EPA UOOl United States Region 5 September, 1989 Environmental Protection Air a id Radiation Division Agency 230 South Dearborn Street Chicago, HSinots £0604 4>EPA Estimation ars'd Evaluation of Cancer Risks Attributed to Air Pollution in Southeast Chicago ( Estimation and Evaluation of Cancer Risks Attributable to Air Pollution in Southeast Chicago John Summerhays Air and Radiation Division United States Environmental Protection Agency Region V Chicago, Illinois September 1989 Digitized by the Internet Archive in 2019 with funding from University of Illinois Urbana-Champaign Alternates / https://archive.org/details/estimationevaluaOOsumm Acknowledgements A study of this magnitude is not completed by a single individual! This report reflects knowledge possessed by nunerous people with expertise on various source types and pollutants. Both in the technical development of emissions and risk estimates and in the documentation of this study, the assistance and advice from many people made this study far better than would otherwise have been possible. Particularly noteworthy are the contributions by Tom Lahre of the Noncriteria Pollutant Programs Branch of the Office of Air Quality Planning and Standards (OAQPS). Through Tom's arrangements the Noncriteria Pollutant Programs Branch provided contractual assistance for the dispersion and risk analyses in this study, without which this study would not have been possible. Tom also provided valuable information, feedback, and comments in both the emissions estimation and risk analysis phases of this study. The Illinois Environmental Protection Agency and the Indiana Department of Environmental Management made Important contributions to this study. These agencies sent out questionnaires to industrial facilities and supplied key information used in the study. The author wishes to acknowledge important assistance from other employees of Region V working on this study. Dr. Harriet Croke compiled emissions estimates for many industrial facilities and managed a contract to develop emissions estimates for waste handling. Also assisting in developing emissions estimates were Barry Bolka and Mardi Klevs. Special appreciation is also extended to Carole Bell and Melody Noel who typed this report. Several other individuals made significant contributions Dr. Milton Clark, of Region V's Office of Health and Environmental Assessment, provided useful advice and comments on the report. Fred Hauchman. of the Pollutant Assessment Branch of OAQPS, served an important role as a central source of Information on unit risk factors. Dr. IIa Cote, formerly also with the Pollutant Assessment Branch, provided significant conments and feedback on health impact assessment. Loren Hall, of the Office of Toxic Substances, provided useful information and constructive comments In both the emissions estimation and risk analysis phases of the study. American Management Systems provided contractual assistance in loading and refining PIPQUIC, a data handling system for urban risk assessments. Midwest Research Institute and Alliance Technologies provided contractual assistance In assessing emissions from waste handling facilities. Valuable review and comments were provided by Penny Carey (Office of Mobile Sources) and Cheryl Siegel-Scott (Office of Toxic Substances). Finally, a lengthy list of other individuals contributed other information on emissions from particular source types or on other aspects of the study. i i 1 TABLE OF CONTENTS Section Page Tables iv Figures v Sunmary vi Introduction I Study Design 3 Emissions Estimation 7 Estimation of Concentrations by 14 Atmospheric Dispersion Modeling Comparison of Modeling and Monitoring 19 Concentration Estimates Evaluation of Cancer Risk Factors 26 Incidence and Risk Estimates 32 Conclusions 49 References 53 i v TABLES Number Page la. Emissions in Source Area by Source Category and Pollutant 12 lb. Other Substances in Study 13 2. Monitoring Studies Conducted in Southeast Chicago 21 3- Comparison of Modeled-Versus Monitored-based Concentration Estimates for Organic Toxicants 22 4. Comparison of Modeled-Versus Monitored-based Concentration Estimates for PCBs 24 5. Comparison of Modeled-Versus Monitored-based Concentration Estimates for Particulate Toxicants 25 6. Carcinogenicity of Inventoried Pollutants 29 7. Contributions to Area Cancer Cases by Source Type and Pollutant Across the Study Area 34 8. Estimated Contributions to Lifetime Cancer Risk at the Grid with the Highest Estimated Number of Cancer Cases 45 V FIGURES Number Page A. Contribution to Estimated Annual Cancer Cases by Source Type vii la. Southeast Chicago Study Area - Source Area 4 lb. Southeast Chicago Study Area - Receptor Area 5 2. Map of Estimated Coke Oven Pollutant Concentrations 17 3. Map of Concentrations of Polycyclic Organic Matter 18 4. Contributions to Estimated Annual Cancer Cases by Source Type 33 5. Relative Distribution of Estimated Lifetime Cancer Cases 35 6. Breakdown by Source Category of Contributions to Estimated Cases 37 7. Contributions to Estimated Cases from Consumer- oriented Sources 38 8. Contributions of Various Types of Solid Waste Handling 40 9. Contributions to Estimated Annual Cancer Cases by Pollutant 41 10. Map of Estimated Lifetime Cancer Risks from Air Pollutants in Southeast Chicago ‘ 43 11. Estimated Lifetime Cancer Risks from Air Pollutants 44 12. Contributions to Estimated Risk at the Peak Incidence Location 46 SUMMARY Increasing concern has developed that air pollution may cause significant cancer risks in urban areas due to the combined effects of multiple sources and multiple pollutants. Given the density of exposed populations in urban areas, the possibility of high risks would further suggest that the number of incidences of resulting cancer cases may also be relatively high. The- Southeast Chicago area has both a substantial concentration of industrial and non-industrial emission sources and a relatively high population density exposed to these emissions. This study was undertaken to evaluate the extent to which this exposure to ambient (outdoor) air contaminants may be a public health problem and to provide an informed basis for determining what emission reductions if any, might be warranted to reduce the exposure. The study sought to use as broad a base of information as possible in evaluating air pollution-related cancer risks In the Southeast Chicago area. The study considered every air toxicant for which the United States Environ¬ mental Protection Agency (USEPA) can estimate a quantitative relationship between the exposure to the air toxicant and the resulting increase in the probability of contracting cancer. All source types for which emissions of the identified pollutants could be quantitatively estimated were included. Estimates were made of emissions in a relatively broad area, so that impacts both from nearby sources and from more distant sources could be included. The National Academy of Sciences has defined risk assessment as a process having four steps: hazard identification, exposure assessment, assessment of dose- response relationships, and risk characterization. The hazard identified for assessment in this study is cancer due to ambient air contamination. The exposure assessment principally involves estimation of ambient atmospheric concentrations which, for most pollutants, were estimated by first deriving an inventory of emissions, and then estimating atmospheric dispersion of these emissions. The assessment of dose-response relationships involves derivation of a unit risk factor, which expresses the probability or risk of contracting cancer that is associated with exposure to a unit concentration of air pollution. Finally, risk characterization involves deriving various measures of risk. The simplest measure of risk is individual risk, representing the risk attributable to air contaminants at a specific geographic location. An alternative measure of risk is the number of cancer cases attributable to air contaminants estimated to occur among the population in the study area. In addition to estimating these general measures of cancer risk, this study also investigated the origins of these risks and incidences, i.e., which source types and which pollutants are the most significant probable causes of these individual and area-wide risks estimated to result from air pollution in the Southeast Chicago area. It must be noted that the risk estimates presented in this report should be regarded as only rough approximations of total cancer cases and individual" lifetime risks, and are best used in a relative sense. Estimates for indivi ¬ dual pollutants are highly uncertain and should be used with particular caution . This study found atmospheric emissions of 3D pollutants in the study area which USEPA considers to be carcinogens. Some of thes« pollutants have been shown to be carcinogenic based on human exposure data, and others have been impli¬ cated by animal studies. The cumulative total number of cancer cases that this study estimated to be attributable to air pollution is about 77 cases over 70 years or about 1 per year. The area for which exposure was assessed has a population of about 393,000 residents. Therefore, the average risk across the area due to air pollution as estimated by this study is approximately 2.0x10"^, or about 2 chances in 10,000. It should be noted that, as a national average across the United States, the chance of contracting cancer over a lifetime from a number of factors (including both voluntary and involuntary exposures) which are not fully understood, is about one chance in three. One in seven people die from cancer. Several types of sources appear to contribute significantly to the cancer cases estimated to result from air pollution in Southeast Chicago. Figure A is a pie chart of the contributions of various source types to cancer cases in the area. The most significant source type is steel mills, particularly the coke ovens found at steel mills. Steel mills appear to contribute about 37% of the total estimated cancer incidence. Emissions from other industrial facili¬ ties, primarily chrome platers, are estimated to cause approximately 18% of the incidence. Roadway vehicles are estimated to cause about 16% of the total cancer cases, and consumer-oriented area sources (e.g., home heating and gaso¬ line marketing) contribute about 8%. Furthermore, the background pollutant impacts from formaldehyde and carbon tetrachloride contribute almost the entire remaining 21%. Together, these source types account for about 99.7% of the estimated air pollution-related cancer risks in the area. This study also provides useful information on what source categories in the area make only minor contributions to the total estimated cancer risks. In terms of estimated contributions to overall area cancer incidence, facilities with hazardous and non-hazardous waste (including landfills, two hazardous waste incinerators, and liquid waste storage tanks and including abandoned hazardous waste sites) contribute about 0.15% of the total, and wastewater treatment plants contribute about 0.14% of the total. Thus, these facilities are clearly estimated to cause much less risk in the Southeast Chicago area than the more dominant source types discussed previously. It is useful to apportion the estimated total number of cancer cases according to the weight of evidence that the pollutants are carcinogenic. According to USEPA's review of the weight of evidence of carcinogenicity, the 30 pollutants for which risks were estimated in this study include 6 "known hunan carcinogens" 22 "probable human carcinogens, and 2 "possible human carcinogens". Of the estimated 77 cancer cases per 70 years, about 58% are attributable to pollu¬ tants that USEPA labels "known human carcinogens," almost 42% are attributable to "probable human carcinogens," and only about 0.03% are attributable to "possible human carcinogens." This study also estimated lifetime individual risks in an array of locations. A peak lifetime risk of about 5x10"^ (or about 5 chances in 1,000) is estimated Figure A. Contributions to Estimated Cases by Source Type vl 11 ix in the study area. However, available Census Bureau information does not indicate any residents in this area. The square kilometer with the highest estimated number of cancer cases has an estimated lifetime risk of about 9xl0‘ 4 (9 in 10,000). In general , risks are greatest in the northeast part of the area and are relatively lower in the less populated southern and western part of the area. The average lifetime risk across the area is about 2.0xl0 -4 (about 2 in 10,000). Consideration of the results of this study should include consideration of various uncertainties inherent in the study. The estimation of emissions generally relies on extrapolation of studies of emission sources elsewhere to the sources in the Southeast Chicago area. In addition to uncertainties in quantitative emissions estimates, there is also qualitative uncertainty since we may not be aware of some sources and source types for some pollutants. Atmospheric dispersion modeling also introduces uncertainty in the estimation of ambient (outdoor) concentrations. Finally, there are significant uncer¬ tainties in the unit risk factors used in this study, due to the necessity for various extrapolations from the exposure conditions in the studies deriving the risk factors to the exposure conditions in the Southeast Chicago area. It is difficult to judge whether the risks in this study are more likely to be underestimated or overestimated. Comparison of monitoring data to the modeling data used in this study suggests that most pollutants are reasonably well addressed, but some pollutants appear underestimated. Thus, this comparison suggests that actual risks may in fact be higher than indicated in this study. Conversely, the conservatism underlying the unit risk factors used in this study implies that actual risks may be lower. Both types of uncertainty appear to be relatively modest for some pollutants and relatively major for other pollutants. Thus, the risk estimates derived in this study may either overstate or understate actual risks. This study did not evaluate routes of exposure to environmental contaminants other than ambient air pollution. While most if not all the water consumed in the area is from Lake Michigan, and not groundwater, drinking water is another potential source of risk. Other environmental exposures include indoor air pollution (including radon gas), fish consumption and dermal exposure. Further, there may be other potential carcinogens or source categories which have not yet been identified. This study identifies various aspects of air toxics exposure in Southeast Chicago that warrant further study. Several such investigations are currently underway. At the same time, the study suggests that options for reducing risks due to air pollution in Southeast Chicago should be investigated. This study identifies the source categories which contribute most to risk in the area and, therefore, most warrant control. The States and USEPA are working toward regulating several of the important source types that this study indicates are significant. It is hoped that this study will form a basis for further discussions concerning the reduction of cancer risks potentially attributable to air toxic emissions in the Southeast Chicago area. Introduction Increasing national attention has focused on the health risks from "toxic" (non-criteria) air pollutants that arise in urban areas where a concentrated level of industrial activity coexists with high population density. Within Region V, an area that combines concentrated industrial activity with high popu¬ lation density is Southeast Chicago. In particular. Southeast Chicago and the surrounding area is one of the nation's foremost locations for integrated steel production and a wide range of other manufacturing activity. This area also has one of the nation's five facilities permitted for polychlorinated biphenyls (PCB) incineration and has a variety of other facilities for treating, storing and disposing of hazardous waste. Therefore, Region V of the United States Environmental Protection Agency (USEPA), with assistance from the Illinois Environmental Protection Agency (IEPA) and the Indiana Department of Environ¬ mental Management (IDEM), has completed an extensive study of air toxicants in the Southeast Chicago area. The goal of this study has been to obtain a broad understanding of the risks of cancer that may be attributable to inhalation of ambient air pollutants found in the Southeast Chicago area. The National Academy of Sciences defines four steps of risk assessments: hazard identification, exposure assessment, evaluation of dose-response relationships for the pollutants in the study, and estimation and characterization of risk. Hazard identification involves identifying an exposure scenario, in this case inhalation of air contaminants, which may be causing adverse health effects. Exposure assessment involves evaluating the ambient concentrations of the pollutants to which the public is exposed. The principal method for assessing exposure in this study is to estimate emis¬ sions and then estimate atmospheric dispersion of these emissions. The evalua¬ tion of dose-response relationships in this study involves the estimation of cancer risk factors, representing the cancer risk estimated to result from breathing a unit concentration (e.g., one millionth of a gram per cubic meter of air). Finally, estimation and characterization of risk involves compiling and analyzing all this information in a way that provides useful statements about risk. It is instructive to compare the methods of risk assessment used in this study to the methods of epidemiological studies of cancer statistics. Epidemiological studies provide a more direct means of considering the impact of environmental contaminants on cancer rates. Unfortunately, due to the difficulties of distinguishing environmental factors from other factors, such studies are often inconclusive. Further, such studies generally do not even attempt to consider the separate influences of the various sources of the various environmental contaminants. The study described in this report thus has different purposes from the purposes of epidemiological studies. Epidemiological studies, if conclusive, can provide a better evaluation of the correlation between air pollution and cancer statistics. However, this study provides a more detailed data base on the potential relative significance of different source types and different pollutants. Further, due to the long periods of exposure that are considered to be involved in cancer induction, current cancer statistics probably reflect exposures over the last several decades. In contrast, this study addresses cancer risks that USEPA methods of risk assessment would associate with current air pollutant concentrations. (This study may be considered to estimate future risks if air pollutant concentrations were to remain constant at current levels for the next several decades.) Furthermore, given the mobility of population in the United States, cancer statistics reflect exposure in 2 % multiple areas where members of the studied population have lived. In contrast, this study focuses specifically on estimated impacts of exposure to pollutant concentrations in the Southeast Chicago area. Thus, this study more serves the purpose of evaluating which source types and which pollutants are best addressed in order to reduce the future cancer risks that current risk assessment methods suggest may result from air pollution in the Southeast Chicago area. This study may be considered in the context of national concern about urban air toxics issues. A USEPA report entitled The Air Toxics Problem in the United States: An Analysis of Cancer Risks for Selected Pollutants (dated May 1985) estimates that as many as 1800 to 2400 cancer cases per year may be attributed nationally to air pollution (not including indoor radon). This report further finds that while individual industrial operations may lead to high localized risks, a much greater share of the cumulative risk from air toxicants comes from activities that are more population-oriented, such as driving motor vehicles and heating (with fireplaces and wood stoves). In fact, limited monitoring data in some large cities indicates that risks even in residential and commercial areas approach the risks found near the highest risk industrial facilities. Further, various studies suggest that cancer risks from air pollution throughout urban areas are commonly in the range of lxl0"3 (i.e., 1 case per thousand people exposed for a lifetime) to IxlO” 4 (1 case in 10,000). These risks arise from the multiple sources of emissions of multiple pollutants that exist in all urban areas. Since 61% of the United States population lives in urbanized areas, and the exposure to high urban toxics risks extends throughout these urban areas, this urban air toxics exposure appears to contri¬ bute the major share of the cases of cancer attributable to air pollution. The purpose of the Southeast Chicago study, then, given the general national picture of urban air toxics risks, is to define, in more detail, the relative contri¬ butions of various source types to that risk in this geographic area. Conducting a study like this requires substantial computerized data handling. Data handling for developing emissions estimates required specifically developed computer programs. Dispersion modeling, risk estimation, and cancer incidence estimation relied heavily on a data handling system known as PIPQUIC (Program Integration Project Queries Using Interactive Commands). PIPQUIC also provided many of the figures shown later In this report. This report includes eight sections. This introduction has focused on the context in which this study was conducted. The next section describes several of the general features of the design of this study. The third section summarizes the procedures and results of the emissions inventory phase. The fourth section describes the exposure assessment, particularly describing the atmospheric dispersion modeling used as the principal method for esti¬ mating pollutant concentrations, and also providing a sampling of the concen¬ tration outputs of this study. The fifth section compares the modeled concen¬ tration estimates against concentration estimates based on monitoring. The sixth section describes the dose-response relationships (i.e., the health impacts associated with given concentrations) used to estimate risks. The seventh section then presents results of the risk estimations, discussing the estimated magnitude of the cancer risk attributable to air pollutants, the relative contributions of different source types and pollutants, and the spatial distribution of the risks over the studied receptor area. The final section summarizes the conclusions of this study. 3 Study Design The first step in this study was to plan a study design. A key decision here was whether to develop a screening study covering multiple pollutants and multiple source types using only readily available information or whether to develop a more focused inventory investigating only a few pollutants and source types. This study was designed for screening purposes, to provide an overview of excess cancer risks that may be attributable to ambient air pollution in the area. This study has been designed to be comprehensive in several respects. First, it has attempted to include all source types that emit any of the substances being studied. Second, although the focus of this study is on exposure in a moderately sized area (approximately 65 square miles), a much broader area was inventoried to include all sources with potentially significant impacts in the selected receptor area. Third, this study attempted to address a comprehensive list of potential carcinogens. With respect to source types, this study included all source types for which air toxics emissions could be estimated. A special aspect of this study was the inclusion of the volatilization from wastewater treatment plants, and emissions from various kinds of facilities with hazardous and municipal solid waste. Specific kinds of such facilities addressed in this study include hazardous waste treatment, storage, and disposal facilities (TSDF's), abandoned hazardous waste sites, and landfills for municipal waste. Emissions from these source categories are difficult to estimate and are not included in traditional air pollutant emissions inventories. However, they were included in this study due to national and local interest in their relative contribution to risk. Also included were source types which have more traditionally been inventoried, such as industrial facilities, population-oriented sources (e.g., dry cleaning) and highway vehicles. Although more information is generally available to estimate emissions from these types of sources, the derivation of emissions factors for the substances inventoried in this study nevertheless required substantial literature research and then development of factors suitable for use in this kind of inventory. This study did not involve direct emissions measurements; instead, emissions estimates reflected production rates of sources in the area (e.g. tons of steel produced) in conjunction with results from various studies of the relationship between production and emissions (e.g., pounds of emissions per ton of steel produced). With respect to spatial coverage. Figure la is a map showing the broad "source area" included in the inventory, and Figure lb is a map showing the smaller target "receptor area" for the exposure analysis. The focus of this study is on air pollutant concentrations in the receptor area and on the cancer impacts that exposure to these air pollutants in this area may cause. However, it is clear that the air quality in this area is affected by emissions that can be transported in from a much broader area. Consequently, emissions were Inventoried for a much broader area. For purposes of this study, the "Southeast Chicago" receptor area was defined as an area that is approximately a 13 kilometer (8 mile) square, having a total area of 169 square kilometers (65 square miles). This area covers much of the southeast corner of the City of Chicago plus portions of adjoining SOUTHEAST CHICAGO ST UDY AREA Figure la. 4 SOUTHEAST CHICAGO STUDY AREA Figure lb. 87th St 5 6 suburbs, ranging specifically from 87th Street to Sibley Boulevard and from Western Avenue to the Indiana State line. This area has a population of about 393,000. By comparison, the inventoried source area covers a 46 kilometer (about 29 mile) square area, with a total area of 2136 square kilometers (about 817 square miles). Since the prevailing winds in the area are from the southwest quadrant, the source area is skewed toward the south and west of the receptor area. The specific boundaries of the source area are, in terms of UTM (Universal Trans¬ verse Mercator) coordinates, from 4584 to 4630 kilometers northing and from 420 to 466 kilometers easting in zone 16. This source area extends 30 kilometers south and west and 16 kilometers north and east of the center of the receptor area. Thus, the emissions study area includes roughly a third of the City of Chicago, most of the city's southern and southwestern suburbs, and a portion of Northwest Indiana. This source area has a population of about 2,361,000. The inventory further includes a few additional point sources outside of this source area which were judged to be potentially significant sources. With respect to pollutants, this study included all potential carcinogens for which a quantitative relationship between air concentration and risk has been estimated. During the initial design of the study, unit risk factors had been estimated for 47 of the 51 substances on the targeted pollutant list. However, further review led to the conclusion that for many of these 47 substances, the evidence of carcinogenicity is too weak or the cancer risk factor estimates are too unreliable to use in this study. This further review concluded that 32 substances had reasonable evidence of being carcinogenic and risks could rea¬ sonably be quantified. Thus, the study list of 51 substances includes 15 sub¬ stances which may or may not be carcinogenic, but could not be quantitatively analyzed, and 4 substances that were included only on the basis of potential noncarcinogenic impacts. (As will be discussed below, all but 2 of the 32 quantifiably carcinogenic pollutants were found to have atmospheric emissions in the studied source area.) Analysis of systemic, noncarcinogenic health effects was considered beyond the scope of this study. First, Agency-reviewed dose-response data for systemic effects due to inhalation of air contaminants were not available at the Inception of this study. Second, analysis of systemic health effects generally requires consideration of concentration thresholds below which no adverse health effects are observed. Therefore, it is necessary to conduct a substantially different and more complicated exposure assessment to evaluate the extent and frequency with which the threshold may be exceeded. Thus, this study focused on cancer effects of the 32 pollutants with agency-reviewed risk factors. As indicated above, this study primarily used emissions estimates in conjunction with atmospheric dispersion modeling rather than using monitoring data to esti¬ mate ambient concentrations of the pollutants being studied. Both methods have advantages and disadvantages as approaches for estimating ambient concen¬ trations. The advantages of modeling include the ability to address concentra¬ tions across an entire geographic area, to address long term average concentra¬ tions, and to estimate concentrations below the concentration levels that avail¬ able monitoring methods can detect. The corresponding disadvantages of monitoring data are that resource constraints generally limit the collectable data to one or a few locations and for relatively short time periods. Additionally, moni- 7 toring methods are not available for some pollutants, and for other pollutants, monitoring cannot detect some of the concentrations of interest. A further advantage of the emissions estimation/dispersion modeling approach is that it readily identifies the separate contributions of sources and source categories to any given concentration, which monitoring data alone cannot do. For these reasons, the emissions estimation/dispersion modeling approach was judged a better means of evaluating concentrations throughout the study area and judged to be a more informative approach, particularly in describing relative contri¬ butions of different source types. On the other hand, monitoring data have the advantage that for the time and location being monitored, and if concentrations are detectable, the uncertainties are generally less than the uncertainties inherent in emissions inventorying and dispersion modeling. For this reason, monitoring data can be used to obtain a "reality check", to suggest at least for the locations and pollutants successfully measured whether or not the modeled concentrations are approximately correct. A further advantage of monitoring is the ability to assess concentrations (at least if concentrations are above detection limits) of atmospheric contamination which is not the direct result of current emissions. A corresponding disadvantage of the emissions estimation/dispersion modeling approach is that this approach is unable to consider such "background impacts". For most pollutants in this study, "background concentrations" may be presumed to be overwhelmed by urban area emissions, and such background concentrations may reasonably be ignored. However, two pollutants in this study are presumed to have origins other than current emissions: formaldehyde and carbon tetrachloride. Although current emissions of these pollutants contribute to ambient concentrations, most of the ambient concentrations are attributable to other origins. Much of the formal¬ dehyde concentration is presumed to be attributable to atmospheric photochemical reaction of other organics. Since carbon tetrachloride remains unreacted in the atmosphere for a very long time, current concentrations are largely the result of an accumulation of historic emissions over wide geographic areas. Thus, monitoring data were used in this study to indicate the concentrations of these two pollutants from origins not addressed by the emissions estimation/dispersion modeling approach. The term "background pollutants" is used in this report to identify these origins of risk. Emission Estimation The emissions inventory is described in separate reports. A detailed description of the inventory is given in a July 1987 report entitled "Air Toxics Emissions Inventory for the Southeast Chicago Area", authored by John Summerhays and Harriet Croke. This report docunents emissions estimates for a wide range of source types, Including source types that are traditionally inventoried in air pollution studies as well as some source types that are not traditionally inventoried such as volatilization from wastewater at sewage treatment plants. An addendum to this report (dated August 1989) updates this report by describ¬ ing limited revisions to the previously described inventory and by describing procedures and results of estimating air emissions from various waste handling sources including facilities for the treatment, storage, and disposal of hazardous waste, from abandoned hazardous waste sites, and from landfills storing municipal waste. Further details on the estimation of air emissions from the handling of hazardous and nonhazardous waste are provided in two reports by the Midwest Research Institute: "Estimation of Hazardous Air Emissions in Southeast Chicago Contributed by TSDF's", covering air emissions from the treatment, storage, and e disposal of hazardous waste, and "Estimation of Hazardous Air Emissions From Sanitary Landfills", covering air emissions from landfills for ordinary muni¬ cipal solid waste. Further details for abandoned waste sites are given in a report by Alliance Technologies Corporation entitled "Estimation of Air Emissions form Abandoned Waste Sites in the Southeast Chicago Area." The reader interested in more details of the procedures, data sources, and emissions estimates should consult these separate reports. The discussion that follows will present only an overview of the development and results of the emissions inventory. This study involved no direct measurement of emissions. Instead, emissions estimates in this study were generally based on local activity levels (e.g. point by point steel production or local traffic levels) in conjunction with the results of measurement studies elsewhere establishing the relationship between activity levels and emissions (e.g.. emissions per ton of steel produced or per mile driven). This approach is used partly because emissions measurements even just for the 88 industrial facilities in this study would be prohibitive!y expensive, and partly because limited emissions measurements do not necessarily provide representative long-term data on emissions. The sources considered in this study include industrial sources, consumer- oriented sources (e.g. dry cleaning and gasoline marketing), roadway vehicles, facilities for handling hazardous and municipal waste, and wastewater treatment plants. From another perspective, many of the industrial sources as well as the waste handling facilities and the wastewater treatment plants are at clearly identified locations, and are labeled "point sources." whereas other industrial activities as well as all of the consuner-oriented sources and roadway vehicles, are more broadly distributed, and are labeled "area sources." The distinction between point and area sources leads to the use of different methods for esti¬ mating emissions. For industrial point sources, three emission estimation methods were used. These methods may be labeled the questionnaire method, the species fraction method, and the emission factor method. In the first method, questionnaires were sent to 29 companies considered candidates for being significant sources of air toxics emissions. These questionnaires requested the annual emissions for each pollutant in this study, as well as stack data necessary for dispersion modeling. These questionnaires were sent by the Illinois Environmental Protection Agency and the Indiana Department of Environmental Management. Region V then reviewed these company responses to assure that complete and reasonable emissions esti¬ mates would be used for these facilities. The second, species fraction method, was used for 59 other identified facilities. This method begins with estimates of emissions of total organic emissions and total suspended particulate emissions, estimates which are based on the best available information on plant operating rates and estimated emissions per unit operation. This method then calls for multiplying these emissions totals times species fractions, expressed as the ratios of the particular species emissions versus the total emissions, thereby estimating species emissions. For example, particulate emissions from blast furnaces (e.g.. Standard Classification Code 3-03-008-25) were estimated to be 0.013* arsenic, and so a blast furnace casthouse that emitted 20 tons per year of particulate matter would be estimated to emit 0.0026 tons per year of arsenic. Similarly, for coke ovens, "coke oven 9 emissions” (expressed as benzene soluble organics) are estimated as 1.1 times total particulate emissions (thus including a fraction of the particulate emissions plus a gaseous organic fraction.) The third, emission factor method, uses a direct emission factor, expressing the quantity of a particular species emitted per unit activity level (e.g. per 1000 gallons of paint solids). The emission factor is multiplied times the actual level of activity to estimate total emissions. This method was only used for one type of source (coke by-product recovery plants), since for all other point source types the direct emission factors were either not available or the source types were not found in the Southeast Chicago area. For area-type sources, both the species fraction method and the emission factor method were used. As an example of the species fraction method, roadway vehicles were inventoried by multiplying total emissions of organics times measured or derived species fractions. As an example of the emission factor method, wood combustion emissions were estimated by multiplying estimates of wood quantities burned in fireplaces and wood stoves times an emission factor of the quantity of the pollutant, polycyclic organic matter, per pound of wood burned. The companion emissions inventory reports provide more details of the methods used for each category in this study, as well as a discussion of the advantages and disadvantages of the two methods. A further issue to be addressed in inventorying area and mobile sources is the spatial distribution of these emissions. The Impacts of given quantities of emissions at any particular location are a function of how distant and how frequently upwind the emission sources are from the impact location. By definition, area sources are collections of sources too numerous and too dis¬ persed to identify the location of each source. The solution to this problem used in this study was to distribute emissions according to the distribution of "surrogate parameters" such as population, housing, or manufacturing employment. For example, it would not have been feasible to identify locations of the estimated 2650 buildings with air conditioner cooling towers, not to mention identifying the approximately 15% of those towers which use chromium as a corrosion inhibitor. Instead, these emissions were distributed in accordance with the known distribution of nonmanufacturing, nonretail employnent. Simi¬ larly, roadway vehicle emissions on freeways and other roadways were distributed according to traffic estimates for freeway and other roadway travel. In addition to inventorying the above, which are relatively traditional air pollution source types, this study also Included several source types that have not traditionally been included in air pollution inventories. One such source category is hazardous waste treatment, storage, and disposal facilities (TSDFs). The Southeast Chicago study area includes a total of 43 facilities regulated under the Resource Conservation and Recovery Act to handle hazardous waste. Included among these facilities is one of the nation's five incinerators of polychlorinated biphenyls (PCBs), a second incinerator handling non-PCB hazardous waste, a hazardous waste landfill, several facilities storing waste in storage tanks, and a majority of facilities loading wastes into drums or trucks. Estimating emissions for TSDFs required several steps. The first step was identifying facilities. The second step was obtaining data on the quantity of 10 each type of waste handled by each facility. The third step was reviewing studies of the composition of various waste streams to estimate the quantity of individual pollutants in the waste streams at each facility. Finally, emissions estimation models were used, relying on the derived estimates of waste quantities and often relying on assumptions about operating procedures to estimate emissions of each pollutant at each facility. Most of these emissions estimates were derived by Midwest Research Institute under contract to USEPA Region V, with Region V deriving a few additional emission estimates. A second type of source for which air impacts have rarely been studied are abandoned hazardous waste sites (sites potentially to be addressed in USEPA's Superfund program). Estimation of emissions for these sites followed procedures very similar to the procedures used for TSDFs. A third type of facility which has not traditionally been Included in air pollution studies, but was included in this study, is municipal waste landfills. Biodegradation in landfills generates methane, and this methane can carry trace amounts of contaminants contained in household and industrial solid waste into the atmosphere. The first step in estimating these emissions was to review available data on the contaminant concentrations found in gases emanating from landfills. The second step was to estimate landfill gas generation rates based on the estimated volumes of landfill gases for each landfill in the study area. The third step multiplied the results of the first two steps to estimate the emissions of each species of concern from each landfill. These estimates were again developed by Midwest Research Institute under contract to USEPA Region V. A fourth source type not traditionally included in air pollution studies but included in this study was wastewater treatment. The focus in this study was on two wastewater treatment plants handling the largest volumes of industrial wastewater in the source area, i.e. the Calumet and the West-Southwest treat¬ ment plants. For each of these plants, the Metropolitan Sanitary District of Greater Chicago made measurements of the volatile organic concentrations in the wastewater entering and exiting each of these facilities for seven consecutive days. Daily quantities of volatile organics were computed by multiplying the wastewater concentrations of each compound of interest times the respective day's volume of wastewater, after which the seven days' quantities were averaged. The next step of the analysis was to address the fate of these contaminants. Possible fates for contamination in the influent wastewater include volatili¬ zation to the atmosphere, biodegradation in the treatment plant, sludge, and treated wastewater leaving the plant. Contaminants in the wastewater leaving the treatment plant, where significant, were addressed by subtracting outgoing contaminant quantities from incoming contaminant quantities. Partitioning to sludge was in all cases insignificant. Nevertheless, volatilization from sludge is included, insofar as sludge contamination was inventoried as if the contaminants remained in the wastewater available to volatilize. Most wastewater contamination either volatilizes or biodegrades. Based on studies measuring volatilization and biodegradation for nonpolar organic solvents (the most significant contaminants considered here) at other wastewater treatment facili¬ ties, it was assuned that volatilization accounts for 40% of incoming contami¬ nation (minus any adjustment for contamination in outgoing wastewater) and biodegradation accounts for the remaining 60%. This study also addressed several other source categories which may be relatively unimportant with respect to the "traditional" (criteria) air pollutants but which have the potential to be significant with respect to toxic air pollutant emissions. While these categories generally emit relatively small quantities of the traditional pollutants, the materials being emitted appear to be highly toxic. Examples of such source categories included in this study are chrome electroplaters (emitting chromium), wood combustion in fireplaces and wood stoves (emitting polycyclic organic matter, a component of "wood smoke", as a product of incomplete combustion), and hospitals (emitting ethylene oxide used in some sterilizing operations). It should be noted that all emissions estimates were, in general, compiled for a 1985 base year. A minor deviation from use of 1985 data is the deletion of sources which are known to have permanently shut down since that time. In addition, the estimates compiled in this study are for typical actual emissions. No attempt was made to evaluate emissions for the scenario in which al 1 plants emit maximum allowable amounts, because this scenario is unlikely to persist continuously over a 70 year lifetime. An important influence on emissions from many source categories is the existence of emission controls. This study sought to develop emissions estimates appropriate to 1985 levels of emission control. A special effort was made to assure that steel mill emissions estimates reflect the current status of controls. For other point sources it is less clear whether emission controls adopted according to various regulations are, in fact, represented in the emis¬ sion estimates used in this study, though again, the goal was to use emission estimates that correspond to 1985 levels of control. For roadway vehicles, the emission estimates reflected elaborate, computer-assisted evaluation of what portion of the vehicle fleet had what degree of emission control as of the 1985 inventory date. In particular, the MOBILE 3 emission factor model was used in conjunction with some updates for the consideration of evaporative emissions. It is noted that more recent information suggests that evaporative emissions may be much higher due to "running losses." For other types of sources, for the few source categories where emissions controls are in place, this study attempted to use emissions estimates that reflect these controls. One special element of the emissions inventory development was the use of data on facility emissions that Section 313 of the Superfund Anendments and Reauthorization Act requires companies to submit. In particular, companies are required under this Section to develop and report emissions estimates for numerous pollutants including most of the pollutants in this study. These data were compared with the emissions estimates that were independently derived in this study. Unfortunately, these reports do not address area sources. Nevertheless, these data were used for additional refinement of the industrial source component of the Southeast Chicago area inventory. Table la summarizes the emissions of known or suspected carcinogens found in this study. In the study area, 30 pollutants were found which USEPA considers carcinogenic. This table distinguishes emissions from steel mills, other industrial operations, consuner-oriented sources, roadway vehicles, hazardous waste treatment storage, and disposal facilities (also including municipal waste landfills), and wastewater treatment plants. This table shows that 30 of the 32 known or suspected carcinogens were found to be emitted in the Southeast Table la. Emissions in Source Area by Source Category and Pollutant (in metric tons/year) Compound* Acryl amide Steel Mills 4 Other Industrial Sources o02 Consuner Sources Mobile Sources Waste Facilities Sewage Treatment Plants Total .02 Acrylonitrile 1.0 .002 1.0 Arsenic 3.9 1.2 5.1 Asbestos .02 .04 .06 Benzene 3044.2 55.2 37.1 812.8 12.0 .7 3962.0 Beryl 1 i urn .0008 .0008 Butadiene .2 8.3 73.1 .2 81.8 Cadmi urn 4.3 .2 .02 4.6 Carbon Tet. .0003 2.7 2.7 Chloroform .0003 31.1 .2 .7 32.0 Chromi un** .07 2.5 .6 3.2 Coke Oven Em. 388.0 388.0 Di oxin .0002 .000009 .0000007 .0002 Epichlorohydrin .09 • .00002 .09 Eth. Di bromide .8 .8 Eth. Dichloride 54.6 .2 .7 55.5 Eth. Oxide 61.5 11.2 72.7 Formaldehyde 14.6 12.6 110.0 353.5 .04 491.7 Gas. Vapors 216.2 4737.2 14376.0 19329.2 Hex-chl-benz. .07 .5 1.3 1.8 Methyl Chi . .3 10.9 .0003 .07 11.3 Methylene Chi. 287.3 1084.0 61.9 8.6 1441.7 Perchl oroeth. 383.7 802.0 .7 6.0 1192.3 PCB’s .0002 .001 .001 001 001 13 Table la. (Continued) Compound* Steel Mills Other Industrial Sources Consuner Sources Mobile Sources Waste Facilities Sewage Treatment Plants Total POM C\J o • 5.6 8.9 14.6 Prop. Oxide .9 Q • «/ Styrene 3.8 1.5 2.4 7.6 Trichloroeth. 374.7 27.8 1.9 404.4 Vinyl Chi. 2.3 • 4.0 6.3 Vinyl idene Chi. .4 .8 .01 1.2 ♦Abbreviations: Carbon Tet. - Carbon tetrachloride Chi. - Chloride Eth. - Ethylene PCB's - Polychlorinated biphenyls Gas. - Gasoline POM - Polycyclic organic matter Hex-chl-benz. - Hexachlorobenzene Prop. - Propylene ♦♦Estimates are for hexavalent (+6) form of chromium. Table lb. Other Substances in Study Substances without Unit Risk Factors found in Southeast Chicago Area Substances without Unit Substances with Unit Risk Factors not found Risk Factors not found Acetone Diethanol amine Dioctyl phthalate Ethyl Acrylate Ethylene Melamine Mercury Ni ckel Nitrobenzene Pentachlorophenol Titanium Dioxide Toluene Xylene Dimethylnitrosamine Allyl Chloride Isopropyl idene Diphenol Radionuclides Methylene Dianiline Nitrosomorpholine Propylene Diehloride Terepththalic Acid 14 Chicago study area. The significance of the emissions shown here is best interpreted in terms of risk assessment results, so this topic will be discussed in the section discussing risk estimates. As shown in Table lb this study found no emissions of allyl chloride or radio¬ nuclides. This reflects the fact that either this study found no methods for quantifying emissions of these pollutants, or no sources were identified in this area. The emissions inventory phase of this study also attempted to include 19 substances without unit risk factors; as described in the inventory reports. 13 of these 19 substances had quantifiable emissions in the study area. A variety of uncertainties apply to the emissions inventory used in this study. Emissions measurements were not conducted in the Southeast Chicago area, and so it was necessary to apply emission factors (i.e., emissions per unit operation) measured elsewhere in estimating emissions in the Southeast Chicago area. This extrapolation from sources elsewhere to sources in the Southeast Chicago area is probably the greatest cause of uncertainty in the emissions inventory. On the one hand, this extrapolation is probably fairly good for some source types, especially for area and mobile sources. For example, roadway vehicles in South¬ east Chicago are probably similar to the roadway vehicles in other places in the United States. On the other hand, for other source types, source-to-source differences in the raw materials used and differences in source operations may yield significant differences in emissions, not just in the quantity of emissions, but even in whether particular substances are emitted at all. Sources providing their own emissions estimates in response to questionnaires may have a better knowledge of the materials being emitted but may have less knowledge of methods for estimating emissions. A second major uncertainty is that some sources of some pollutants may be missing in this inventory either due to lack of awareness of the source or source type or due to unavailability of information with which to quantify emissions. This is likely to be.a particular problem for relatively unknown pollutants and for pollutants that are difficult to measure. Lesser uncertainties exist in various aspects of the emissions estimation process. Data used to estimate emissions in this study include source operating rates, emission factors for particulate and organic emissions, data on composition of these emissions, data on the extent of emissions producing activities (e.g., pounds of wood combusted), and data used for area sources to spatially distribute these emissions. For each of these types of data, the best reasonably available data were used, but even the best reasonably available data have uncertainties in their measurement and in their adequacy in representing emissions in the Southeast Chicago area. Estimation of Concentrations by Atmospheric Dispersion Modeling The principal method used in this study to estimate concentrations is to model the atmospheric dispersion of the emissions estimates described in the previous section. Atmospheric dispersion is a function of several factors. From the standpoint of selecting atmospheric dispersion models, two important factors are the averaging times of the concentrations and the nature of the emissions sources. With respect to averaging times, some dispersion models are designed to estimate short term average (e.g., 1 hour average) concentrations, and other models are designed to estimate long term average (e.g., annual average) concen¬ trations. The health effect being addressed in this study, cancer, is most 15 appropriately addressed by evaluating lifetime cumulative doses (Cf. the "USEPA Guidelines for Carcinogen Risk Assessment", 51FR33992). Therefore, dispersion models for estimating long term average concentrations were selected. With respect to the emissions sources, some dispersion models are designed to address point sources (i.e., stacks or other similarly localized emission points), and other dispersion models are designed to address area sources. This study includes both types of sources. Therefore this study used one model for point sources and a second model for area sources. The models used in this study were the Industrial Source Complex Long Term model (ISCLT) for point sources and version 2 of the Climatological Dispersion Model (CDM-2) for area sources. The two models reflect the obvious differences in initial dispersion (e.g., the broad dispersion of area source emissions even at the moment of emission). However, the degree of atmospheric dispersion assumed in the application of these two models was the same. One parameter in both models is the choice of dispersion coefficients. Separate sets of disper¬ sion coefficients are available for urban versus rural areas to represent the degree of atmospheric mixing under various meteorological conditions. In this study, for both models, Briggs’ urban dispersion coefficients were used. A second parameter in both models is the meteorological data used. As a simpli¬ fication in estimating long-term average concentrations, both models in this study use stability array (STAR) data showing the joint frequency distribution of winds in each of six classes of wind speed and six classes of atmospheric stability for each of 16 wind directions. Both models estimate concentrations for each wind speed/stability/wind direction category. These models then estimate an annual average concentration by averaging the category-specific concentrations, weighted according to the frequency of each meteorological category. For both models, the meteorological frequency distribution was based on 1973 to 1977 data collected at Midway Airport, representing the nearest, most recent, and most representative complete data set available. Further, both models assumed relatively flat terrain. Finally, it should be noted that both of these models are state-of-the-art models which are routinely used for regu¬ latory applications where estimates of atmospheric transport and dispersion are necessary. In fact, both of these models are reference models noted in USEPA's Guideline on Air Quality Models (Revised) , July 1986, (EPA-450/2-78-027R). Although this guideline does not address the pollutants in this study, the study uses the models recommended in the guideline for the general type of modeling being conducted here. The discussion of emissions estimation has noted that point sources in this study include steel mills, most other industrial sources, waste handling facilities, and wastewater treatment plants. That discussion also noted that area sources include a few industrial source types (chrome platers, degreasing, and barge loading), consuner-oriented sources, and roadway vehicles. This same distinction applies to selection of a dispersion model for addressing each source type. An important exception is that a selected set of steel mill operations were simulated with a small but finite initial dispersion, reflecting the modest area from which these emissions arise. These emissions were simulated using the area source algorithm of ISCLT. For example, a typical coke oven was simulated by distributing emissions into three neighboring 40 foot squares. This approach was intended to simulate more realistically the dispersion of these emissions, and was used for coke ovens and for roof monitors at steel- PROPERTY Of WMRC LIBRARY 16 making furnaces. A second exception is chrome platers. In Illinois, it appeared that a sufficient listing of electroplaters was available to treat these emis¬ sions as point sources, assigning the area's emissions to the identified plater locations. This treatment has the advantage of providing more realistic treat¬ ment of the dispersion characteristics of these sources. Note that in Indiana, where no listing of sources was available, this source category was both inven¬ toried and modeled as area sources. A third exception is municipal waste landfills, which were simulated as area sources using CDM-2 using landfill- specific dimensionso A sample of the concentrations estimated In this study is shown in Figure 2. This figure shows a map of coke oven pollutant concentrations. This map highlights the grid system used in estimating concentrations. The area was divided into 1 kilometer squares, and concentrations were estimated at the center of each square. The geographic coordinate system used In this study was the Universal Transverse Mercator (UTM) system. In UTM coordinates, the square with the highest coke oven pollutant concentrations extends from 4614.5 kilo¬ meters to 4615.5 kilometers north and from 452.5 kilometers to 453.5 kilometers east in zone 16. In Chicago streets, this square extends roughly from 117th Street to 112th Street and from almost a kilometer west of Torrence Avenue to a little east of Torrence Avenue. The concentration estimate used for this grid square was estimated at 4615 kilometers north/453 kilometers east, which is near 114th Street and Torrence Avenue. Although the receptor resolution (i.e., the estimation of concentrations at 1 kilometer Intervals) is adequate for the purposes of this screening study, it must be understood that a finer receptor resolution (i.e., estimation of concen¬ trations at more closely spaced intervals) would be expected to yield a higher peak concentration. This is because estimation of concentrations at more locations can be expected to identify some locations with somewhat higher concentrations. That is, the actual peak concentration for coke oven pollutants is probably somewhat higher than the 6.1 ug/m^ shown in figure 2. However, the design and scope of this study was not to obtain a precise peak concentration estimate but rather to address area-wide impacts from multiple pollutants and multiple sources. The estimate of area-wide exposure to specific pollutants would also be more precise if a finer receptor resolution were used. However, concentrations generally do not change dramatically more than a few kilometers from a given source, so the use of a finer receptor network would not be expected to alter the area-wide exposure estimates significantly. Figure 3 shows a map of concentrations of polycyclic organic matter. (This map and Figure 2 were both produced by PIPQUIC.) This figure shows concentrations generally increasing toward the center of Chicago, reflecting the increase in population density and, therefore, density of sources of polycyclic organic matter (particularly mobile sources and homes being heated) as one approaches the center of Chicago. Similar concentration estimates were made for the other pollutants in this study. However, the most meaningful way of addressing multiple pollutants is to use the common denominator of risk. This discussion will be included later in this report. Figure 2. Map of Estimated Coke Oven Pollutant Concentrations 17 Figure 3. Map of Concentrations of Polycyclic Organic Matter (in ug/m 3 ) 18 Sibley Blvd. 19 An unavoidable element of uncertainty is introduced in estimating atmospheric dispersion. In general, the data and equations used to estimate atmospheric dispersion are an approximation of real atmospheric phenomena. Specifically, in Southeast Chicago, the proximity of Lake Michigan may cause alterations in the frequency of some wind directions and wind speeds and may also affect the extent of dispersion in this area as compared to the meteorology at Midway Airport. Generally, atmospheric dispersion models are considered accurate within a factor of two. Although actual uncertainties for annual average concentration estimates are difficult to quantify, this generalization does give a sense of the uncertainties in the modeling element of this study. A measure of the uncertainty in the exposure assessment may be obtained by comparing this study's results to the results of a study by USEPA's Office of Mobile Sources (OMS) using a different method off assessing exposure. The OMS study used a modified version of the National Ambient Air Quality Standards Exposure Model (NEM). In this model, a matrix of relationships between carbon monoxide emissions factors for various situations and the resulting monitored carbon monoxide concentrations for various exposure environments are used in conjunction with other pollutant emission factors to estimate exposure to these other pollutants. An important aspect of the NEM is the consideration of the variable exposure encountered by a typical commuting individual traversing various types of locations (including indoor locations). This model also relies ultimately on monitoring data rather dispersion modeling to estimate exposure. If the OMS study results are "normalized" to the same emission rates as were used in the Southeast Chicago study, the following population-average exposures from mobile source emissions are estimated: for benzene, 1.22 ug/nw (OMS study) versus 0.57 ug/m^ (Southeast Chicago study), a ratio of 2.2:1; for butadiene, 0.13 ug/m^ (OMS) versus 0.05 ug/m^ (Southeast Chicago), a ratio of 2.6:1; and for formaldehyde, 0.63 ug/m^ (OMS) versus 0.25 ug/m^ (Southeast Chicago), a ratio of 2.5:1. (The OMS study also found a roughly 6 times higher average risk from polycyclic organic matter. However, this assessment was based on a "relative potency" approach of using mutagenicity tests and analogous bioassay tests to estimate adjusted carcinogenic potencies of particulate extracts from specific sources types, e.g. diesel particulate. Thus, these results are difficult to compare to the Southeast Chicago assessment using carcinogenic potencies based on whole animal studies for a surrogate polycyclic organic compound.) These comparisons do not demonstrate that the Southeast Chicago study results are a given factor too low or that the OMS study results are a given factor too high. This comparison also may or may not be indicative of differences that might apply for source types other than mobiles sources. Nevertheless, this comparison, involving exposure for a mobile individual versus exposure at fixed points and exposure estimates tied to monitoring data versus exposure estimates tied to dispersion modeling, suggests an uncertainty and potential underestimation of exposure by a factor of two or three. Comparison of Modeling and Monitoring Concentration Estimates This study uses monitoring data in two ways. The first use is to compare with dispersion model estimates, to provide an indication of the reliability of the 20 model estimates. The second use, applicable to formaldehyde and carbon tetra¬ chloride, is for quantifying concentrations of "background pollutants" which are not the direct result of current emissions. Various monitoring programs have been conducted in the Southeast Chicago area to measure concentrations of pollutants in this study. Table 2 summarizes the studies from which data were available. This table shows the organization conducting the monitoring, the location(s) of the monitoring site(s), the monitoring method, the sampling period, the number of samples, the sampling duration (frequency and averaging time) and the pollutants monitored. Table 3 presents a comparison of modeled versus monitored concentration estimates for the organic substances for which monitoring data are available. For each comparison, the monitoring data represent the average over the full time period for which reliable data are available. The modeling data in effect are 5 year averages (since the underlying meteorological data are 5 year averages and the underlying emissions data are intended to be similarly long-term averages). The modeling results are also specifically Interpolated to the location of the monitor from the concentrations estimated at the nearest modeling grid points. Although in a few cases such interpolated results may differ significantly from the results that would be obtained by direct modeling for concentrations at the monitor location, particularly near major sources where spatial gradients may be high, in most cases these differences should be small. The best comparison on Table 3 is for benzene. For this pollutant, the monitored values are within a factor of two to three higher than the modeled concentrations. Given the relative sparsity of monitoring data (in no study were more than about 30 days sampled), the uncertainty of the monitoring methods at concentrations close to the detection limit (generally not more than around three times the detection limit), and the uncertainties in the emissions Inventory and modeling analysis, these results should be considered quite comparable. Note that although the modeled estimates could be adjusted to include the benzene component of coke oven emissions, this would only be a few percent increase. Less encouraging are the comparisons for toluene and xylene, where monitored values are between one and two orders of magnitude greater than modeled estimates. The same may be true of chloroform, whereas the comparison for perchloroethylene and trichloroethyl ene appear to be as close as the comparison for benzene. However, the concentrations that the Illinois Institute of Technology (I IT) and the Hazardous Waste Research Information Center (HWRIC) identify for perchloroethylene, trichloroethylene, chloroform, and carbon tetrachloride are below the detection limits as identified for Illinois EPA/Radian's monitoring, so these comparisons may not be reliable. It has been noted previously that a substantial portion of formaldehyde and carbon tetrachloride concentrations may be attributed to origins other than current emissions. In this study, the emissions estimation/dispersion modeling approach Is considered the best means of addressing the Impacts of current emissions. For formaldehyde and carbon tetrachloride, monitoring data provide the best indication of the sum of direct impacts from current emissions plus indirect impacts from other causes. Thus, in this study, background concen¬ trations for these two pollutants were evaluated by determining a total con¬ centration from available monitoring data and then subtracting the concentration attributable to current emissions. These background concentrations were assumed to be uniform throughout the Southeast Chicago area. Total concentrations of Table 2. Monitoring Studies Conducted in Southeast Chicago 21 0) Cl -—. *o *-4 ITS >1 ITS •v_4 to Z 4—4 CO *o 4-s U) QJ CO 1/1 i/i OS c U *3 * U u * l- 3 ITS E ITS ITS ITS = ITS •>— (— O) L 31 CO 4-S c a— 4 - o os £ 4- ai ££ Oil E O E • 1/1 • lO • • >1 Ol c (/) >» 1/1 >1 >» to IO 1- >* OS o 4. >> ITS 4- 4- ITS 4- 4. as its r— - r 1.-0 z as "a Z £ > T3 Q. 4-> O) > as E cm ^r a; cvj «3- c\j <0 4- 00 3 M 0) i- CM Cl) *” to o to to to r— C IQ ITS 10 ITS 4-> 4-* 4-> CO as as as as & E E E • • • • c to to to to •f 4. 4- >1 u E XI XI ITS T3 *>■ O CM o in. CNJ 4 - O) -O to OS n E IO tn vo c-~- ITS o as >i o 4-1 S 4 ^ CD o id CO to • f'-- >— to o o n- r>- N- VO CO CO 00 CO CO 00 '■N 31 4^ 4^ 4^ co o CM 4— VO to CO 3> *^s. 4-S LO r»- r»- C *3 CO 00 o O o CO o co o •- O ID ^ 4-S s 4-> 4-* 31 4-» 31 4-S r— o CO •— 1 a— r~ a. t_ 4-S S S *4- *-4 (D ID to VO VO E as CO CO 00 00 00 ITS O. f-4 4*^ 31 4-. -4^ id co r— a— O to CM 4^ ^a- 31 to as 4. *— as Q. r — as E a. c 31 4- ns E ITS c OS O (O ITS Z •a— ^3 4- 31 «4- 4- 4- 4- to 4- ■u» E (- O OS "3 4- as to as o • as as its O -C 4-» •*- U its as 4-> X 4- 4-S 4-> 4-S ^ 4. o 4-S 4-> to u as X 4-> 4-> to ITS as tO U O US 3 U_ •P“ 4-> 4-> IQ IQ VI •f- c 4-> •i- its x: as >1 c sz C 4- r- C "3 •*- e as r—■ C Q. U 4- £ ITS ITS •*“ (-SOU- as c H- o ITS ITS t— o U_ IT? E *1" 4-S 0-5010 5 C <_S o o c c c JZ LU 4-4 o r- aa—- o o o 4—^ u 4~- 4—4 CM UJ LU LU O LU LU *r- LU o LU ID LU LU LU • CNJ O Cl 4-> o 31 4-» O 4-1 o jC CO O CO CM CD • ^ • • ITS x • ic • ITS % u • x: 9 • 4 r— ID CD Ol id 4— CD a o O 4-> o 4-> o ID 3i 31 VD CD CD o —— ID ID o ID o 1/1 ID ID VD ID ID •4— ID r— LO ^4 ID o 4^ o •a* oc o _i TT T}- • nr TT o TT O «T -C z o s 4-. z Z 4^ OS OS '*s* E •^s. O ■v^ O *4^ o ID -3 z c z £ z 31 31 Z u 2C U y 0) z c Z -C z z ID • £ LD O o CD C •a— r*— ID D a— r — o o u CM u ID VO • 4-> • • . 4 - —L. • f— • p— • LU • 4-S • ID • ID • 4-> r— VO 31 ID E CM 4- £ ID £ D r— 31 vD VO VO JZ VO 4U f— c o p— O 4- —i r— 4- r— C »— to 4-s *-H 31 JZ VO VO tn VO 4-> OS VO vO VO os VO •«— VO E VO JZ VO *r— 4_4 31 D- Z •r- > TT • • 4J- > TT JC IO 31 4- — to •f* 4—4 C 4- —' LU LU 4—4 4- 4—4 to —' 3 4—4 — .. CQ 4- IO L. O ITS • • flO IO ■3 4- 00 3 o t_3 ID ID v_s 3 «C CQ to 3 4- C E 4-> <4- 3 4- IO c ■3 4- to 3 L-4 O 4-> » 4-S - - 1 OS 4-> IO c CL-O 4^, OS o o. o Q. •r— c c O IO £ c to to •f— UJ • 4 — LU X •f— ►—4 i— IO OS OS 4J 4-> o 4- o 3 u oz IO o ITS to t— o to c f— O 4- l/> >1 4- E to Nl •»— 4-> tO e~ IO • 4— O •f— 31 3 os L cr: •4— o • 4 — 3E o O c 4-> X o 4- o 4-> o o c c sc C < c OS o 4- 4-> C OS to to 14 - 31 c IO O. £ H- •p— (— Cl. z LU cc 3 — 4 — 4. f— VD r— Vt- IO r— r— O 1—4 3 1 “ 1 o z ——t Table 3. Comparision of Modeled-versus Monitored-based Concentration 22 *-> c (O a; u c o u ■o 0 ) QJ fa m in CNJ o o CD CNJ o 00 o o CNJ Cl CNJ VO o ro OJ t- o in r-* o CNJ © a. 2E o o qj c QJ QJ CO .QJ O -O l_ <0 O QJ *2, o «4- o l_ o QJ QJ E "C QJ f— CO >i E «_> QJ C QJ -C 4-> QJ QJ QJ O SL 0J c QJ QJ *>> QJ O t- O u u QJ u >1 4-> i i CNJ Cl Ch CO • • • • • • • • 00 CNJ CNJ Cl E E — QJ *♦- o o •— 4-> 00 U »— QJ f- 4-> CO QJ 4-> "O QJ "O *C QJ ■a •*- C <4- <0 QJ QJ 0J QJ ■C • 4 -> QJ »- * CO o oo a* ** C 00 O QJ L. t- U *4- 03 •<- C QJ Cl 00 -f 0J oo t— V * * * 23 these two pollutants at each of the receptor locations were then derived by adding the uniform concentration representing background impacts plus the vari¬ able concentrations representing direct emissions impacts. As seen in Tables 2 and 3, formaldehyde was monitored at one location in the area. Data are available for a year starting in September 1987. The average of available data is a concentration of 2.50 ug/m^. At the monitor location, the impact of direct emissions is estimated to be 0.27 ug/m^. Therefore, the formaldehyde concentration attributed to photochemical formation is the difference of 2.23 ug/m^. Tables 2 and 3 show that carbon tetrachloride was monitored at three locations In the Southeast Chicago area. However, Table 2 also shows that two of the three studies (by I IT and HWRIC) include only a small nunber of samples, and Table 3 shows that the third study (by Illinois EPA/Radian) did not report any detectable concentrations. Atmospheric accimulation of carbon tetrachloride over prior decades may be presumed fairly uniformly distributed in the global atmosphere, and so a more reliable indicator of the atmospheric accimulation of carbon tetrachloride is from more thorough studies elsewhere. In areas of the United States that may be presuned not to have significant sources of carbon tetrachloride, available monitoring data suggest concentrations generally between 0.6 and 0.8 ug/m^. An average value of 0.76 ug/m^ is used as the average value in the Southeast Chicago area. Most of this concentration is assumed uniform throughout the area; only the minor portion of the concentration attributable to current emissions is treated as varying from location to location. Table 4 compares PCB concentrations monitored by IEPA with modeled concen¬ trations. Possible explanations for this relatively poor comparison include missing emission sources, uncertainties in the monitoring method, a short and therefore possibly unrepresentative monitoring period, and the long atmospheric res-idence of PCBs. Table 5 compares particulate matter monitoring data with modeled concentrations. For arsenic, cadmium, and chromium, the two sets of concentrations are quite similar, indeed well within the uncertainty ranges for the monitoring and modeling data. (Note that for chromium, both the monitoring and modeling data show total chromium concentrations.) The other pollutant shown in Table 5, benzo(a)pyrene, again seems to show a close comparison between monitored and modeled concentrations. This comparison is complicated by the fact that the monitoring method measures specifically benzo(a)pyrene, a compound which in the inventory is included in the class of compounds labeled polycyclic organic matter (POM) as well as in coke oven emissions. The designated modeled value in Table 5 was estimated as a somewhat arbitrary IX of the combined mass of POM plus coke oven pollutants. Given the uncertainty in this comparison, no firm conclusions can be drawn from the similarity of these monitored and modeled data. These comparisons of modeled versus monitored concentration estimates appear to support two generalizations: (1) for many pollutants, the modeled and monitored concentration estimates agree reasonably well, and (2) where substantial differences exist, the modeled values are much lower than the monitored values. The first generalization suggests that for most pollutants, this study provides 24 Table 4. Comparison of Modeled- Versus Monltored-based Concentration Estimates for PCBs (all concentrations in ug/m^) Monitored Modeled Bright School .0019 .000003 Washington School .0003 .000002 Grissom School .0005 .000004 Table 5. Comparison of Modeled- Versus Monltored-based Concentration 25 Monitored (HWRIC) .001 .002 .013 Modeled .0027 .0031 .021 .0072 Monitored (NPN) .00214 .00055 .0064 .0076 E c o o a> a> ■a X o aj .a CO •T— CO >— u § 5 « OJ — c LkJ c •r* E o GJ y QJ E o Nl u UJ co •o u c »> —i u