily migration from resi- dence to a work place and seasonal or other periodic patterns of movement should be considered in population exposure estimat- ing. • Concentrations of chemicals should be estimated at a population centroid instead of the other way around. ------- • Instead of restricting a population exposure study area to an arbitrary distance of 20 km from a source, the study area should be extended to a distance at which an annual mean concen-^ tration due to that source decreases to a diminutive level. • Results of population exposure analyses should be re- ported in a manner independent of an arbitrary assumption of a particular dose-response function. 2.4 STATISTICS COMMITTEE • Use a quantitative approach based on expected values and 95 percent confidence limits of the individual elements of the analysis to estimate exposure levels and their associated con- fidence limits. • The precise definition of the three levels of analysis should remain flexible to allow for the contractor to allocate his resources in the most cost-effective manner. • The definition of exposure should include continuous and correlated space and time variations. ------- SECTION 3 WORKSHOP SUMMARY: EMISSIONS ESTIMATING This chapter contains the emissions estimating committee's recommendations for improving emissions estimates in future EPA exposure assessment studies. It also contains a recommended procedure for evaluating emissions data used in exposure studies. Additionally, this chapter lists potential sources of emissions data-and the -kind of information that should be recorded. 3.1 WORKSHOP RECOMMENDATIONS After reviewing and discussing available EPA exposure assessment documents, the emissions estimating committee developed the following recommendations. Specific sources of emissions data should be given in each report. The report should state if the data are from litera- ture, regulatory inventories, or industrial records and if these data are a result of estimates, emissions factors, mate- rial balance calculations, or sampling. Such documentation of emissions data will be useful if future studies require addi- tional data. When specific data obtained from plant sampling are used, the analytical procedure, frequency of sampling, and process conditions should be included, if possible. ------- The year that emissions data were obtained and the plant production capacity at the time of sampling should be stated. The emissions data for each source should be listed as a range of values in addition to a single numeric measure of cen- tral tendency. The basis for the range should be given. The range should reflect the precision of the available data, as well as the possibility that all emission sources have not been included in the available data base. A range of emission values is necessary to quantitatively indicate the level of certainty associated with the emissions data. Providing a range of values may require an increased level of technical effort for determin- ing emissions, may potentially involve personal judgments during the initial level of effort, and may result in industry's con- cern that only the high value of the range will be used. How- ever, in addition to a measure of central tendency, a numerical range is the best way to alert the users of the data to the inaccuracies involved. If the final risk assessment indicates that a greater accuracy in the emissions data base is required, the improved accuracy would then be reflected in a narrowing of the range. At each level of technical effort, a decision must be made concerning the adequacy of the emissions data when combined with the precision of the atmospheric dispersion modeling effort and the population exposure estimates. If emissions data are judged inadequate, better emissions data must be collected. Emissions data for a screening-level effort might be based on literature and regulatory inventories data for generalized plants and in- dustries. For technical efforts which may result in standards development, emissions data should include site specific infor- mation (e.g., individual stack parameters) which will require an improved data base. For the most detailed atmospheric dis- persion models and population estimates, the emissions data should include daily and seasonal variations in emissions. 7 ------- As the accuracy of atmospheric dispersion modeling and pop- ulation exposure estimates is increased, more accurate emissions data may be necessary. Emissions data bases should be improved by expanding the data base sources. For example, more extensive literature searches, additional industrial input, and added information from regulatory agencies may provide more data. Industry data obtained under Section 114 of the Clean Air Act, ambient monitoring data, or source sampling data may be neces- sary to expand the emissions data base. The report at each level of technical effort should include specific recommenda- tions for improving the emissions data base. The sources of emissions data must be thoroughly documented to. withstand the pressures of litigation. This may require that estimates and historical data be improved by requiring industry to sample at specific sites. At each level of technical effort, a copy of the data should be provided to industry for review. An explanation of its potential end' use should accompany the data. If industry reviews the data, the accuracy of the data base may be improved, and potential conflicts may be resolved early in the evaluation process. Additional review of emissions data by trade and profes- sional associations should be considered. 3.2 RECOMMENDED PROCEDURE. FOR EVALUATING AND IMPROVING ESTIMATED EMISSIONS DATA The degree of uncertainty involved in an exposure assess- ment cannot be defined before the assessment is started. In some cases a wide range or broad confidence interval in the estimated emissions data can be tolerated. Even with a wide range, the results of a Level I study of exposure assessment 8 ------- may allow a clear decision regarding further work on a partic- ular pollutant. In many cases, however, refinement of the emissions data will be required as the assessment is pursued. The repetitive proces.s involved in this refinement has been illustrated in a decision tree prepared by the emissions estimating committee. The decision tree is illustrated in Figure 1. In the initial stages of a screening level assessment, several factors must be evaluated for any given pollutant. (During the workshop, the screening level assessment was referred to as a "Level I" effort.) These factors include: 1. The types of sources (point, fugutive, storage, area, etc.) of the pollutant, 2. The physical characteristics of the sources (height, velocity of emitted stream, temperature, etc), 3. The geographical location of the manufacturers and users, 4. The location, in some cases, of individual sources within a plant, and 5. The estimated emissions rates of the particular pollutant of concern. As shown in Figure 1, .estimated emission rates can be obtained from numerous origins (block 1). Potential leads for first estimates of rates include technical literature, indus- trial data, EPA reports, and files of local, state and federal regulatory agencies. The emission rates obtained must be quan- tified. At a minimum, a mean and a range of values for the emission rate'must be defined. To understand why a given emission source may have a widely varying composition requires an understanding of variables which ------- Figure 1. Decision tree for Level I and II assessments 10 ------- affect the emissions. As shown in the decision tree, the esti- mated emission or range of the emission rates must be judged for adequacy after the emissions data source is first examined (block 2). The estimated emissions data are inadequate if the preliminary analysis of the data and/or data origins does not provide, as a minimum requirement, the mean and the numerical range of emission rates. In this situation, more data must be obtained. This improvement can be accomplished by expanding the data search to include additional resources (block 3). These resources can include industry contacts, trade and professional associations, files of additional government agencies, manufac- turers and vendors of equipment and processes, and more detailed engineering evaluations of the emissions source. This further effort must result, at the very least, in a mean and a numerical range for the estimated emissions before the assessment can pro- ceed as shown in Figure 1. If the emissions estimate can be judged to be adequate enough to proceed (block 2), the emissions estimate should be sent to industrial representatives for their review and comments (block 4). These representatives should include those companies or plants which emit the particular pollutant being studied. The estimated emissions data should be sent to as many of the sources as possible within the effort's budget and time con- straints. Any company whose emissions data were used in de- veloping the estimated emissions rate should have the opportu- nity to comment on these data. It is also suggested that the estimated emissions data be sent to related associations and trade groups for review (e.g., the Technical Association of the Pulp and Paper Industry - TAPPI - or the Electric Power Research Institute - EPRI). Even for small sources, such as dry clean- ers, trade associations may have useful comments on the data. If documented industrial review results in the data's being judged inadequate, all data should be carefully re-examined 11 ------- (block 5). The data should be reviewed with regard to the basis and methodology used to develop them (material balance, sam- pling, vendor specifications, etc.)- If a technical assessment of all the data indicates the need for an adjustment in the . estimated emissions rate, this change should be made. Addi- tionally, if resources permit, industrial review of the revised estimated emissions rate can be sought again. It should be obvious that this repetitive process cannot be continued in- definitely. Time and economic constraints will govern the extent to which this process can be pursued. The initial dispersion modeling effort could be performed concurrently with the industrial review (block 5). Changes in estimated emissions rates could easily be accommodated at inter- mediate stages in the modeling (block 7). After emissions estimates have been reviewed by industry (and adjusted if necessary) they can be used in the atmospheric dispersion model in conjunction with the population estimates to yield the screening level exposure assessment (block 7). The results of this initial assessment will indicate if further refinements of estimated emissions are needed at the screening level of effort (block 9). Three possibilities may exist. With the existing precision of the atmospheric model, population estimates, estimated emissions rate, and health assessment, it may be obvious (even in the worst case) that the exposure to the particular pollutant is below the harmful to health level. In this case the pollutant can be eliminated from further consideration. On the other hand, the exposure assessment may show, even under best case conditions, a high exposure and a high risk when considered with the health assess- ment. The pollutant must then be assessed at a more intensive level of technical effort which could be the basis for setting 12 ------- a standard. (During the workshop this type effort was generally referred to as "Level II" effort.) The third possibility is that the results of the screening level of assessment are not accurate enough for a clear decision regarding the further consideration of a particular pollutant (block 13). In this case, a decision must be made on the improvement of the accuracy of the estimated emissions, The part of the assessment to be improved will generally be decided on the basis of one or more of the following factors: cost of improvement required and available time probability of success degree of probable improvement in accuracy However, a decision to improve the estimated emissions data or concentration profiles may require that more sophisti- cated techniques be used than were initially applied. These could include the acquisition of more specific data from in- dustry (through unofficial request or through Section 114 let- ters) , the actual sampling of some representative emission sources, and/or through ambient air monitoring (blocks 3 and 15). When the data have been acquired, the estimated emissions should be revised to reflect any-increase in accuracy gained from the additional effort. The revised emissions estimate should then, ideally, progress through the steps shown in the .decision tree, starting with submission of the revised estimated rates to industry for review. At some point in the repetitive process, the precision of the exposure assessment should be suf- ficient to justify a decision regarding the further considera- tion of the pollutant being studied. It should be clearly recognized that, in the extreme, the recommended procedure for obtaining estimated emissions rate 13 ------- data for even a screening exposure assessment could require a substantial funding and technical manpower commitment. In the majority of cases, however, very few repetitions are antici- pated for the screening assessment. When the screening level of effort results in a more exten- sive assessment for a pollutant, the adequacy of each part of the exposure assessment must be evaluated as to its ability to support standards. If the dispersion model and population estimate are adequate, the adequacy of the estimated emissions data must be determined (blocks 13 and 14). If emissions data are determined to be inadequate, the basis for estimating the emissions rate must be improved and expanded (-blocks 3. and 15) . Data could be -obtained under Sec- tion 114 of the Clean Air Act, or from an ambient sampling source. If data from the screening effort are judged adequate, they should be sent to industry for review. Potential conse- quences of the assessment should be conveyed to industry at this point (blocks 10 and 30), Industry review should also in- clude review by appropriate professional and trade organiza- tions, such as (but not limited to) American Institute of Chem- ical Engineers, American Petroleum Institute, or American Society for Testing and Materials (block 11). For generic sources such as dry cleaners or gasoline service stations, it will not be practical to send data for each to review. In that case the use of trade organizations should be actively pursued. After industry data review, EPA must again determine the adequacy of the emissions data (blocks 12 and 24). If data are judged inadequate, the data base must be improved (blocks 3 and 23). If data are judged adequate, they should be combined with the dispersion model and the population estimate (blocks 6 and 18). 14 ------- When estimated emissions data are linked with population estimates through dispersion modeling, the resulting exposure assessment is combined with the health assessment to determine the risk caused by the pollutant under study. At this point, the results must be validated. Three possibilities exist. The results may indicate that the health risk is below the value which requires a standard for control. In this case, the study is complete. Alternatively, the results may indicate that the health risk is above the value judged to require a standard for control. In this case, EPA may recommend regulation to the Science Advisory Board. If the results are judged inadequate to support a recom- mendation to the Science Advisory Board, a decision must be made-as to which .part of the study needs improvement. If the dispersion model and population estimate are judged adequate and the emissions data inadequate, improved data must be obtained (blocks 3 and 15). If the emissions data are adequate, either or both the model and population estimate must be im- proved. This will involve a higher level study (blocks 13 and 25). 3.3 OBTAINING AND DOCUMENTING EMISSIONS DATA The information presented in the reports reviewed during the workshop did not provide an adequate basis for judging the quality of the data. The following paragraphs contain sugges- tions for obtaining and documenting data so that their adequacy may be assessed. The report from which the data were taken should be ref- erenced. Additionally, the original developer and reporter of the data should be noted, if different from the reference report. Data included in the reports which are excluded from 15 ------- consideration should be noted. The reason for exclusion should be given. Information obtained should include the time period of the base data; the time period for any projection; the capacity of the process (design, nominal, actual); any sampling and monitor- ing information, site visits, written and verbal communications, and/or engineering estimates; the types of emission sources in- cluded (point, fugitive, storage). The emissions data sources listed below comprise a starting list of sources and types of information which may be useful. The extent to which these and other sources are used will depend on the quality of data needed to provide compatible results with the other parts of an exposure assessment. Sources of data include: trade/technical journals industry publications licensor specifications federal, state, local and international regulatory agencies industry contracts If possible, the data should include emission rates for point, area, and fugitive emissions, as well as emissions from storage and waste disposal. If data are not available from one or more of these sources, the data gaps should be clearly identified. Necessary physical characteristics of each source must be included. A partial list of source characteristics includes: chemical composition of the emission physical state of the emission 16 ------- temperature, pressure, velocity, etc. of the emission height above grade of the emission source; plot and plan views of surrounding structures. The extent of source characteristic details to be used will vary as the level of technical effort changes. For the Level I efforts, general or representative characteristics can be used. For Level III efforts (especially in coordination with detailed atmospheric modeling efforts), individual site specific data will- be required. When the emissions rate varies in a distinct pat- tern (e.g., seasonal or daily,) these data should be presented consistent with the detail of the dispersion modeling and popu- lation estimates to be used. 3.4 CLOSING REMARKS Emissions data, combined with estimates of population, atmospheric dispersion, and health effects, must be used by EPA to obtain an accurate risk assessment for pollutants under study. Budget, time and other limitations will cause the accuracy of the risk assessment to change for each pollutant. When the objective is only to screen and give priority to a list of pollutants, the quality of the estimated emissions data does not need to be the same as that required when the objective is to provide a basis for setting a standard. As a consequence, the amount of detail and effort expended to estimate emissions varies. For each pollutant being studied, there will be many deci- sions required to obtain data of the desired quality. It should be emphasized that when the effort may lead to the setting of a standard, the estimated emissions rate must be documented so that it can withstand the trials and pressures of litigation. The emissions estimating committee developed its recommendations as a guide to assist EPA in providing adequate data for future risk assessment studies. 17 ------- SECTION 4 WORKSHOP SUMMARY: ATMOSPHERIC MODELING The atmospheric modeling committee reviewed the exposure assessment reports provided by EPA and discussed at length a number of factors influencing selection of a modeling approach. Members of the committee could not agree on the best modeling approach for Level I studies. However, the committee was able to recommend approaches for Level II and III efforts. Section 4.1 of this chapter contains the committee's recom- mendations for Level II and III exposure assessments. Addition- ally, committee members' differing opinions as to Level I modeling approaches are summarized. Modeling methods used in previous exposure assessments are critiqued in Section 4.2. Section 4.3 summarizes committee discussion throughout the meeting. In- depth comments by individual committee members are included in Appendix D. 4.1 .RECOMMENDATIONS The atmospheric modeling committee recommended atmospheric modeling approaches for Levels II and III exposure assessments. These recommendations are summarized below. The types of models recommended in the OAQPS Guideline Series, "Guideline on Air Quality Models," should be used in Level II studies. This document describes various models suitable for many situations. The selection should be based upon at least 18 ------- a brief study of the local situation. The meteorology to be used in such a model application should be the best meteorolog- ical data for the particular area which can be obtained by phone and mail. Normally this would be the STAR from the nearest site for which this compilation is available. However, if another meteorological data base is more suitable, it should be used. If the site has high terrain or is on a lake front or ocean front or in a deep valley which perturbs the simple meteorology normally used in these models, more complex meteorological data should be obtained, if the budget allows. Level III studies should use the best quality atmospheric models currently available. The current "state-of-the-art" con- sists of models somewhat more advanced than those described in "Guideline on Air Quality Models." Level III studies should consider site-specific meteorological data, and all of the com- plexities of the interaction of plumes and structures. The modeling committee could not agree on a recommendation for a Level I modeling approach. Some members felt that a simple, uniform meteorological approach should be taken. This type of approach (but not necessarily the details of the approach) is exemplified in the SRI International reports reviewed during the workshop.. SRI used a set of dispersion estimates which can be reduced to a single curve on a plot of (concentration/emissions) vs. radial distance from the source. Some of the group felt that such a plot could be computed for average meteorology for the United States with several possible lines indicating different stack heights. If this approach is used, EPA should prepare the best possible single-estimating nomogram or chart and request that it be used by all contractors preparing Level I assessments. Some committee members thought that limited site-specific terrain and meteorology should be considered in addition to the 19. ------- simple approach described above. If there is complex terrain or extremely unusual meteorology at the site, the simple nomo- gram of (concentration/emissions) vs. radius should be modified to account for this. Other members of the committee felt that under no circum- stances should a simple meteorological dispersion approach ever be used for any meteorological dispersion calculations. These members felt that an approach similar to the Level II approach recommended by the committee be used for Level I as well. This view is explained in more detail in Appendix D. 4.2 CRITIQUE OF METHODS USED IN PREVIOUS STUDIES During the workshop, the modeling committee reviewed 13 reports describing previous EPA exposure studies. These reports are listed in Appendix C. This section contains the committee's comments regarding the modeling approaches described in these reports. The committee felt that the modeling methods and assump- tions were not adequately documented. This was particularly true in the case of the SRI International reports which refer the reader to calculations made by OAQPS. While those calcula- tions may be of the higest quality, there was not enough in- formation presented in either the reports or in any document cited to allow an outside observer to understand in detail what was done and form an intelligent judgement as to whether it was done properly. OAQPS identified some reports as being Level I reports; others were identified as Level II reports. It appeared to the committee that several Level II studies had dispersion modeling suitable for a Level I study, while some assessments in Level I used models suitable for Level II. Mr. Suta, of SRI 20 ------- International, indicated in his presentation that he had not heard of the distinctions between Levels I and II before the workshop. Thus, this is not a criticism—explicit or implied-- of his work or of the work in the SRI documents; rather it is an indication that there should be agreement as to what will be presented in future Level I and II studies. 4.3 COMMITTEE COMMENTS AND DISCUSSIONS 4.3.1 Cost Effectiveness For each level of study, there will be distinct budget limi- tations. EPA's objective is to select that level of study which produces the most useful, reliable, and accurate information within these budgetary constraints. The committee questioned how much our information will improve as we go to increasing levels of complexity. One panel member suggested that the accuracy of the information as a function of the budgetary expenditure has the shape shown in Figure 2. en 01 u CO Cfl W 14-1 o CO o* Expenditure (manpower and financial) Figure 2. Comparison of estimate quality vs level of effort. If that is true, then extreme expenditures are probably not justified. One committee recommendation for future studies would 21 ------- be that whenever a Level II study is done on a pollutant for which a Level I study has been done, the Level I study should be included as an appendix. The change in exposure estimate as a result of going from Level I to Level II methodology should be made clear in this report. Similarly, if a Level III study is done for a chemical which has had a Level II study, a similar comparison should be reported. This will not be of great help in the particular set of modeling exercises, but may contribute to our understanding of the shape of the curve shown in Figure 2. 4.3.2 Toxicity/Carcinogenicity EPA has indicated that future exposure assessment studies will be conducted using the most conservative dose-response relationship for a carcinogenic sub-stance. The committee assumed that the probability of an excess cancer is proportional to the lifetime inhalation or ingestion of the material, independent of dose rate. This is obviously a "worst case" assumption which has interesting consequences for modeling because it indicates that there is no concern whatever for short-term, high-concentra- tion dosages. This is the exact reverse of the situation with criteria pollutants, which have a toxicological type assumed response. Our recommendations for models generally direct the attention to annual average concentrations whi'ch are suitable for this type of assumption. However, we recommend that, at least in Class III studies, the short-term concentration be determined if it is convenient. A natural consequence of this worst-case dose-response assump- tion is that there is no concentration level so low as not to be of interest. We are inherently in the situation where long-range transport is of significance. Any arbitrary choice such as "we will terminate the dispersion calculations and exposure calcula- tions at 20 miles" or "we will terminate the exposure concentra- tion to 1/100 ng/m3" is not consistent with this dose-response 22 ------- assumption. Such arbitrary cut-off points may result in grossly underestimated exposure concentrations. The committee felt that efforts should be made to estimate removal mechanisms and removal rates for potential airborne carcinogens. Based on these efforts, decay constants for each chemical should be factored into the dispersion model. Use of such a constant will allow modeling to extend to boundaries at which concentration-exposure levels become negligible compared to those close to the emission source. 4.3.3 Documentation The committee strongly urged that in any studies which will be used for regulatory purposes, the models and meteorological data used must be adequately documented. Although proprietary models whose details are not revealed to the public may be use- ful for other applications, the committee felt that any modeling whose quality may be examined by a court of law or challenged before a regulatory agency should not be done by such a model. This implies a strong preference for models such as those listed in the EPA Guideline Series whose details are widely known and understood. However, if a contractor is willing to document a proprietary model, then it should be considered. 4.3.4 Time Resolution of Emissions Data If it is possible to obtain emission estimates which are resolved by time of day and season of the year, that would be most helpful. It is an observational fact that in many areas of the country the wind directions are strongly biased by season and time of day. (For example, in Salt Lake City during most of the year, the wind is from the north during the daytime and from the south at night.) Our current level of modeling sophistication 23 ------- is adequate to take advantage of information about time resolu- tion of emission data if those data are available. 4.3.5 Other Media, Occupational Exposures The completed exposure assessment reports reviewed by the committee suggest that EPA intends to consider only exposures to the populace from airborne pollutants which are inhaled. That may be the best approach for the present. However, exam- ples are known in which pollutants enter drinking water and food by passing through an airborne state; for example, lead which enters drinking water and food in particulate fallout from the atmosphere. Certainly for any Level III study, the question of whether the atmospheric removal processes for this material will cause it to enter drinking water or food should be examined. Similarly, it is possible that air pollutant control mea- sures intended to minimize atmospheric exposure will have the consequences of increasing occupational exposure. Normally, both are decreased by such measures, but examples can be imag- ined in which reducing atmospheric exposure would result in more tightly enclosed factories with the result that the indoor concentrations would increase, etc. This possibility should be considered in any study leading to detailed regulations. Finally, the possibility exists that in -attempting to solve an air pollution exposure problem, we may create a water pollu- tion problem. The converse is also true. For example, the reports on cadmium show that municipal incinerators are a major source of atmospheric cadmium. There we are solving the solid waste problem and creating an air pollution problem. Similarly, it seems that with limestone scrubber sludge, we are solving an air pollution problem and creating a solid-waste problem. In a study leading to detailed regulation, the technological 24 ------- possibility of such intermediate transfer of pollution should be considered. 25 ------- SECTION 5 WORKSHOP SUMMARY: POPULATION EXPOSURE ESTIMATING The population exposure estimating committee discussed the exposure assessment methodology, as well as the primary elements that are needed to adequately quantify exposures of .the general population to a given toxic substance. The committee reviewed the thirteen reports of population exposure assessment provided by the U.S. Environmental Protection Agency. After long', intense deliberation, the committee has arrived at several recommendations and caveats for population exposure assessment. These are discussed in the following sections. Sec- tion 5.1 contains a summary of the recommendations made by the committee. A more detailed description of each of the recommen- dations is given in Section 5.2. Sources of uncertainties in the population exposure estimating are discussed in Section 5.3; in Section 5.4, further research needs for improving population exposure estimates are described. 5.1 SUMMARY OF RECOMMENDATIONS The population estimating committee recommends that the following six improvements be incorporated in population expo- sure assessments made in the reports reviewed. 26 ------- MEDLIST (Master Enumeration District List) population data should be supplemented by a population growth factor, U.S. Geo- logical Survey population data, other census population data (e.g., the 4th count population data), and special population survey data. Human activity information such as time spent indoors/out^- doors and physical activity levels (resting, working, or exer- cising) should be incorporated in population exposure estimating. . Population mobility, such as daily migration from residence to a work place and seasonal or other periodic patterns of move- ment should be considered in population exposure estimating. Concentrations of chemicals should be estimated at a popula- tion centroid instead of the other way around. Instead of restricting a population exposure study area to an arbitrary distance of 20 km from a source, the study area should be extended to a distance at which an annual mean concen- tration due to that source decreases to a diminutive level. Here, a diminutive level is a level of chemical-specific concentration, life-time exposure to which does not pose any significant health risk to the public. Results of population exposure analyses should be reported in a manner independent of dose-response functions. To make the results usable for a nonlinear model, as well, the numbers of people exposed to various annual concentrations should be reported in either a histogram (i.e., for each concentration interval) or a cumulative distribution (i.e., at or above various concentra- tion levels). 27 ------- 5.2 RECOMMENDATIONS FOR POPULATION EXPOSURE ASSESSMENT Our discussion of population exposure assessment identified six areas where additional efforts appeared to bring about a significant improvement in either population exposure estimates or the utility of estimated population exposure for a final health-risk assessment. These areas are: 1. Population Data 2. Activity Pattern and Time Budget 3. Population Mobility 4. Concentration/Population Data Interface 5. Cut-Off Point 6. Population Exposure Parameter This section discusses how each of the six elements affects esti- mated population exposure to a given toxic substance and provides some recommended methods for improving estimates of population exposure as well as estimates of each element. Table 1 lists recommended improvements as a function of the sophistication level of the exposure assessment. 5.2.1 Population Data The census data tapes called "MEDLIST" provide a convenient population data base, indicating where and how many people reside, down to the enumeration district/block group (ED/BG) level. An enumeration district is the smallest census statistical area and is usually covered by a single census surveyor. But, even though MEDLIST provides a reasonable spatial coverage of population dis- tribution all over the United States, its population estimates are, by now, nearly 10 years old and need to be updated. The commit- tee does not believe that MEDLIST alone can provide adequate information on either the current (instead of 1970) population or the dynamic (instead of stationary resident) population. 28 ------- TABLE 1. RECOMMENDED LEVEL OF SOPHISTICATION IN EACH OF THE SIX ELEMENTS TO BE CONSIDERED IN ESTIMATING POPULATION EXPOSURE Element Level I Study Level II Study Level III Study Population MEDLIST (census) Estimate of cur- rent population, national growth factor. U.S.G.S. improve- ments, other pri- vate sources (state or county level). Local estimated growth factors. 4th count (tract data (population mix) Activity Type (micro- environment) NO Mobility (displace- ment) Concentration/ Population Interface Cut-Off Point Exposure Parameters NO Concentrations to be interpolated into ED/BG Centroids Up to a distance at which concen- trations .due to a given source decrease to a diminutive level 1. (people x cone.) 2. No. of people exposed to specified cone. interval YES (general) YES (general) Concentrations to be interpolated into ED/BG Centroids Same YES (detailed) YES (detailed) Distribute popula- tion within ED where concentra- tion gradients are high. Same ., 2. and 3. Sum of cone. from different sources before computing expo- sures to a given pollutant 1., 2., and 3. 29 ------- Population estimates are updated annually by the Bureau of the Census by taking random samples from a few percent of the entire population. Results are summarized down to the number of people residing in each county and by the so-called population growth factor, i.e., the current population relative to the 1969- 1970 census-year population. The committee considers estimates of current population in a study area made by taking a product of the 1970 population and the national growth factor adequate for Level I studies. For Level II and III studies, however, the committee recom- mends use of a more region-specific growth factor for estimating the current population. Because population growth takes place nonuniformly over a study area, a search of spatially resolved growth factors or current population estimates is encouraged. Potential sources of such population data include local planning agencies, local Chambers of Commerce, local/state real estate boards, the U.S. Geological Survey (U.S.G.S.), the Bureau of Economic Analysis, and the Bureau of the Census. A great deal of assistance can sometimes be obtained from regular users of the census population data whose names and addresses are ,listed in Appendix E. It should be noted that the U.S.G.S. population estimates may be more appropriate in rural areas in which the Census Bureau estimates of population do not have adequate spatial resolution to accurately locate the rural population. For Level III studies, it might be advisable to use the 4th count census data in order to obtain detailed population characteristics by age, sex, etc. It could also be used to perform specific site surveys which would provide an up-to-date distribution of population in the vicinity of a given set of sources. 30 ------- 5.2.2 Activity Pattern and Time Budget Population exposure analyses conducted in the thirteen reports reviewed by the committee are based on the implicit assumption that people are exposed to ambient air 24 hours a day at their residence location. This 24-hour ambient-exposure assumption greatly simplifies a population exposure analysis. However, it may not be appropriate when concentrations of a study chemical are expected to be different between indoors and out- doors or when diurnal patterns of human activity levels and ambient concentrations are correlated. Because many people both live and work indoors, information on human activity pattern and time budget (i.e., how long and what time of the day people stay indoors or outdoors) is needed to make a realistic population exposure estimate. For a Level I study incorporation of this information may not be necessary. But it should be incorporated into a more elaborate population exposure analysis, particularly for the Level III study. 5.2.3 Population Mobility Population mobility also poses a problem for a population exposure analysis. The population in any given area is not static, and its* size and spatial distribution exhibit consider- able diurnal and seasonal variations. Many people commute to the place of their employment, go to school, or visit a resort area. At these places they may be exposed to pollutant con- centrations different from those at their residence. In a large metropolitan area, the diurnal shift of the population from residence to work place is quite large. With a sharp gradient of pollutant concentrations over the metropolitan area, consideration of population mobility becomes quite impor- tant for correctly estimating exposures of the population to any given chemical. Cyclic movements of the population occur not 31 ------- only within a day but also from season to season and year to year. For Level II and III studies this mobility consideration should be incorporated into the population exposure analyses. In particular, such a consideration'becomes essential for esti- mating exposures of employed adults who commute to places where air quality is very different from that at their residence. 5.2.4 Concentration/Population Data Interface To obtain a population exposure estimate requires a proper interface of population and concentration data in space and, for a more elaborate analysis, in time, too. Concentration is con- tinuously distributed over space and its value at a given loca- tion can be estimated by air quality simulation models or from nearby air quality monitoring values. Population, however, is distributed in a discrete manner and is incorporated into a statistical area. Furthermore, without knowing a detailed housing pattern over a study area, it is not possible to cor- rectly estimate the population in a particular area from popu- lation figures of nearby statistical areas. Population esti- mation differs in this way from concentration estimations which can be accomplished by using data from adjacent areas. With .these Limitations in mind, the committee recommends estimating a concentration at a population centroid into which local population is aggregated, rather than the other way around. When MEDLIST data are used, a concentration at the population centroid of each enumeration district should be estimated. Then, according to the concentration value, values of population expo- sure parameters should be calculated. If the population data are aggregated into a larger area (a rectangular or segmented con^- centric area is often used), a concentration at the population centroid of that area should be used for population exposure 32 ------- calculations. Using a concentration at the population centroid is preferred over using a concentration at the geographical cen- ter. 5.2.5 Cut-Off Point In computing a population exposure parameter, a problem arises as to where such calculations should be determined. Without natural sinks for a toxic substance, the concentration never decreases to absolute zero. Therefore, the calculated number of people exposed or the product of exposed population and concentration keeps increasing as the study area is enlarged. This problem of monotonically increasing results in population exposure estimates comes from the basic assumption of a linear dose-response function for toxic substances. In all the reports reviewed, the area in which measureable population exposure takes place is bounded by an arbitrary dis- tance, e.g., 20 km from the source. This cut-off criterion ap- pears to be too arbitrary and may result in missing a signifi- cant portion of the population who are exposed to a low but not negligible level of concentration. Suppose a source is located 25 km upwind of New York City. Then, the exposed population or the population-concentration product of New York City may ex- ceed that of the 20 km study area around the source. To avoid the potential problems outlined above, the commit- tee strongly recommends exploring better criteria for the cut- off point. One possible criterion may be a distance at which a concentration due to a given set of sources drops to a diminu- tive level, e.g., 0.1 ng/m3 for chemical i. Here the diminutive level should be chemical-specific and should be determined by considering the toxicity of that chemical. A diminutive level may be defined as a low concentration at which level an average person can withstand exposures to the chemical throughout his 33 ------- life without increasing any measurable health risk. 5.2.6 Population Exposure Parameter Population exposure to a toxic substance is usually re- ported in two quantities: the number of people exposed, and a product of people exposed x concentration. Basically, these two parameters appear to be adequate for quantifying population exposure. However, values of the two parameters should be com- puted for various concentration intervals rather than for an entire range of concentrations. The linear dose-response model leads to the practice of ex- pressing exposures as the-..product-of ,annual, average concentra- tion times population. A nonlinear model, however, would not necessarily be compatible with this measure. It is therefore recommended that all exposure analyses also report the numbers of people exposed to various intervals of annual average concen- tration or, alternatively, the number of people exposed to con- centrations at or above various concentrations (i.e., a cumula- tive distribution). These values are independent of any partic- ular dose-response model and may be used with any future dose- response models that may be developed. For source-related population exposure estimates, people may be counted more than once: first as exposed to a concentra- tion due to one source; second as exposed to a concentration due to another source; and so forth. Such double or triple count- ings of people do not make any difference as long as a linear dose-response function holds. To make the population exposure estimates useful for a more general dose-response function, total exposure of the population to a combined concentration due to several sources or types of sources should also be computed. 34 ------- 5.3 UNCERTAINTIES IN ESTIMATING POPULATION EXPOSURE In estimating population exposure to a toxic substance, many assumptions and simplifications have been employed and will also be used for future studies. However, uncertainties involved in estimating population exposure must be explicitly stated. Sources of uncertainty in the estimation of exposed populations include the following parameters: 1. Population Estimations - Data from the Census Bureau and from private sources make available ways of esti- mating the uncertainties involved in the population estimations. These uncertainties are known and are not very large. 2. Growth Factor - This is a parameter also furnished by the Census Bureau and .other sources. The uncer- tainties involved in the growth-factor estimations are relatively higher than the ones involved in the population estimations. 3. Mobility - This parameter estimates the daily move- ment of a population in and out of an enumeration district. Uncertainties are difficult to estimate, yet they should be explicitly considered because, in certain cases, they may be significant. 4. Indoor/Outdoor - This is a field researched by few. Indoor (non-work-place) sources of hazardous sub- stances should be investigated as contributors to total pollutant exposures. Exposures from such sources may constitute a major portion of total population exposure (e.g., perchlorethylene inside of dry-cleaning establishments, .polycyclic organic matter (POM) from cigarette smoke). If indoor sources are not considered, the relative contribu- tion of outdoor sources (and the benefits from controlling such sources) may be greatly overesti- mated. The uncertainties involved are high. 5. Time Budgets - Time allotted to the indoor environ- ment has been studied by sociologists. Uncertainties involved in estimating time budgets are known rela- tively well for certain populations, but are not known well for the general population. 35 ------- 6. Cyclic Variation - This parameter estimates seasonal or periodic populations (such as the 4-year cycle in the Washington, D.C., area). There is a moderate range of uncertainty in estimating cyclic variation. It is case-specific and, on certain occasions, may be significant. Therefore, it should be addressed in all studies. 7. Uniform Distribution - The assumption of population uniformly distributed within an ED involves large uncertainties, specifically in certain areas. Urban populations, for example, are not uniformly distrib- uted in either the horizontal or vertical (high- rise apartments or offices) directions. Population distribution is of importance close to major pollutant sources and in rural areas where each district is large, The uncertainties involved must be studied indepen- dently, and the population distribution is recommended as -a -research -area. 8. Population Grid Points - The approach of allocating all population to the geometric center (grid point) of a district as opposed to the centroid (population weighted center) introduces uncertainties that relate to the air pollution patterns. These uncertainties can be calculated and should be addressed. 5.4 RESEARCH NEEDS Extensive data bases exist indicating where people reside, down to the enumeration district/block group (ED/BG) level. For Level I and possibly Level II efforts these data are probably sufficient. For greater accuracy, more information is needed in three areas: 1. How people are distributed within the ED/BG. 2. How people move about and where they go during the course of a day, a week, or over the period of a year. 3. How to better measure the actual dosages they receive. 36 ------- The distribution within an ED/BG can become critical close to emission sources, where emission levels are high; or in thinly populated areas, where the size of an ED/BG can be large. In either case, significant errors may be encountered in assigning the correct concentration if-there is a -significant concentration gradient over the ED/BG. More research is needed in the second area to avoid possible errors in exposure estimation from failing to take into account the movement of people on a daily, weekly (in particular, week- day/weekend), and seasonal basis. Since, for purposes of esti- mating cancer risks, there is an interest in lifetime exposures it may also be desirable to characterize permanent moves of people from one area to another (e.g., general shift of elderly people from northern industrial zones to the sun-belt region) . In con- nection with the movement of people, it will also be important to determine whether they are indoors or outdoors. There is a need to determine how the movement is to be characterized for use in an overall model and the most appropriate way to develop a usable data base. ' ... • •, - _ Health effects are a function of the amount of hazardous material absorbed by the body. This absorption is a function of both activity level (breathing rate, level of exertion) and expo- sure. Activity levels may vary with time of day, season, and location. The correlation of activity levels with exposure pat- terns shoul-d be investigated as a potentially significant factor for future risk assessment studies. 37 ------- SECTION 6 WORKSHOP SUMMARY: STATISTICS The ultimate objective of the entire assessment is to make a best estimate of the number of people exposed at various con- centration levels to the potentially hazardous chemical. To do this, we need to know the emissions, the atmospheric dispersion, and the population distribution data. If we assume that these factors interact with one another, then the uncertainty or variance of the final estimate is a complex function of the variances of the individual parts of the calculation. If the variances of the three parts of this calculation decrease in a particular order such as a2dispersion > this ranking of uncertainties should aid in deciding how resources could be allocated in attempting to improve our knowl- edge and attempting to make the best estimates of exposure. Similarly, it should be a guide to understanding the expected confidence intervals of the estimates . When we consider the exposure part, we want to know what would be indicated by individual personal samplers worn by a large and representative sample of the populace. Very little work has been done in getting people to wear such samplers, but we are ultimately attempting in this kind of study to predict the answer which would be found if a large group of the populace did indeed wear such samplers. The read- ings of such samplers would be influenced by the emission rate and its temporal variation, the variations in concentration at various points caused by the meteorological dispersion of the 38 ------- emissions that variation caused by indoors-outdoor concentration differences, and the variations caused by the movement and activities of the individual. In the ultimate calculation scheme, a statistical distribution of exposures would be obtained. The purpose of the statistics committee was to aid EPA in developing realistic confidence intervals for use in exposure assessment studies. During the EPA Exposure Assessment Work- shop, members of the committee observed committees in atmospheric modeling, emissions estimation, and population estimation. In- formation gathered from these other groups was applied in the development of confidence intervals for exposure assessment studies. 6.1 RECOMMENDATIONS The recommendations of the statistics committee are summa- rized below. A quantitative approach based on expected values and 95 percent confidence limits of the individual elements of the analysis could be used to estimate exposure levels and their associated confidence limits. Each of these limits should be accompanied by a detailed description of the process used to estimate it. The definition of exposure should include continuous and correlated space and time variations. Since error is intro- duced, details of the process and/or calculations used to esti- mate exposure should be included in the contractor's report. The precise definition of the three levels of analysis should remain flexible to allow for the contractor to allocate his resources in the most cost-effective manner. The contractor should be required to explicitly define and document his 39 ------- approach to the allocation of resources early in the course of the work. Proposals should include a description of the con- tractor's plans in this regard. The procedure should not require accuracy levels to be specified in advance for any pollutant, nor should comparable accuracy levels be required for the three component areas of any one study. A decision may be reached on the basis of data having limited accuracy. Each initial study of a pollutant should deter- mine the accuracy of the data and decide if further accuracy is necessary. 6.2 STATEMENT OF THE PROBLEM For statistical purposes, it is convenient to consider an exposure assessment as an estimate of population exposure in ppb-person-years plus a measure of the accuracy of this estimate. A convenient and useful form for this information is a set of confidence limits and their associated probability, e.g., we are 95 percent confident that the result is between a and b ppm- person-years. These numbers may be calculated if each element in the process of producing the exposure estimates is associated with a confidence level of this form. This information may then be used to determine whether the exposure estimate exceeds the value H (e.g., the desired cut- off point for Level I listing) at a specified level of confidence. Thus the exposure estimate Hx and its associated upper and lower control limits UCL^ and LCL^ are compared to H . If, as in the case of Figure 3, the UCLjj is less than HQ, we would accept the hypothesis that the true exposure to this chemical is below H and thus decide not to list it. 40 ------- w 1 LCLg 1 1 HTTfT 1 •^T H o Figure 3. Exposure level not within hazardous range. On the other hand, if as in Figure 4, the lower confidence limit exceeds H , we would decide that the true exposure is in excess of HQ and thus list the pollutant. The third case, obviously, is where the confidence limits straddle HQ; in this case it is necessary to undertake further study to refine (nar- row) the limits by taking more (or more accurate) measurements. Such refinement may still be within the framework of a Level I study, or may require accuracies normally regarded as belonging to Level II. H. LCLH; Figure 4." Exposure level within hazardous range. It should be noted that, due to the two-sided nature of the 95 percent confidence interval, the probability of making a wrong decision (e.g., not listing a pollutant which should be listed or vice versa) does not exceed 2% percent. It should be clear from Figures 3 and 4 that it may be pos- sible to reach a decision regarding a pollutant (i.e., to list or not to list) even when limited precision in one of the three component areas causes the confidence interval to be quite large. 41 ------- Obviously, very dirty or very clean sources may be easily recog- nized without substantial study. However, no attempt should be made to require the same precision in different studies, or in the components of a single study. An initial analysis, based on readily-available data, should be used to decide what statistical level of accuracy is sufficient. If this level is not reached in each study, efforts can be made to improve the accuracy of the assessment. Cost effectiveness may be increased consider- ably if the "adaptive approach" described above is used. The preceding paragraphs explain in general terms what type of information is desired and how it will be used. To be more specific the estimate of exposure which we have referred to as "H" above may be expressed as: H all space time sources Q(s,t) *(s,t), p(s,t) dtds (1) where : i|» =»• meteorological dispersion function Q «* emissions rate p » population distribution function, and s and t are indices of space and time. Equation (1) summarizes, in a very general way, the roles of the three component parts (emissions, atmospheric transport, and population distribution) . Again, it should be emphasized that no uniformity of precision for the three components should be sought in the initial analysis, since it is possible that valid conclusions may be drawn from imprecise data. Precision in one area may* compensate for lack of precision in another. It is most important that the precision of the data in each component be well known and documented. 42 ------- It should be noted that continuous and correlated space and time variations are included in this definition of exposure. This is an important concept. This type of definition is recom- mended for exposure estimates. Upper and lower confidence limits should account for errors created by simplifications, assumptions, and uncertainties in the data and modeling procedures used to estimate H. Details of the process and data used to calculate these limits are to be considered an essential part of the report- ing process. 6.3 RECOMMENDED APPROACH The approach to calculating H that we recommend is to esti- mate an expected value and upper and lower confidence limits on each element used in the calculation of H. The confidence limits are not necessarily symmetrical but are defined to provide a 95 percent probability that the true value falls within the interval as shown: Prob (LCL < X < UCL ) = 0.95 xi xi (2) Where X^ is Element i used in calculating H. Each of these limits should be accompanied by a detailed descrip- tion of the process used to estimate it. By combining these estimates in accordance with the schematic in Figure 5, H e E£ Q IISSIONS 3TIMATES D E * ISPERSION STIMATES PO E P PULATION STIMATES Figure 5. Combination of data to estimate hazard. H and its associated confidence limits not necessarily at 95 per- •cent probability may be estimated. 43 ------- The estimation of the confidence limits of H is not neces- sarily the result of a straightforward convolution of the error distributions assumed for each element of the calculation. Rather, correlations between emissions, dispersion, and popula- tion movement must be accounted for, thus increasing the complex- ity of the problem. We have chosen to leave the development of an approach to this problem to the contractor because it is a nontrivial problem for which an efficient solution may best be developed on a case by case basis.. Depending on the error distributions used for each of the elements of the calculation, the confidence limits of H may be estimated in several different ways. Monte Carlo techniques provide a straightforward, albeit possibly expensive, method of estimating the error distribution of H. Stratification of the variables into seasonal, day vs. night, indoor vs. outdoor, and/or other categories may help to reduce the multicolinearity problems to the point where straightforward convolution of the distributions may provide good results. A third possibility is the development of an analytical approach to the derivation of the distribution of the product of Q, tp, and p. 6.4 THE THREE LEVELS OF ANALYSIS. The three levels of analysis discussed at the meeting of 11-13 December correspond roughly to good, better, and best; these are largely qualitative categories bounded by the levels of effort suggested for each. The precise definition of each level and the confidence limits which may be expected of the results are the subject of the individual committee reports. The concern of the statistics, committee in this regard is to leave the contractor sufficient latitude in the definition of "levels" so that his resources may be allocated to the area where the most cost effective improvements in the estimate of H are to be found. Thus, the contractor should be required to evaluate early in his efforts an appropriate allocation of resources with 44 ------- respect to narrowing the confidence limits of H. An early inter- mediate result should be an analysis of the potential for improv- ing each element of the estimation of H, and its cost effective- ness. The final reports at. each level should conclude with similar information pertaining to the potential for improvement of the estimates with further work. We would expect that the contractor's proposal discuss his plans for dealing with these issues. 6.5 STATISTICAL PROBLEMS IN EXPOSURE ASSESSMENTS The estimation of numerical confidence limits on quantities for which there is a paucity of data is a task which many investi- gators will approach with a substantial and understandable degree of trepidation. The process vmay require at times that educated j guesswork be substituted for measurement. For the present purpose a key point to remember is that professional judgment in these matters is an improvement over using estimates without confidence limits. The members of the statistics committee feel comfortable with this type of information provided that it is accompanied with a clear statement of the basis for the estimates whether they come from measurement, engineering judgment, surrogate emis- sions measurements, etc. However, it should be recognized that if the precision estimates are not well-based, the conclusions will be correspondingly less reliable. The discussion would allow the user to understand the kind and quality of information in- cluded in the report and to act accordingly. 45 ------- APPENDIX A WORKSHOP COMMITTEES ATMOSPHERIC MODELING COMMITTEE Noel deNevers, Chairperson University of Utah, 3062 MEB Department of Chemical Engineering Salt Lake City, Utah 84112 Richard -Porter Texas Instruments, MS 3949 P.O. Box 225621 Dallas,Texas 75265 Elmer Robinson Air Pollution Research Washington State University Pullman, Washington 99164 Walter Dabberdt Atmospheric Science Division SRI International Menlo Park, California 94025 Richard Shultz Trinity Consultants 100 N. Central Expressway, Suite 910 Richardson, Texas 75080 Steve Hanna ATDL/NOAA P.O. Box E Oak Ridge, Tennessee 37830 EMISSIONS ESTIMATING COMMITTEE Kenneth Baker, Chairperson Greene & Associates, Inc. One Energy Square Dallas, Texas 75206 46 ------- Macon Shepard Environmental Consultants, Inc. P.O. Box 1178 Clemson, South Carolina 29631 Thomas Kittleman E.I. DuPont de Nemours and Co, Inc, 1352 Louviers Building Wilmington, Delaware 19898 Robert G. Wetherold Radian Corporation P.O. Box 9948 Austin, Texas 78766 Ralph White Hydroscience, Inc. 9041 Executive Park Drive Knoxville, Tennessee 37919 POPULATION ESTIMATING COMMITTEE Yuji Horie, Chairperson Technology Service Corporation 2811 Wilshire Blvd. Santa Monica, California 90403 William F. Biller 68 Yorktown Road East Brunswick, New Jersey 08816 Demetrios Moschandreas Geomet Inc. 15 Firstfield Road Gaithersburg, Maryland 20760 Anton Chaplin Teknekron 2118 Milvia Street Berkley, California 94704 Richard Londergan TRC 125 Silas Deane Highway Wethersfield, Connecticut 06109 47 ------- STATISTICS COMMITTEE Richard Pollack 868 Gellston Place El Cerrito, California 94530 M.R. Leadbetter Department of Statistics University of North Carolina Chapel Hill, North Carolina 27514 Terence Fitz-Simons EPA/EMSL Research Triangle Park, North Carolina 27711 48 ------- APPENDIX B WORKSHOP ATTENDEES EPA EXPOSURE ASSESSMENT WORKSHOP ATTENDEES LIST December 11, 1979 NAME Gerald E. Anderson William D. Baasel Kenneth Baker William F. Biller Anton Chaplin Bob Coleman Walter Dabberdt Noel deNevers James L. Dicke Mike Dusetzina Tom Feagans Terence Fitz-Simons Steve Hanna Yuji Horie Tom Kittleman Ross Leadbetter Barbara Lee Richard Londergan David Mage Justice A. Manning Jack McGinnity Craig Miller D.J. Moschandreas Nancy B. Pate Bill Petersen Richard L. Pollack Richard A. Porter Elmer Robinson Harold Sauls George J. Scheve Richard H. Schulze Macon Sheppard Ben Suta Bob Wetherold Ralph White ORGANIZATION Systems Applications, Inc. EPA/ORD/IERL Greene & Associates, Inc. Consultant Teknekron Research EEA, Inc. SRI International University of Utah EPA/OAQPS EPA/SASD EPA/OAQPS EPA/EMSL ATDL Technology Service Corp. DuPont Statistician, U. of N.C. Radian Corporation TRC EPA/ORD/EMSL EPA/OAQPS/SASD EPA/OAQPS/SASD EEA, Inc. Geomet, Inc. USPHS/EPA/OAQPS EPA/ESRL LBL Texas Inst. Inc. Washington State Univ. EPA/EMSL NOAA/EPA/OAQPS Trinity Consultants, Inc. Env. Consultants, Inc. SRI International Radian Corporation Hydroscience PHONE NO. (415) (919) (214) (201) (415) (919) (415) (8011 (919) C919) (.919) (919) C615) (213) (302) (919) (512) (203) (919) (919) (919) (703) (301) (919) (919) (.415) (214) (509) (919) (919) (214) (803) (415) (512) (615) 472-4011 541-2815 691-3500 257-0164 548-4100 471-2506 326-6200 581-6024 541-5381 541-5355 541-5355 541-2792 526-1237 829-7411 366-4718 929-5172 454-4797 563-1431 541-2231 541-5345 541-5204 528-1900 948-0755 541-5202 541-4564 486-6292 238-5635 335-1526 541-3123 541-5391 234-8567 654-5410 326-6200 454-4797 690-3211 49 ------- APPENDIX C EXPOSURE ASSESSMENT DOCUMENTS REVIEWED 1. Anderson, Gerald, Rinnan Exposure to Atmospheric Concentra- tions of Selected Chemicals. EPA Contract No. 68-02-3066. SAI No. EF-156, Systems Applications, Incorporated, 950 Northgate Drive, San Rafael, California 94903, December 1979. 2. Coleman, R., et al., Assessment of Human Exposures to Atmo- spheric Cadmium, EPA Contract No. 68-02-2836, Tasks 3 and 6, Energy and Environmental Analysis., Inc., Arlington, Virginia, June 1979. 3. Coleman, R. et al., Sources of Atmospheric Cadmium, Energy and Environmental Analysis,Inc., Arlington, Virginia, n.d. 4. H.E. Cramer Company, Inc., Dispersion Model Analysis of the Air Quality Impact of Emissions from Benzene_Storage and Loading Facilities, EPA Contract No. 69-02-2507, Salt Lake City, Utah, February 1979. 5. Mara, Susan J., and Shonh S. Lee, Assessment of Human Expo- sures to Atmospheric Benzene, Center for Resource and Environmental Systems Studies Report No. 3OR, EPA Contract Nos. 68-01-4314 and 68-02-2835, SRI Projects EGU-5734 and CRU-6780, SRI International, Menlo Park, California, May 1978. 6. 'Mara, Susan J., Benjamin E. Suta, -and Shonh S. Lee, Assess- ment of Human Exposures to Atmospheric Perchlorethylene, draft final report, Center for Resource and Environmental Systems Studies Report No. 73, EPA Contract No. 68-02-2835, SRI Project CRU-6780, SRI International, Menlo Park, Cali- fornia, January 1979. 7. Schewe, George J., "Modeling Analysis of Maximum Ambient Arsenic Concentrations Due to Primary Copper Smelters," Paper for Environmental Protection Agency, Model Applica- tion Section, n.d. 50 ------- 8. Suta, Benjamin E. , Assessment of Human Exposure to Atmospheric Acrylonitrile, final report, Center for Resource and Envi- ronmental Systems Studies Report No. 100, EPA Contract No. 68-02-2835, Task 20, SRI Project CRU-6780, SRI International, Menlo Park, California, August 1979. 9. Suta, Benjamin E., Human Exposures to Atmospheric Arsenic, Resource and Environmental Systems Report No.50, EPA Contract Nos. 68-01-4314, 68-01-2835, SRI Projects EGU- 5794 and CRU-6780, SRI International, Menlo Park, Cali- fornia , September 1978. 10. Suta, Benjamin E., Human Population Exposures to Coke-Oven Atmospheric Emissions, final report, Center for Resource and Environmental Systems Report No. 27, EPA Contract Nos. 68-01-4314 and 68-02-2835, SRI Projects EGU-5794 and CRU- 6780, SRI International, Menlo Park, California, October 1978 (rev. May 1979). 11. Suta, Benjamin E., Human Population Exposures to Coke-Oven Atmospheric Emission Under Attainment of OSHA Worker Standards, final report, Center for Resource and Environ- mental Systems Studies Report No. 65, EPA Contract No. 68-02-2835, Task 10, SRI Project CRU-6780, SRI Interna- tional, Menlo Park, California, Rev. April 1979. 12. Suta, Benjamin E., Assessment of Human Exposure to Atmo- spheric Ethylene Bichloride^final report,Center for Resource and Environmental Systems Studies Report No. 82, EPA Contract No. 68-02-2835, Task 17, SRI Project No. CRU-6780, SRI International, Menlo Park, California, May 1979. 13. Systems Applications, Inc., Human Exposure to Atmospheric Chloroprene, preliminary report — technical progress narrative, Office of Air Quality Planning and Standards, SAI Project No. 179-55, San Rafael, California, April 1979 51 ------- APPENDIX D INDIVIDUAL COMMENTS FROM ATMOSPHERIC MODELING COMMITTEE MEMBERS Dl.O PROBLEMS ENCOUNTERED USING THE SINGLE-NOMOGRAPH APPROACH IN LEVEL I ASSESSMENTS Dl.l Level I Nomographs — Richard Porter It has been suggested that nomographs be used by the con- sultant for the Level I survey of pollutants. It is important that a sufficiently detailed set of nomographs be developed by EPA for the contractors' use to insure that pollutants be ranked in the proper order. It is not possible to design a single nomo- graph that can characterize all pollutants for all conditions. For instance, the use of a nomograph designed for elevated releases of buoyant plumes in level rural areas for a pollutant that is emitted from roof top vents in industrial valley situa- tions could result in the pollutant being ranked in the lower 20% of the list instead of the upper 20%. Separate nomographs should be provided to the contractor for each major source release height and terrain situation. The following are situations for which distinct nomographs should be provided. 1. Rural flat terrain-release heights: roof top, 30 m, 75 m, 100 m, 200 m. 2. Urban flat terrain-release heights: roof top, 30 m, 75 m, 100 m, 200 m. 52 ------- 3. Rural complex terrain-release heights: roof top, 30 m, 75 m, 100 m, 200 m. 4. Urban complex terrain-release heights: roof top, 30 m, 75 m, 100 m, 200 m. This suggests four nomographs with five Xu/Q curves each. In the case of a pollutant that is only emitted from roof top vents in urban complex terrain, a simple nomograph would be used. In the case of a nationally distributed pollutant emitted from urban sources, the nomographs for complex and flat terrain could be used in proportion to the distribution of the sources. D1.2 Input Meteorology for Modeling Activities — Elmer Robinson In assessing the potential impact of toxic chemical materials, it is useful to use various modeling or simulation techniques. These techniques can be carried out at several levels of sophisti- cation with concurrent savings in cost and effort but at a sacri- fice in output specificity. The several levels of modeling and sophistication have been denoted as Levels I, II, and III for this particular toxic material evaluation. In Levels I and II, certain savings in effort are available by using more-or-less standard meteorological factors in the dispersion estimates. While this procedure will always reduce the precision of the resulting dispersion, there are some readily recognizable situations where an answer can be grossly in error. Since a number of these suspect situations can be easily recog- nized and dealt with, it seems worthwhile to consider this prob- lem so that meteorological input data can come from a "modified standard" scheme rather than a single set of standard values such as have been described and used by Youngblood. For the dispersion modeling program under Levels I and II approaches, the most important meteorological factors are wind speed and stability. Wind direction frequencies for prevailing 53 ------- winds may also be important in certain situations. It is recog- nized that under Levels I and II that site-specific information will probably not be available within the constraints of cost and time. Level I studies will use some sort of national annual average input while Level II studies will seek out local climato- logical data, such as STAR tabulations, for use in the analyses. For Level I studies, it is proposed that the meteorological factors be based on regional or national data with categories selected from a tabulation of climatological data. For example, readily available maps of wind climatology could be used to develop a table of wind speed factors for specific sites in various regions. For some sources, similar data could be developed based on conditions averaged over the largest urban areas in the country (maybe 25?). Table Dl. is an example matrix for a regional table of annual average wind speed and prevailing direc- tion frequency, and Table D2. is an example of the average urban area situation. TABLE Dl. REGIONAL ANNUAL AVERAGE CLIMATOLOGY FOR LEVEL I MODELING ESTIMATES Annual Average Persistence Region Windspeed Factor NE Coastal NE Inland SE Coastal SE Inland Appal. Val. Upper Midwest 3 mph 2 3 2 2 4 60% 30 60 30 60 30 NW Coast 4 60 54 ------- TABLE D2. AVERAGE ANNUAL CLIMATOLOGY FOR URBAN AREA SOURCES Annual Average Persistence Urban Size Windspeed Factor Over 1,000,000 pop. 3 mph 40% over 500,000 over 100,000 over 50,000 Although the- data listed in the above tables are only illustrative guesses and are not valid numerical values, a range of at least a factor of 2 should be expected in these values. They would transfer directly to similar factors in terms of dispers.ion and. pollutant concentration .estimates . Thus since the use of this improved climatological input data does not involve significant additional effort in the preparation of a Level I summary (once the factors have been derived), it is suggested that this scheme of regional climatic factors be developed for this type of report. Stability could also be summarized on a regional basis dependent on whether the source had a diurnal cycle. For example, an 8-5 urban source might be characterized by C stabil- ity while a rural, 24-hour source may have D stability. In Level II studies, some more-or-less local climatology in the form of STAR tabulations will be used in the analysis. These, however, should reflect reasonable changes in climatology from the observation site, usually an airport, to the source site. An investigation using the STAR data should be encouraged to consider modifying the STAR frequencies to determine whether modified conditions would be important in the concentration and population exposure indices. The sort of changes to be considered include (1) channelling of winds at a valley site 55 ------- but not in open, upland sites, (2) more stable and more limited wind variability at coastal sites compared to inland locations, (3) less stable and lower wind speeds for urban areas compared to a local rural airport site, etc. The STAR-type of evaluation has also been found to over-predict the frequency of D stability in many areas; i.e., arid western sites. If such a shift to unstable and/or stable conditions relative to neutral conditions would influence the exposure estimates significantly, then such changes should be considered. D2.0 SPECIFIC MODEL RECOMMENDATIONS - Walt Dabberdt The recommended, generalized procedure for selecting and implementing dispersion models is outlined schematically in Tigure Dl. The procedure applies equally to any of the three levels of analysis. However, the rigor and specificity of the process and hence the results vary considerably among the three levels. Accordingly, the guiding premise for each level should first be recognized: LEVEL SOURCE OF ANALYSIS I Objective is to provide a first-order ranking of the various substances and source categories. Approach employs a simplistic methodology (i.e., nomographs) to relate annual-average normalized concentration to fundamental classes of source configuration (i.e., plume height and spatial extent of source) and emission features (i.e., daytime vs. continuous emissions) using a default set of meteorological data. II Objective is to identify specific sources and pol- lutants that may require controls. Approach employs available, site-specific emissions, meteorological, and monitoring data as inputs to a comprehensive dispersion model (normally of the steady-state Gaussian variety) that is of the type referenced in the OAQPS modeling guidelines, UNAMAP series, or equivalent.. 56 ------- Source Configuration Pollutant Characteristics Terrain Features Available Data Analysis of Concentration Data Selection Averaging or Exposure Time Receptor Characteristics Specification of Model Inputs Modifleattoi Calibration of Model Reaction Rates Meteorology Source Features Physical: Wake Effects Plume Rise OftaAii U«l«i*l stacx neiuju Source Layout Figure Dl. Model selection and implementation procedure, 57 ------- LEVEL SOURCE OF ANALYSIS . Ill Objective is to generate the best set of concentra- tion estimates necessary to determine the degree of control required of specific sources or source categories. Approach employs models that match in a mathematical sense the physical characteristics/ features of the site (i.e., source configuation and rate, meteorology, terrain, pollutant trans- formations/sinks, exposure time, and receptor loca- tions) . Analysis is to be supported by site- specific meteorological data and the model results are to be evaluated using representative or sur- rogate monitoring data. Having determined the pollutant or substance of interest and the level of analysis that is necessary, the first major step in the analysis procedure is MODEL SELECTION. In the case of Level I analysis, the modeling should already be available in the form of a short series of nomographs that relates normalized concen- tration (annual average) to source height and extent, and the "schedule" of emissions (i.e., daytime only vs. continuous). With Levels II and III, the model selected must meet the require- ments imposed by: . source configuration, pollutant characteristics (inert vs. reactive, gas vs. particle), terrain features, and the exposure or integration time. Whereas the emphasis of the EPA to date is only on annual-average concentrations, it is the strong recommendation of the modeling committee that discrete events (such as accidents) also be considered. While the integration time may be short, the subsequent dosage received may often be 'the-maximum for all -sources/time periods. That is to say, the corresponding risk to health may be greatest for sub-annual releases and exposures. Level II analysis will nor- mally employ steady-state Gaussian plume (or equivalent) models, whereas more sophisticated models may be required for Level III (e.g., puff, grid, or particle-in-cell methods). The second major task is the SPECIFICATION OF MODEL INPUTS. Levels II and III modeling seek to provide maximum realism in 58 ------- the results, consistent with the quality of the inputs. In the case of Level II, available site-specific or site-representative data are to be used whereas Level III demands that data be used that properly and thoroughly represent all important aspects of the dispersion problems. For example, Level II may employ air- port Service A wind data while Level III may require construction of mesoscale flow patterns and the generation of time-dependent curvilinear traj ectories. In operating the model to simulate annual-average concentra- tions, it is important to recall that the basic objective is to calculate annualized population exposures (APE). Symbolically, the APE is the cross-product of emissions (Q), dispersion (D), and population (P)integrated over a one-year period, or: APE = f I E Qtj D^ P^ (1) where i = 1, 24 (hours per day) and j = 1, 365 (days per year). In performing the modeling (i.e., the D or QD calculations), it is imperative that the actual computed APE be equivalent to what would be obtained through a strict adherence to the formulation given in (1) above. 59 ------- D3>.0 ESTIMATION OF ERRORS - Richard Pollack Atmospheric modelers seldom, if ever, have sufficient moni- toring data to validate a modeling technique for more than a few locations. In addition, the assumptions required by a model are never perfectly satisfied in a given application; as a result, it is difficult to specify confidence limits on the accuracy of any given model. The most desirable method for assessing.the validity of a model in a given application is to compare it with a portion of the monitoring data not used in the calibration and/or adjustment of the model. The recommendations presented below are based, where possible, on this approach. For the cases where modeling is to be performed in the absence of data, we recommend considering the use of a default option; i.e., confidence limits which are sufficiently wide to account for any reasonable possible error. The details of the process of estimating confidence limits where data are available and for setting a default option or other procedure where it is not should be the subject of an EPA guid- ance document to be supplied to the contractors. The modeling group did not feel that addressing these issues in full detail was within the scope of the brief meeting. The modeling group recommends that, given the guidance referred to above for each level, a modeling exercise should be accompanied by numerical estimates of confidence limits and a qualitative discussion of the potential sources of error. These sources include, but are not necessarily limited to, those factors listed in Table D3. The recommendation for analysis at specific levels is as follows: Level I: The development of the nomograph should include validation exercises to develop estimates of expected errors by comparing predictions and observations in a variety of situations. The developer of the nomograph 60 ------- should consider the option of providing error estimates stratified by the input variables; for example,separate estimates for urban and rural cases, low level and elevated sources, flat vs. complex terrain, etc. Level II: Error estimates should be based on site- specific data if available. Otherwise a default option (or alternative approach as recommended by the study suggested above) is recommended. The report should include a discussion of the processes and factors (e.g., Table D3.) which are (un)accounted for in the model. EPA guidance for developing confidence levels should be followed. Level III: For Level III, site-specific data should be required for model validation. EPA guidance for developing confidence levels should be followed. TABLE D3. POTENTIAL SOURCES OF ERROR Use of non-site-specific meteorology of source geometry Inadequate representation of atmospheric process or other factors influencing concentrations such as: terrain removal processes wake effects source height plume rise entrainment The quantity to be estimated is ideally that which would be obtained by "hourly summing" considering fully temporality varying emissions, meteorology, and population. In fact, our approach will not attempt to estimate this directly but rather estimate either the product of the annual averages of E, x/Q. P or a "slightly stratified" version of this, e.g., day, night, and seasons. Therefore, there are two quantities at error: 61 ------- 1. Our errors in estimating the annual averages or "slightly stratified" variables -- This type of error is discussed in the previous pages. 2. The error introduced by using annual averages or "slightly stratified" data as a surrogate for the full temporal case.' This error would require a separate effort to address because it depends largely on covariance terms; i.e., emissions and x/Q correlate both seasonally and diurnally, popula- tion movement, x/Q» and wind direction are all cor- related. This is why the concept of convolving the distributions won't work. In other words, if we could estimate all annual averages perfectly, how far off would we be from actual human exposure? The multiple convolution may work reasonably well, strati- fied by day, night, and season. D4.0 DETAILED MODELING RECOMMENDATIONS -- Richard Schulze For Level II studies, the committee recommends the use of EPA Guideline models with routinely recorded meteorological data. Where plume chemistry or terrain is important, the model used should incorporate adjustments for these factors. For annual concentrations in urban areas, model such as CDM and TCM appear to be the most appropriate of the Guideline models, but both have several shortcomings. For example, both models include buoyancy plume rise and ignore momentum plume rise and the effects of stack and building downwash. These models use rural-derived formulas for estimating vertical plume speed and then adjust the observed stability category to make a rough adjustment to urban conditions. The vertical dispersion param- eter for stack emission is adjusted to such a great extent that it is probably not appropriate to locate receptors within 500 meters of stacks with heights of 20 meters or less. The models do allow some rough estimates of the effects of daytime and night- time emission. 62 ------- The area source algorithm in CDM requires extensive computer time and some experience in correctly selecting the density of the sampling points. In TCM the user must exercise judgement as to the number of grid squares across which emissions from one grid sequence are to be spread. Both user's guides caution that best results from the area source algorithm are obtained when there is relatively limited, 50 percent or less, variation in area source emission rates from adjacent grid squares. In rural areas a model such as VALLEY -- with no terrain adjustments— or AQDM can be used. These models do not make the adjustments for calculating the vertical dispersion coeffi- cients for urban stability categories and the initial plume speed contained in TCM and CDM. The VALLEY model is also suggested -for analyzing concentrations in rough terrain after making adjust- ments to include the effects of entrainment from plumes from point sources. Meteorological data should be carefully selected. In hilly terrain and shoreline areas, the airport observations of wind speed and direction are often not typical of those found at plant sites. There appears to be a tendency for observed wind directions to be skewed at some locations, especially those operated by the Armed Forces. One must also carefully specify STAR data. For CDM and TCM, a "day-nite" STAR should be used, while a regular six-category STAR should be used with VALLEY and AQDM. For Level III studies, the committee recommends that the use of refined models be considered, particularly if changes in hazard exposures of greater than 20 percent from the use of Level II models are identified. For example, if hour-by-hour emission rates and population exposures in specific grid squares are available, then it would 63 ------- be appropriate to use a sequential model, such as CRSTER, RAM, or ISC to calculate annual averages. If sources and exposures in hilly or mountainous terrain are analyzed, the use of site-^ specific meteorology is suggested. The investigator for Level III studies is urged to critically examine the assumptions in the models suggested for the Level II analysis and evaluate the benefits of refinements. These include trajectory models in hilly or mountainous areas, increased sophis- tication in analyzing plume chemistry and plume removal processes, studies of the effects of sea breezes in shoreline areas, the effects of building downwash, and the use of monitored data to adjust modeled results to realistic levels. D5.0 MODELING CONSIDERATIONS - Steve,Hanna D5.1 Importance of Short Term Studies The EPA emphasis on annual averages is understandable because of the dependence of carcinogenicity on dosage. The modeling done so far has been concerned strictly with routine emissions. However, accidental releases also can result in high dosages, but over a short period of time. Possible accidents include explosions, truck and train wrecks, and industrial spills. In these cases, employees or nearby residents could receive dangerous dosages within a few minutes. I recommend that models be developed for handling accidental releases. These models would need to have the following capabilities: 1. Ability to calculate plume rise or sink for dense gases or highly momentum-dominated plumes, 2. Ability to account for source effects such as the initial dilution imposed by the presence of a railroad car, 3. Ability to calculate plume trajectory in a complex terrain situation. 64 ------- 4. Ability to calculate instantaneous puff diffusion. D5.2 Use of Monitoring Data If .good monitoring data exist, then they should be used to improve the modeling technique. In general, high-quality monitor- ing data should have precedence over model predictions. It is recognized that high-quality data depend greatly on intelligent site selection and competent maintenance of the instrument. Level I studies are intended to be simple screening pro- cedures to separate the innocuous chemicals from the potentially dangerous ones. Even at this level, good monitoring data can be used to "calibrate" simple diffusion models. For example, if the average model prediction by an urban box model is 10 yg/m3, and a properly located monitoring station indicates an average of 2 yg/m3, then a factor of 0.2 should be applied to all future model predictions for different scenarios. In Level II studies, the monitoring data can be compared with model predictions (UNAMAP series) to develop regression equations between observed and predicted concentrations. For critical cases under Level III, monitoring data can be used to adjust internal model assumptions. Empirical relations between observed concentrations and meteorological parameters and source parameters should be developed. For example, this analysis may show that high concentrations usually occur with a certain wind direction and stability. Or, the analysis may suggest that an emission source has been missed in the area around a certain monitor. In all cases, the basic philosophy is to use all the good data to the fullest extent possible. 65 ------- D5.3 Importance of Including Transport and Interaction With Water, Land, and Biota Potential carcinogens can follow pathways through several media. For example, a substance released from a smokestack into the air may fall out or deposit onto a certain watershed. It then will be transported by ground water and streams or may enter biota. Some of it may be resuspended into the air or may vola- tilize. In other words, a multimedia model is needed for several of the substances that will be studied. Emphasis must be on transfer across interfaces, such as dry and wet deposition, resuspension, or volatilization. These effects can be treated in a Level I study using current techniques. Level II and III studies would require extensive literature searches and possibly more research. D6.0 INDOOR/OUTDOOR EXPOSURE -- Richard Schulze (1st paragraph) and Noel deNevers (2nd paragraph) The committee assumed that outdoor air pollutant levels were typical of population exposures. In fact, most people spend the majority of their time indoors where concentrations are frequently only a fraction of levels found outdoors. The committee made no attempt to develop recommendations relating indoor to outdoor pollutant levels or population exposure levels to outdoor con- centrations . Although indoor and outdoor concentrations are not the same, if our health effects data are based on epidemiology, with con- centrations measured outdoors (as in the studies which led to the particulate, S02 and N02 ambient air quality standards), then the real assumption in using outdoor air quality to esti- mate health damage is that the relation of indoor to outdoor air contamination will be the same in the future as it was during the period of the epidemiological study. This is a much more plausible assumption than the one that indoor and outdoor air quality are the same. ,>. ------- APPENDIX E REGULAR USERS OF CENSUS DATA U.S. BUREAU OF CENSUS REGIONAL OFFICES ALABAMA Graduate Program in Hospital and Health Administration Attn: Dr. Tee H. Hiett School of Community and Allied Health University of Ala. in Birmingham •Room 205 SCAB Birmingham, Ala. 35294 (205) 934-5223 *Alabama Development Office Attn: Gil Gilder State Capitol Montgomery, Ala. 36130 (205) 832-6400 C, 1-7, 10 *Alabama Public Library Service Attn: Anthony Miele 6030 Monticello Drive Montgomery, Ala. 36130 (205) 277-7330 C, 1-7, 10 **University of Alabama Attn: Dr. Carl Ferguson Center for Business and Ecomonic Research Box AK University, Ala. 35486 (205) 348-6191 C, 1-7, 10 ALASKA Institute of Social and Economic Research Attn: Mr Lee Huskey University of Alaska Anchorage, Ak. 99504 (907) 278-4621 or Attn: Mr. Gary Lu Fairbanks, Ak. 99701 (907) 479-7436 B, 1-5 ARIZONA *Northern Arizona University Attn: Dr. Ron Gunderson College of Business and Research Flagstaff, Ariz. 66011 (602) 523-3657 C, 1-10 **Arizona Department of Economic Security Attn: Mr. Richard A. Froncek 1717 West Jefferson P.O. Box 1623 - 045Z Phoenix, Ariz. 85005 (602) 255-5984 C, 1-10 67 ------- *Department of Library, Archives and Public Records Attn: Sally Hronek Federal Documents Section Capitol, Third Floor 1700 West Washington Phoenix, Ariz. 85007 (602) 255-1121 C, 1-10 Resource Consultants, Inc. Attn: James T. Kirk P.O. Box 7132 Phoenix, Ariz. 85011 (602) 265-1161 B, 1-5, 8-10 *Arizona State University Attn: Glenda Rauscher Coordinator of Research College of Business Administration Tempe, Ariz. 85281 (602) 965-3961 C, 1-10 *University of Arizona Attn: Dr. Lee B. Jones Dean of the Graduate College Administration Bldg., Rm. 501 Tucson. Ariz. 85721 (602) 626-4032 C, 1-10 ARKANSAS **Industrial Research and Extension Center Attn: Dr. Forrest Pollard University of Arkansas P.O. Box 3017 Little Rock, Ark. 72203 (501) 371-1971 C, 1-3, 5, 6, 10 *0ffice of the Governor Attn: Joan Roberts State/Federal Relations Little Rock, Ark. 72201 (501) 371-2611 C, 1-3, 5, 6, 10 CALIFORNIA Urban Decision Systems, Inc. Attn: James A. Paris 2032 Armacost Avenue P.O. Box 25953 Los Angels, Calif. 90025 (213) 826-6596 A, 1-5, 8, 9 Research Systems, Inc. Attn: Gerald J. Jansen 365 South Meadows Ave. Manhattan Beach, Calif. 90266 (213) 372-8838 D, 1-3, 5-6 Allstate Research and Planning Center Attn: Nicholas Gannam Allstate Insurance Company 321 Middlefield Road ..Menlo Park, Calif. 94025 (415) 324-2721 D, 1-5, 7, 9 Decision Making Information Attn: Ron Hinckley 2700 N. Main Street, Suite 800 Santa Ana, Calif. 92701 (714) 558-1321 C, 1-5, 10 Demographic Research Company Attn: Joseph J. Weissmann 233 Wilshire Blvd. Santa Monica, Calif. 90401 (213) 451-8583 D, 1-10 Speron, Inc. Attn: Edward J. Skowron 14621 Titus Street Van Nuys, Calif. 91401 (213) 873-4114 B, 1-5, 8, 9 R-marketers 68 ------- COLORADO DISTRICT OF COLUMBIA Business Research Division Atten: C.R. Goeldner Graduate School of Business Administration and School of Business -Administration University of Colorado Campus Box 420 Boulder, Colo. 80309 (303) 492-8227 B, 1-6 Bureau of Business and Public Research Attn: Dr. William Duff School of Business University of Northern Colorado Greeley, Colo. 80631 (303) 351-2080 B, 1-4 CONNECTICUT Urban Decision Systems, Inc. Attn: James A. Lunden 21 Charles St. P.O. Box 551 Westport, Conn. 06880 (203) 226-7367 A, 1-5, 8, 9 DELAWARE **0ffice of Management, Budget and Planning Attn: Helen Gelof Townsend Bldg. P.O. Box 1401 Dover, Del. 19901 (302) 678-4271 C, 1-3, 5, 8-10 *University of Delaware Attn: John Falcone, Director Computer Center Newark, Del. 19711 (302)" 453-6065 C, 1-3, 5, 8-10 Applied Urbanetics, Inc./Equal Employment Opportunity Data Systems Attn: Laura DeLong Third Floor 1701 K Street, N.W. Washington, D.C. 20006 (202) 331-1800 D, 1-10 Metropolitan Washington Council of Governments Attn: Frank Goodyear 1225 Connecticut Ave., N.W. Washington, D.C. 20036 (202) 223-6800 A, 1-10 FLORIDA Census Access .Program Attn: Ray Jones University of Florida Libraries Library West Gainesvilla, Fla. 32611 (904) 392-0361 D, 1-3, 6, 10 Regional Information Coordinating Center Attn: Steven A. Logan Tampa Bay Regional Planning Council 3151 Third Avenue N., Suite 540 St. Petersburg, Fla. 33713 (813) 898-0891 B, 1, 3-6, 9 Applications Programming Group Attn: Brent Dorhout Florida State University Computing Center Tallahassee, Fla. 32306 (904) 644-3860 C, 1-5, 7-9 69 ------- GEORGIA *0ffice of Computing Activities Attn: Dr. Margaret Park Director of Information Services University of Georgia Athens, Ga. 30602 (404) 542-3106 C, 1-5, 7, 9, 10 **0ffice of Planning and Budget Attn: Tom Wagner State of Georgia 270 Washington Street, S.W. Atlanta, Ga. 30334 (404) 656-2191 C, 1-5, 9, 10 R-Gov't HAWAII Department of Budget and ••Finance Attn: Frances J. Santos Electronic Data Processing Div. P.O. Box 150 Honolulu, Hawaii 96810 (808) 548-5910 B, 1, 3 R-St. gov't IDAHO Center for Research, Grants and Contracts Attn: Dr. Emerson C. Maxson Boise State University 1910 University Drive Boise, Idaho 83725 (208) 385-15 71 B, 1^10 ILLINOIS Chicago Area Geographic Information Study Attn: Edwin N. Thomas Department of Geography University of Illinois at Chicago Circle Box 4348 Chicago, 111. 60580 (312) 936-3112 or 996-5274 B, 1-5, 7-10 Dept. of Sociology, Anthropology, and Social Work Attn: Vernon C. Pohlmann Illinois State University Normal, 111. 61761 (309) 438-2387 or 436-7667 C, 1-6, 8-10 Concordia Teachers College Attn: Dr. William Kammrath 7400 Augusta River Forest, 111. 60305 (312) 771-8300, Ext. 299 D, 1-5, 7-10 INDIANA Research Associates, Inc. Attn: John J. Carter P.O. Box 44640 Indianapolis, Ind. 46244 (317) 266-6926 B, 1-5, 8, 9 IOWA University of Iowa Attn: Chia-Hsing Lu Laboratory for Political Research Scheffer Hall, 321A University of Iowa Iowa City, Iowa 52242 (319) 353-3103 B, 1-5, 10 KANSAS Center for Public Affairs Attn: Fred A. Cleaver 607 Blake Hall University of Kansas Lawrence, Kans. 66045 (913) 864-3700 C, 1-5, 7 70 ------- KENTUCKY Urban Studies Center Attn: Dr. James M. Brockway University of Louisville Gardencourt, Alta Vista Rd. Louisville, Ky. 40205 (502) 588-6626 B, 1-5, 7, 8, 10 LOUISIANA *Experimental Statistics Dept. Attn: Dr. Nancy Keith 173 Agriculture Administration Bldg. Louisiana State University Baton Rouge, La. 70803 (504) 388-8303 C, 1, 3, 5, 7, 9, 10 *Reference Department Attn: .Blanche Cretini Louisiana State Library P.O. Box 131 Baton Rouge, La. 70821 (504) 342-4913 C, 1, 3, 5, 7, 9, 10 **State Planning Office Attn: Leigh Harris 4528 Bennington Avenue Baton Rouge, La. 70808 (504) 925-1584 C, 1, 3, 5, 7, 9, 10 Bureau of Business Research Attn: Dr. Charles 0 Bettiner, III College of Business Administration Northeast Louisiana University Monroe, La. 71201 (318) 372-2123 B, 1, 3-5, 10 *Division of Business and Economic Research Attn: Vincent Maruggi University of New Orleans Lake Front New Orleans, La. 70122 (504) 283-0248 C, 1, 3, 5, 7, 9, 10 Louisiana Tech. University Attn: Dr. Edward J. 0'Boyle Research Division, CAB P.O. Box 5796 Ruston, La. 71270 (318) 257-3701 B, 1-3, 5, 6, 9, 10 MARYLAND BRC Associates, Inc. Attn: Belur K. Radhakrishnan 4336 Montgomery Avenue Bethesda, Md. 20014 (301) 656-2996 E, 1-5, 8 Systems Sciences, Inc. Attn: Miles Letts 4720 Montgomery Lane Bethesda, Md. 20014 (301) 654-9343 D, 1, 2-5, 7, 9 Data Services Division Attn: Thomas E. Jones Westat, Inc. 11600 Nebel Street Rockville, Md. 20852 (301) 881-5310 D, 1-9 MASSACHUSETTS Urban Data Processing, Inc. Attn: Robert G. Coyne 20 South Avenue Burlington, Mass. 01803 (617) 273-0900 D, 1-4, 6-9 MICHIGAN Inter-University Consortium for Political and Social Research Attn: Dr. Jerome M. Clubb P.O. Box 1248 Ann Arbor, Mich. 48106 (313) 764-8508 or 763-5010 D, 1-3, 5, 10 71 ------- Computing Services Center Attn: Barbara B. Wolfe 5950 Cass Avenue Wayne State University Detroit, Mich. 48202 (313) 577-4777 or 577-4778 B, 1-3, 5, 7-9 Data Coordination Division Attn: Patricia C. Becker Planning Department City of Detroit 3400 Cadillac Tower Detroit, Mich. 48226 (313) 224-6389 B, 1, 3-6, 8-10 Michigan State University Attn: Anders C. Johanson Computer Laboratory • East Lansing, Mich. 48824 (517) 355-4684 B, 1-5, 7-10 Tri-County Regional Planning Commission Attn: Gerald J. Burger 2722 E. Michigan Avenue Lansing, Mich. 48912 (517) 487-9424 B, 1, 3-6. 8-10 Oakland County Advance Programs Group Attn: Jeffrey A. Kaczmarek 1200 North Telegraph Rd. Pontiac, Mich. 48053 (313) 858-0732 A, 1, 3-6 MINNESOTA Minnesota Analysis and Planning System Attn: Thomas A. Ehlen Agriculture Extension Service University of Minnesota 415 Coffey Hall St. Paul, Minn. 53108 (612) 376-7003 D, 1-6, 9, 10 MISSISSIPPI Department of Sociology Attn: Ellen S. Bryant Mississippi State University P.O. Drawer C State College, Miss. 39762 (601) 325-5024 B, 1, 3-6, 10 The Center for Population Studies Attn: Dr. Max W. Williams The Institute of Urban Research The University of Mississippi Bondurant Hall, 3W University, Miss. 38677 (601) 232-7288 B, 1-6, 10 MISSOURI Public Affairs Information Service Attn: Ed Robb University of Missouri-Columbia 10 Professional Building Columbia, Mo. 65201 (314) 882-8256 or 882-8367 C, 1-7, 9, 10 Office of Administration Attn: Ray Harrington Division of Budget and Planning P.O. Box 809 Room 129, Capitol Bldg. Jefferson City, Mo. 65102 (314) 751-2073 C, 1, 3-6 R-gov't Mid-America Regional Council Attn: Peter S. Levi 20 West Ninth Street Bldg. Third Floor Kansas City, Mo. 64105 (816) 474-4240 C, 1, 3-9 72 ------- University of Missouri-St. Louis Attn: John G. Blodgett Computer Center 8001 Natural Bridge Road St. Louis', Mo. 63121 (314) 453-5131 C, 1-5, 7-9 MONTANA Research and Information Systems Division Attn: R. Thomas Dundas, Jr. Montana Department of Community Affairs Capitol Station Helena, Mont. 59601' (406) 449-2896 B, 1-7, 10 NEBRASKA Academic Computing Services Attn: D.F. Costello University of Nebraska 3835 Holdrege Lincoln, Nebr. 68508 (402) 472-3763 C, 1-10 NEVADA Central Data Processing Division Attn: Gordon L. Harding Department of General Services State of Nevada Carson City, Nev. 89701 (702) 885-4091 B, 1-5, 7-9 NEW JERSEY Princeton- Rutgers Census Data Project Attn: Jane Wolin Center for Computer and Information Services Rutgers University Hill Center, Busch Campus P.O. Box 879 Piscataway, N.J. 08854 (201) 932-2483 D, 1-10 Princeton-Rutgers Census Data Project Attn: Judith S. Rowe Princeton University Computer Center 87 Prospect Avenue Princeton, N.J. 08540 (609) 452-6052 D, 1-10 NEW MEXICO Bureau of Business & Economic Research Attn: Lee Brown Institute for Applied Research Services Robert 0. Anderson School of Management University of New Mexico Albuquerque, N.M. 87131 (505) 277-2216 B, 1-6, 8, 9 NEW YORK Community Services Research and Development Program Attn: Frank Rens Department of Social and Preventive Medicine State University of New York at Buffalo 2211 Main Street Buffalo, N.Y. 14214 (716) 831-5521 B, 1-9 CDP Marketing Information Corp. Attn: Clifford W. Potanza 7 High Street Huntington, N.Y. 11743 (516) 549-5801 D, 1-4, 8, 9 National Planning Data Corp. Attn: Patricia Allard P.O. Box 610 Ithaca, N.Y. 14850 (607) 273-8208 D, 1-7, 10 73 ------- Market Statistics Attn: Edward J. Spar 633 Third Avenue New York, N.Y. 10017 (212) 986-1800 D, 1-5 Tri-State Regional Planning Commiss ion Attn: Max Schwartz World Trade Center New York, N.Y. 10048 (212) 938-3323 C, 1-9 Technical Assistance Center Attn: Gordon DeVries State University of New York Plattsburgh, N.Y. 12901 (518) 564-2214 B, 1-5 NORTH CAROLINA *Institute for Research in Social Science Attn: Susan Clarke University of North Carolina Manning Hall 026A Chapel Hill, N.C. 27514 (919) 966-2411 C, 1-6, 10 Systems Sciences, Inc. Attn: Edgar A. Parsons Box 2345 Chapel Hill, N.C. 27514 (919) 929-7116 D, 1-5, 7, 9 **North Carolina Department of Adminis tration Attn: Karan Bunn Division of State Budget and Management 116 West Jones Street Raleigh, N.C. 27603 (919) 733-7061 C, 1-6, 10 *North Carolina Department of Cultural Resources Attn: David Sevan State Library 109 East Jones St. Raleigh, N.C. 27611 (919) 733-3343 C, 1-6, 10 OHIO Northeast Ohio Areawide Coordinating Agency Attn: Frederick E.J. Pizzedaz- 1501 Euclid Avenue Cleveland, Oh. 44115 (216) 241-2414 A, 1-3, 5-9 Census Processing Center Attn: Michael Melton Battelle-Columbus Laboratories 505 King Avenue Columbus, Oh. 43201 (614) 424-7760 D, 1-9 **0hio Department of Economic and Community Development Attn: Jack Combs Office of Research 30 East Broad Street Columbus, Oh. 43215 (614) 466-2115 C, 1-7, 10 *0hio State University Libraries Attn: Bernard Bayer Mechanized Information Center 1858 Neil Avenue Mall Columbus, Oh. 43210 (614) 422-3480 C, 1-7, 10 OKLAHOMA University Computer Center Attn: Eldean Bahm Oklahoma State University Mathematical Sciences Bldg. Stillwater, Okla. 74074 (405) 624-6301 B, 1, 3, 5, 6 74 ------- OREGON Bureau of Governmental Research and Service Attn: Dr. Robert E. Keith University or Oregon P.O. Box 3177 Eugene, Ore. 97403 (503) 686-5234 B, 1-5, 7-9 Center for Population Research and Census Attn: Edward Schafer Portland State University P.O. Box 751 Portland, Ore. 97207 (503) 229-3922 C, 1, 3, 5-7 PENNSYLVANIA Robinson Associates, Inc. Attn: Morris Olitsky Bryn Mawr Mall 15 Morris Avenue Bryn Mawr, Pa. 19010 (215) 527-3100 E, 1-8, 10 Central Management Information Center Attn: L. Jeanne Yingling State of Pennsylvania Building #33, Bomb Rd. Harrisburg International Airport Middletown, Pa. 17057 (717) 787-1648 B, 1-3, 5 R-gov't, ed., quasi-public Contract Research Associates Attn: Marjorie L. McCann 251 Harvey Street Philadelphia, Pa. 19144 (215) 438-9391 C, 1-5, 9 Delaware Valley Regional Planning Commission Attn: Edward McNichol Penn Towers Building 1819 John F. Kennedy Blvd. Philadelphia, Pa. 19103 (215) 568-3211 A, 1-4, 6-9 K.H. Thomas Associates Attn: Kenneth H. Thomas University City Science Center Suite 200 3508 Market Street Philadelphia, Pa. 19104 (215) 382-2700 C, 1-6, 10 Innovative Systems, Inc. Attn: Robert J. Colonna 341 Fourth Avenue .Pittsburge, Pa. 15222 (412) 391-2364 C, 1-5, 8, 9 Southwestern Pennsylvania Regional Planning Commission Attn: Wade G. Fox 564 Forbes Avenue Pittsburgh, Pa. 15219 (412) 263-3500 B, 1-9 RHODE ISLAND Social Science Data Center Attn: Prof. James M. Sakoda Department of Sociology Brown .University Maxey Hall Providence, R.I. 02912 (401) 863-2550 B, 1-5 TENNESSEE Bureau of Business and Economic Research Attn: Paul R. Lowry Memphis State University Memphis, Tenn. 38152 (901) 454-2281 C, 1-6 R-gov't, quasi-gov't, nonprofit 75 ------- Regional and Urban Studies Information Center Attn: Dr. Andrew S. Loebl Oak Ridge National Laboratory P.O. Box X Oak Ridge, Tenn. 37830 (615) 574-5966 or 574-5957 D, 1-5, 7 TEXAS Institute of Urban Studies Attn: Dr. Frank W. Anderson The University of Texas at Arlington P.O. Box 19588 Arlington, Tex. 76019 (817) 273-3071 C, 1-5, 9, 10 Texas Natural Resources Information System Attn: C.R. Baskin Systems Central Office P.O. Box 13087 Austin, Tex. 78711 (512) 475-3321 B, 1, 3, 5-7, 9 National Planning Data Corp. Attn: Lawrence B. Miller Suite 208 4560 Belt Line Road Dallas, Tex. 75234 (214) 980-0198 D, 1-7, 10 Houston-Galveston Area Council Attn: Dr. Joe W. Pyle 3701 West Alabama Suite 200 P.O. Box 22777 Houston, Tex. 77027 (713) 627-3200 B, 1-6, 8-10 UTAH Population Research Laboratory Attn: Dr. Yun Kim Utah State University Logan, Ut. 84322 (801) 752-4100 Ext. 7662 C, 1-5, 8, 10 Bureau of Economic and Business Research Attn: R. Thayne Robson University of Utah Room 404 College of Business Building Salt Lake City, Ut. 84112 (801) 322-7274 B, 1-10 VIRGINIA C.A.C.I., Inc. - Federal Attn: Ronald C. Steorts 1815 N. Fort Myer Drive Arlington, Va. 22209 (703) 841-7800 D, 1-5, 7, 10 **Tayloe Murphy Institute Attn: Dr. Julie Martin Box 6550 University of Virginia Charlottesville, Va. 22096 (804) 924-7451 C, 1-5, 10 International Data and Development, Inc. Attn: J.C. Barrett P.O. Box 472 Merrifield, Va. 22116 (703) 525-7806 D, 1-5, 7-9 R-Private Companies **Division of Planning and Budget Attn: Robert Griffis 445 Ninth Street Office Bldg. P.O. Box 1422 Richmond, Va. 23211 (804) 786-7771 C, 1-5, 10 76 ------- Data Use and Access Laboratories Inc. (DUALabs) Attn: Deirdre Gaquin 1601 North Kent Street Suite 900 Rosslyn, Va. 22209 (703) 525-1480 D, 1-5, 10 R-gov't, nonprofit WASHINGTON Population, Enrollment, & Economic Studies Division Attn: John R. Walker Office of Financial Management State of Washington HOB (AL-01) Olympia, Wash. 98504 (206) 753-5617 B, 1-6, 10 WEST VIRGINIA State of West Virginia Attn: Daniel S. Green Program Support Services Governor's Office of Economic and Community Development State Capitol Complex Building 6, Room 548 Charlestown, W.Va. 25305 (304) 348-4010 or 348-2246 B, 1, 3, 5, 6 WISCONSIN **Wisc. Demographic Services Center Attn: Donald G. Holl Department of Administration Room B-110, 1 West Wilson Street Madison, Wise. 53702 (608) 266-1927 or 266-1067 C, 1-10 *Applied Population Laboratory Attn: Dr. Doris Slesinger University of Wisconsin 240 Agriculture Hall 1450 Linden Drive Madison, Wise. 53706 (608) 262-1510 C, 1-10 WYOMING Institute for Policy Research Attn: G. Fred Doll University of Wyoming P.O. Box 3925 University Station Laramie, Wyo. 82071 (307) 766-5141 B, 1, 3 77 ------- *SDC participating agency **SDC lead agency Geographic Coverage Codes: A-county or groups of counties in one or more States, D-all States, E-tapes obtained as required. Service Codes: 1-tape copies, 2-special files/extracts, 3-print-outs, 4-analytical rpts./area comparisons and profiles, 5-consultation, 6-map copies, 7-computer mapping/graphics, 8-address matching/geocoding, 9-GBF/DIME assistance, 10-training Restrictions Code: R (followed by organizations served) BUREAU REGIONAL OFFICES AND DATA USER SERVICES OFFICERS The 12 regional offices maintained by the Bureau of the Census in cities outside the Washington area offer a variety of services to users of Census Bureau data. These offices are now staffed with Data User Services Officers who can answer inquiries about census publications and other Bureau products, assist users in the access to and use of census data needed for specific applications, and make presentations to groups interested in the statistical programs and products of the Bureau. Regional office addresses and the names and phone numbers of Data User Services Officers are listed below. When writing, include "U.S. Bureau of the Census" in the address. Atlanta, Ga. 30309: 1365 Peachtree St., N.E., Room 638 Wayne Wall (404-881-2274) Boston, Mass. 02116: 441 Stuart St., 8th Floor Judith Cohen (617-223-0668) Charlotte, N.C. 28202: 230 South Tryon St., Suite 800 Lawrence McNutt (704-371-6144) Chicago, 111. 60604: 55 E. Jackson Blvd., Suite 1304 Stephen Laue (312-353-0980) Dallas, Tex. 75242: 1100 Commerce St., Room 3C54 Valerie McFarland (214-767-0625) Denver, Colo. 80225: P.O. Box 25207, 575 Union Blvd, Gerald O'Doimell (303-234-5825) Detroit, Mich. 48226: U.S. Federal Bldg. and Courthouse, Room 565, 231 W. Lafayette St. Timothy Jones (313-226-4675) Kansas City Kans. 66101: One Gateway Center, 4th and State Sts. Kenneth Wright (816-374-4601) 78 ------- Los Angeles, Calif. 90049: 11777 San Vicente Blvd., 8th Floor E.J. (Bud) Steinfeld (213-824-7291) New York, N.Y. 10007: 26 Federal Plaza, Federal Office Bldg., Room 37-130 Jeffrey Hall (212-264-4730) Phildelphia, Pa. 19106: William J. Green, Jr. Federal Bldg., Room 9226, 600 Arch St. David Lewis (215-597-8314) Seattle Wash. 98174: 915 Second Ave., Room 312 Larry Hartke (206-442-7080) 79 ------- |