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Assessment of Human Exposure to Air Pollution: Methods, Measurements, en c! Moclels KEN SEXTON Health Effects Institute P. BARRY RYAN Harvard School of Public Health Human Exposure: Introduction / 208 Definitions / 208 Concentration, Exposure, and Dose / 208 Components of Exposure / 208 Types of Exposure Information / 209 Individual Exposure Versus Population Exposure / 209 Methods / 211 Air Monitoring / 211 Biological Monitoring / 217 Research Recommendations / 218 Measurements / 219 Air Monitoring / 220 Research Recommendation / 223 Biological Monitoring / 225 Research Recommendation / 225 Modeling Human Exposure to Air Pollution / 226 Statistical Modeling / 226 Physical Modeling / 228 Physical-Stochastic Modeling / 229 Source Apportionment / 230 Validation and Generalization / 230 Research Recommendation / 231 Summary and Conclusions / 231 Summary of Research Recommendations / 232 Air Pollution, the Automobile' and Public Health. @) 1988 by the Health Ejects Institute. National Academy Press, Washington, D.C. 207

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208 Human Exposure to Air Pollution Human Exposure: Introduction Accurate estimates of human exposure to inhaled air pollutants are necessary for a realistic appraisal of the risks these pollut- ants pose and for the design and implemen- tation of strategies to control and limit those risks. Except in occupational settings, such estimates are usually based on mea- surements of pollutant concentrations in outside (ambient) air, recorded with out- door fixed-site monitors. Indeed, compliance with existing Na- tional Ambient Air Quality Standards (NAAQS), intended to protect public health with an adequate margin of safety, depends exclusively on outdoor measure- ments of pollutants. But, such measure- ments are subject to biases because most people spend much more of their time indoors than out, and air pollutant concen- trations are often much higher inside build- ings than outside (National Research Council 1981; Spengler and Sexton 1983~. In addition, available evidence indicates that personal exposure to many pollutants is not adequately characterized because the time people spend in different locations and their activities vary dramatically with age, gender, occupation, and socioeconomic status (National Research Council 1981; World Health Organization 1982, 1983; Yocum 1982; Spengler and Sexton 1983; Spengler and Soczek 1985~. In this chapter, the state of the art of air pollution exposure assessment is discussed with emphasis on gaps in our knowledge and the implications of those gaps for fu- ture research. First, important terms are defined, and then the methods available for monitoring exposure, the results of expo- sure assessment studies, and the models for exposure estimation are examined. Definitions Concentration, Exposure, and Dose The concentration of a specific air pollutant is the amount of material per unit volume of air. Concentrations are most commonly expressed as mass per unit volume (for example, micrograms per cubic meter). Concentrations of pollutant gases may be reported as volume per unit volume (for example, parts per million by volume) and discrete particles as number per unit vol- ume (for example, number of fibers per cubic centimeter). Exposure refers to any contact between an airborne contaminant and a surface of the human body, either outer (for example, the skin) or inner (for example, respiratory tract epithelium). Thus, exposure requires the simultaneous occurrence of two events: a pollutant concentration at a particular place and time, and the presence of a person at that place and time (Duan 1982; Ott 1985~. Exposure is expressed quantitatively by a description of the duration of the contact and the relevant pollutant concen . tratlon. There is an important distinction be- tween concentration and exposure. Con- centration is a physical characteristic of the environment at a certain place and time, whereas strictly speaking, exposure de- scribes an interaction between the environ- ment and a living subject. Thus, a concen- tration in a room with people present is a surrogate measurement of exposure, but is valid only to the degree that it approxi- mates the concentrations actually experi- enced by each individual in the room. The distinction between exposure and dose is also important. As stated above, exposure is the pollutant concentration in the air at the point of contact between the body and the external environment. Dose is the amount of the pollutant that actually crosses one of the body's boundaries and reaches the target tissue. The difference between exposure and dose is illustrated by considering two peo- ple, one sedentary and one vigorously ac- tive, in a room where the air pollutant concentration is constant. Both have the same nominal exposure. But because ot faster and deeper breathing, the actual dose of air pollution delivered to lung tissues is greater in the active subject than in the sedentary subject. Components of Exposure Three aspects of exposure are important for determining related health conse- quences.

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Sexton and Ryan 209 1. Magnitude: What is the pollutant con- centration? 2. Duration: How long does the expo- sure last? 3. Frequency: How often do exposures occur? Magnitude is an important exposure pa- rameter because concentration typically is assumed to be directly proportional to dose and ultimately to the health outcome. But exposure implies a time component, and it is essential to specify the duration of an exposure. The health risks of exposure to a specific concentration for 5 minutes are likely to be different, all other factors being equal, than exposure to the same concen- tration for an hour. Similarly, the fre- quency of exposure or the time between subsequent exposures might have health im- plications. Whether a person is exposed once a week or several times a day can be an im- portant determinant of air pollution injury. A real-time air pollutant monitor carried by a person for 24 hr would provide a continuous exposure record for that period. Depending on the pollutant and the per- son's activities during that period, the record might show some intervals of zero exposure and some intervals of very high exposure. The full record would contain all exposure information for that day, but it is often too complex to work with, as well as too difficult and expensive to obtain. It is common to rely on data summaries (averages) that depend on the capabilities of the available instruments. In most exposure studies, magnitude is defined as the average concentration over some specified time in- terval (for example, 1, 8, or 24 fur). Dura- tion is the time (or average time) from the beginning to the end of a nonzero expo- sure, and frequency is the number of expo- sure episodes (of a specified duration) per . . unit ot time. Types of Exposure Information Data on human exposure can be presented in several ways (Ott 1982, 1983-84, 1985~. For an individual, i, a plot of exposure magnitude as a function of time, Civet), typically covering a 24-hr period, is called an exposure profile. As shown in figure la, additional data about the subject's activities can be combined with the exposure profile to show when, where, and how the high- est-magnitude exposures occurred. Integrating the function CittJ with re- spect to time t for a specified time period yields the integrated exposure. The integra- tion is represented graphically in figure lb and shows a Bohr integrated exposure of 960 parts per billion-hour (ppb-hr). The integrated exposure does not provide infor- mation about the pattern of exposure over subintervals of the averaging time, nor does it reflect the magnitude of short-term peaks in exposure. Figure lc shows several examples of average exposure, ta' arrived at by dividing the integrated exposure by the period of integration. The figure gives eight 3-fur averages, three 8-fur averages, and a single Bohr average derived from the same Bohr period of data. In spite of the importance of these dis- tinctions, it is common to refer to the average exposure that is, the average concentration during a specific measure- ment period (for example, 24 hr) as the exposure. In some instances, it is also com- mon to refer to the average concentration measured by a fixed-site monitor as the exposure, even though no individual was actually in the vicinity of the instrument for the duration of the measurement period. The blurring of these distinctions, like those between weight and mass or between heat and temperature, causes little confusion for those well versed in the liter- ature. For others, however, it is important to keep in mind that a measurement of air pollutant concentration is a surrogate for exposure only to the degree that it reflects actual concentrations experienced by people. Individual Exposure Versus Population Exposure The pollutant concentrations experienced by an individual during normal daily activ- ities are referred to as personal or individual exposures. A personal exposure depends on the air pollutant concentrations that are present in the locations the person moves through as well as on the time spent in each location. Individual exposures for a speci

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210 Human Exposure to Air Pollution A 150 . 140 30 _ . _ 120 Q 110 O 1 00 ~ 90 z 80 c, 70 0 60 <) 50 o z 40 30 20 10 960 Q 880 Q 800 C' 720 O 640 x 560 O 480 c~ 400 ~ 320 `3 240 '_ 1 60 - 80 140 130 120 Q 1 10 - z 100 cc 80 .~ 70 z 60 8 50 o 30 20 10 0000 0300 0600 E o c' c ._ _, 0 ~ ~ ' ._ E ce c~ a - 6 1 CO ~ ,_ E 0 ._ c~ {~ ~ Q O ~ Q c E ~3, _ 1 1 ~ ~ c .O .' ~ ~._ L E , - c CO ~.O ~:= _o ~C ~._ C~C Co ~o JE {D r 1 1 o a: C~ .O ~ O Ct J .= o Cd ._ 1 Q E ce I I I 1 1 1 B 24hour integrated exposure Plot of cumulative exposure - t~ = 24hour average t`, = 8-hour average t~ = ~hour average - - I I I I I 1 0900 1200 1500 1800 2100 2400 TIME (hour of the day) Figure 1. Examples of NO2 exposure information: A: a 24-hr exposure profile and associated time-activity pattern data for a specific individual; B: a plot of cumulative exposure and the calculated 24-hr integrated exposure; C: calculated exposures averaged over 3 hr. 8 hr. and 24 hr. . . . . . . . . .

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Sexton and Ryan 211 fled group of people may vary widely because of their different time-activity pat- terns (Dockery and Spengler 1981; Quack- enboss et al. 1982; Ott 1983-84; Sexton et al. 1983; Letz et al. 1984; Spengler et al. 1985; Stock et al. 1985; Wallace et al. 1985a). Measuring any one person's exposure is a relatively straightforward procedure, but from a public health perspective it is im- portant to determine the population expo- surc the aggregate exposure for a speci- fied group of people, such as a community or an occupational cohort. It is rarely nec- essary or desirable to measure the exposure of each member of the group. But some measure of the distribution of individual exposures is needed. This typically includes at least a measure of the central tendency (for example, mean exposure) and of its variability (for example, variance). An ac- curate and statistically valid characteriza- tion of even these simple descriptors of population exposure may require many personal exposure measurements. The upper tail of the distribution is fre- quently of special interest, because it repre- sents the segment of the population that has much higher-than-average personal expo- sures. Determination of the numbers and kinds of people who experience exception- ally high exposures can be critical for health risk assessment. This is especially true when the relationship between the pollut- ant dose and resultant health effects is highly nonlinear. Typically, more personal exposure measurements are needed to ac- curately estimate the tails of the distribu- tion than are needed to estimate its mean and variance. Methods Basically, there are two general approaches to air pollution exposure assessment: (1) air monitoring, which depends on either direct measurements (personal monitors) or indi- rect measurements (fixed-site monitors combined with data on time-activity pat- terns), and (2) biological measurements that use biological markers to assess expo sure. In the past, questionnaires have also been used to estimate exposures, particu- larly in epidemiologic studies. Typically, questionnaires are used to categorize re- spondents into two or more groups (for example, exposed or unexposed, high ex- posure or low exposure). This is a qualita- tive, often retrospective, method for esti- mating air pollution exposure. It depends on a priori knowledge of exposures and their determinants to develop effective questionnaires (for example, high formal- dehyde exposure for workers in certain industries, or high carbon monoxide [CO] and lead [Pb] exposure for traffic police- men, bus drivers, and toll collectors). Most often the information necessary to develop . . .. . ~ . qUeStlOnnalreS IS O ~talnec . trom previous studies that used either air monitoring or biological monitoring to measure expo- sures. The questionnaire method is really a way to extend the results of prior air mon- itoring or biological measurements to a larger or different population and is not a separate approach. Air Monitoring Direct Approach to Exposure Assessment. A personal monitor is a small, lightweight device, such as a diffusion tube or a filter with a battery-operated pump, that can be carried or worn by a person during his or her normal daily activities. Personal monitors make it possible to measure ex- posures for an identified subset of the general population. Moreover, if study participants maintain records of their activ- ities, then locations where highest concen- trations occur as well as the nature of emission sources can often be inferred. The major impediment to this type of assess- ment has been the lack of suitable instru ments. Small, quiet, portable personal exposure monitors that are sensitive enough to mea- sure ambient concentrations of some pol- lutants are now available (Lautenberger et al. 1981; Rose and Perkins 1982; Wallace and Ott 1982; Bartley et al. 1983; Underhill 1984~. Pollutants that can be measured ac- curately with personal monitors include nitrogen dioxide (NO2) (Palmes et al. 1976;

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212 Human Exposure to Air Pollution Palmes and Tomczyk 1979; Woebkenberg 1982; Yanagisawa and Nishimura 1982), respirable particles (Turner et al. 1979), formaldehyde (Geisling et al. 1982; Kring et al. 1984), sulfur dioxide (SO2) (Coleman 1983; Kring et al. 1983), organic vapors (Feigley and Chastain 1982; Seifert and Abraham 1983; Vo-Dinh and Miller 1983; Compton et al. 1984; Reggin and Peterson 1985; Sheldon et al. 1985), and CO (Akland et al. 1985; Ott et al. 1986~. Personal monitors can be grouped into two general categories: integrated samplers that collect the pollutant over a specified time period and then are returned to the laboratory for analysis, and continuous samplers that use a self-contained analytical system to measure and record the pollutant concentration on the spot. Instruments in both categories can be either active or pas- sive. Active monitors use a pump and a power source to move air past a collector or sensor. Passive monitors depend on diffu- sion to bring the pollutants into contact with the collector or sensor. Information about personal monitors is summarized in table 1 (Wallace and Ott 1982~. Most personal monitors available today are integrated samplers with sampling pe- riods ranging from 8 hr to a week or more. Active integrated sampling devices are commonly used to obtain integrated expo- sure measurements over an 8- to 24-hr period. In general, they are bulky, noisy, and require frequent calibration to ensure the validity of the data they collect. Passive samplers are simple, small, quiet, inexpensive, and easy to use; but at ambient concentrations normally require a longer sampling period (for example, 1 or 2 weeks) to collect enough material for anal- ysis. A passive sampler, therefore, cannot be used to relate short-term exposures (minutes or hours) to specific events or sources. Moreover, passive samplers are affected by temperature, relative humidity, and air movement and tend to be less accurate than active monitors. They are most appropriate for large-scale surveys of population exposure, where pinpoint accu- racy is not required and long-term expo r ~ . sures are ot primary interest. Although considerable progress has been made in miniaturizing real-time analytical monitors, much work remains to be done. Continuous personal monitors have not been developed for most of the important pollutants (see table 1~. Those that are available can provide data on measured concentrations as a function of time throughout the day. These data can be used to construct exposure profiles (see figure 1) and, when combined with time-activity information, can be used to relate short- term exposures to specific events and sources. Because they record a large num- ber of real-time measurements, continuous personal monitors should log and store data to be most effective. Participants are typically asked to main- tain a detailed record of their time-activity patterns during the test period. The record is usually a log or diary documenting the subject's location and activity at particular times. Recently, a small microprocessor- based data logger was developed that auto- matically computes and stores times and average concentrations (Ott et al. 1986~. The subject only records the type of activ- ity engaged in and presses a button, and the instrument stores all other information electronically to be retrieved and analyzed later. As Wallace and Ott (1982) pointed out, the direct measurement of exposures using personal monitors raises several method- ological issues. Personal monitoring studies . . . are comp. .ex, expensive, t~me-consum~ng, and labor-intensive. They present prob- lems because they generally require the selection and recruitment of representative subjects; the distribution, maintenance, and retrieval of many monitors; either a labo- ratory analysis of many air samples re- turned from monitors in the field or cali- bration and validation of many real-time monitors; and the transcription and statis- tical analysis of data on pollutant concen- trations and time-activity patterns. The problems raised by the three latter points are fairly obvious, but the difficulties asso- ciated with selecting and recruiting ~ test sample require amplification. . ~ , ~ . Personal exposure monitoring is, by its very nature, an intrusive event in the life of the study participants. The degree of incon

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Sexton and Ryan 213 Table 1. Personal Exposure Monitors Capable of Quantitative Pollutant Measurements at Ambient Concentrations Monitor Type Pollutants Collection Method Analytical Method Integrated, Respirable particles (sulfates, Pump/stack filter (2 size Microbalance chemical Active nitrates, metals) fractions) analysis PIXE Respirable particles (mass only) Pump-impactor/precipita- Piezoelectric tor Respirable particles (sulfates, Pump/filter Microbalance PIXE nitrates, metals) Sulfur dioxide, nitrogen Pump/impingers/filter Colorimetric gravimetric dioxide, respirable particles Nonpolar volatile organics Pump/Tenax cartridge Thermal desorption/ GC-MS Organochlorine pesticides, Pump/polyurethane foam GC polychlorinated biphenyls Integrated, Carbon monoxide Diffusion Electrochemical Passive Nitrogen dioxide Diffusion tube (TEA) Colorimetric adsorbent Nitrogen dioxide Badge/TEA Colorimetric Nitrogen dioxide Di~usion/dimethylsilicone Colorimetric filter/TEA Nitrogen dioxide Di~usion/TEA-impreg- Colorimetric nated filter Formaldehyde Permeable membrane MBTH, pararosaniline Formaldehyde Diffusion badge Chromotropic acid Polynuclear aromatics Diffusion badge Room-temperature phosphorescence Vinyl chloride Permeable membrane Solvent desorption/GC badge/activated charcoal Radon Plastic (records radiation Etching/microscopic damage) examination Continuous, Carbon monoxide Pump electrolyte Sulfuric acid Active Carbon monoxide Pump electrolyte Solid polymer NOTE: GC = gas chromatography; GC-MS = gas chromatography-mass spectrometry; MBTH = 3-methyl-2- benzothiozolinone; PIXE = proton-induced x-ray emission; TEA = triethanolamine. SOURCE: Adapted with permission from Wallace and Ott 1982, and from the Air Pollution Control Association. venience depends on the size, weight, ap- pearance, and ease of operation of the mon- itor, as well as other aspects of the study, such as the need to fill out logs or diaries. The demands of the project protocol and the associated inconvenience may cause many people to refuse to cooperate. It is particularly difficult to get the cooperation of schoolchildren, non-English-speaking people, disadvantaged people, or those with low socioeconomic status. The re- sponse rate may be raised by offering in- centives, but, even so, additional incentives for those that complete the study may be necessary to forestall high dropout rates. In . . . any case, simply wearing a monitor or filling out a log can cause the participant to change his or her behavior and conse- quently introduce bias (Sexton et al. 1986a; Ryan et al. 1987~. Direct personal monitoring is the most accurate means of exposure assessment, but it is also the most expensive. Large-scale personal monitoring studies are a recent development, so many survey design, lo- gistic, and technical problems remain to be solved. More attention should be fo- cused on these issues to make subsequent

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214 Human Exposure to Air Pollution personal exposure studies more cost-effec- tive. Indirect Approach to Exposure Assess- ment. The indirect approach estimates in- tegrated exposure by combining measure- ments of pollutant concentrations at fixed sites (for example, outdoors at a busy in- tersection, indoors at home) with data logs and diaries about the times people spend in specific environments (Fugas et al. 1972; Fugas 1975; Dockery and Spengler 1981; Duan 1981, 1982; Ott 1982; Sexton et al. 1983, 1984b). The general form of the equation used to calculate time-weighted . . 1ntegratec exposure IS J Ei= ~ Cjtij j where Ei is the time-weighted integrated exposure for person i over the specified time period; Cj is the pollutant concentra . . . . . . lion in microenvironments tic IS t he aggre- gate time that person i spends in microen- vironment j; and r is the total number of . . microenvironments that person i moves through during the specified time period. A microenvironment is defined as a three-dimensional space where the pollut- ant level at some specified time is uniform or has constant statistical properties. Out- doors in a specific community, inside an individual motor vehicle, and inside a par- ticular residence are examples of locations that can be defined, under appropriate con- ditions, as microenvironments. Examples of potentially important microenviron- ments for exposure assessment are given in table 2. Several assumptions are implicit in the application of equation 1: 1. The concentration Cj in microenvi- ronmentj is assumed to be constant during the time tic that person i is there. This is not always the case. For example, it is likely that air pollution levels inside one's resi- dence will vary substantially during the 14 to 16 hr/day that most people spend at home, because of variations in emission rates and air exchange rates. 2. The concentration Cj within microen- vironment j and the time ti that person i spends there are assumed to be independent events. This assumption is not universally valid, however. Persons sensitive to pollut- ants like tobacco smoke and formaldehyde, or to noxious odors, such as those from paint and cleansing solutions, are likely to avoid microenvironments where concen- trations of these pollutants are elevated. 3. The number of microenvironments necessary to characterize personal exposure adequately is assumed to be small, but in fact, it is not clear how many are necessary. Within the indoor residential environment, for example, the variability in short-term particle concentrations from activities such as cooking, smoking, and cleaning might (1) necessitate the inclusion of several addi tional microenvironments in the model to comply with assumption 1 above. 4. The time-weighted integrated expo- sure, usually measured over 24 hr. is di- rectly related to the health outcome. This may not be the case for adverse health effects due to short-term peak exposures (hours, minutes, or in some cases seconds) to pollutants such as formaldehyde, NO2, or ozone (03~. The concept of a time-weighted inte- grated exposure is illustrated in figure 2. A unit width is indicated on the j axis for each of five microenvironments: indoors at home, indoors at work, indoors in other locations, in transit, and outdoors. The concentration of respirable particles (RSP) is displayed on the Y axis, and the fraction of time that person i spends in each micro- environment over the 2=hr period is plot- ted on the t axis. The volumes of the boxes shown in figure 2 represent contributions from each of the five microenvironments to the time-weighted integrated exposure. The contribution of each microenviron- ment is represented mathematically in the table at the bottom of figure 2. Even though respirable particle concen- tration was low inside the home, it contrib- uted significantly to the time-weighted ex- posure because this person spent 18 out of 24 hr there. Conversely, the respirable par- ticle concentration outdoors made only a

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Sexton and Ryan 215 Table 2. Potentially Important Microenvironments for Air Pollution Exposure Assessment Mi cro en viron men ts Comments Outdoors Urban Metropolitan areas where air pollution levels are high as a result of a high density of mobile and stationary sources. Suburban Small- to medium-sized cities where air pollution levels tend to be lower than metropolitan areas, although transport of urban pollution can affect local air quality under certain conditions. Rural Agricultural communities and small towns with few major anthropogenic sources of air pollution. Air pollution levels tend to be low, although trans port of urban and suburban pollution can affect local air quality under certain , . . conaltlons. Indoors Occupational Industrial Manufacturizing and production processes, such as those in petrochemical plants, pulp mills, power plants, and smelters. Nonindustrial Primarily service industries where workers are not involved in manufacturing and production processes, such as insurance companies, law offices, and retail sales outlets. Indoors Nonoccupational Residential Single-family houses, apartments, mobile homes, condominiums Commercial Restaurants, retail stores, banks, supermarkets Public Post offices, courthouses, sports arenas, convention halls Institutional Schools, hospitals, convalescent homes Indoors Transportation Private Automobiles, private airplanes Public Buses, subways, trains, commercial airplanes minor contribution because this person was outdoors less than half an hour during the 24-hr period. This illustrates the general problems as- sociated with attempts to define the limits of microenvironments that are sufficiently homogeneous, to identify which among them are the significant contributors to integrated exposure, and to measure or estimate both the pollutant concentration Cj and the average time, tic, the subject spends In the microenvironment. Better documentation of time-activity patterns, as well as more information about approximate indoor and outdoor pollutant concentrations would help investigators specify important microenvironments and choose fixed monitoring sites. In most cases, however, there is not enough infor . . . . - and spatial aspects of people's activity pat- terns are reflected separately in the time budgets and mobility patterns that sociol- ogists, urban planners, economists, and transportation analysts use. these data are A ~ , . . . . -- r ~ --- ~ ~ - - - 7 not in a form suitable for application to exposure assessment. Only in the past few years have both temporal and spatial as- pects of people's everyday movements . . . . . . . . seen ~nvestlgatec . in conjunction wit ~ air pollution measurements (Spengler et al. 1980, 1985; Dockery and Spengler 1981; Dockery et al. 1981; Moschandreas 1981; Ott and Flachsbart 1982; Sega and Fugas 1982; Sexton et al. 1983, 1984b; Flachsbart and Brown 1985; Nagda and Koonz 1985; Wallace et al. 1985a,b). , . . . . . - - r matron to determine wnlcn m1croenvlron- ments are adequately defined, which can be bypassed or lumped with others, which should be subdivided, and which should have their limits altered to ensure accurate exposure estimates. Although the temporal much ot what IS known about human time-activity patterns can be traced to two studies now more than a decade old (Szalai 1972; Chapin 1974~. A summary of mea- sured 24-hr time-activity patterns from these studies is provided in table 3. Both studies found that on most days people are inside their residences for an average of 65

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216 t Human Exposure to Air Pollution RSP CONCENTRATION (regime) 130 120 110 100 90 80 70 60 50 40 30 20. / 10 rppcT1/~ ~ ~/~ 0.9~ 0, ~O, ~ ~ C~ i RSP Time Microenvironment Microenvironment Concentration Fractiona Cj x tij Contribution Type (Cj, ~g/m ) (tij) (,ug/m3) to Ei (%)b Indoors at Home 15 0.75 11.25 47 Indoors at Work 50 0.15 7.50 31 Indoors, Other 25 0.04 1.00 4 In Transit 90 0.04 3.60 15 Outdoors 40 0.02 0.80 3 Ei = ~ Cj x t<, = 24.15 ,ug/m3 a Fraction of 24 hr spent in each microenvironment. b Percentage that each microenvironment contributes to the Bohr, time-weighted, integrated exposure (E,). Figure 2. Examples of the relative contributions from specific microenvironments to an individual's time- weighted, integrated exposure to respirable particles (RSP). to 70 percent of the time, and indoors at home, work, or elsewhere for more than 90 percent of the time. Although these values vary with age, gender, occupation, socio- economic status, and day of the week, it has become clear that indoor microenvi- ronments must be taken into account for a realistic assessment of exposure to many air pollutants (National Research Council 1981; World Health Organization 1982, 1983; Yocum 1982; Spengler and Sexton 1983; Lebowitz et al. 1984; De Bortoli et al. 1985; Stock et al. 1985), including NO2 (Quackenboss et al. 1982; Ryan et al. 1983; Sexton et al. 1983; Spengler et al. 1983), formaldehyde (Environmental Health Per- spective 1984; Sexton et al. 1986a), CO Jaeger 1981; Ott and Willits 1981; Ott and Flachsbart 1982; Ziskind et al. 1982), respi- rable particles (Spengler et al. 1981; Sexton et al. 1984a,b; Sexton et al. 1986b), radon (Nero and Lowder 1983), and organic va

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Sexton and Ryan 217 Table 3. Summary of Average Time-Activity Patterns for a 24-Hr Period Hours in Each Location Location Chapin (1974) Szalai (1972) Indoors Home Work Other Subtotal Outdoors Home Work Other Subtotal In Transit All modes Total 16.03 4.61 1.31 21.95 0.27 0.27 0.54 1.16 23.65a 16.75 4.03 1.63 22.41 0.23 0.12 0.35 1.25 24.01 a Shortfall from 24 hr not explained by the author. SOURCE: Adapted with permission from World Health Organization 1982. pars (Beau and Ulsamer 1981; Hollowell and Miksch 1981; Parke et al. 1981; Miksch et al. 1982; Molhave 1982; Otson et al. 1983; Wallace et al. 1984; Andelman 1985; Wallace et al. 1985a; Sexton et al. 1986c). In addition to the problems of identify- ing important microenvironments and of obtaining valid measurements of pollutant concentrations, the indirect approach suf- fers from the same problems as the direct approach: the selection and recruitment of a representative sample of people; the distri- bution, maintenance, and retrieval of many monitors; either a laboratory analysis of many samples returned from monitors in the field or calibration and validation of many real-time monitors; and the tran- scription and statistical analysis of data on pollutant concentrations and time-activity patterns. Biological Monitoring Air monitoring traditionally has been the principal means of exposure assessment. A major shortcoming of this approach is its failure to take account of factors such as respiration rate and depth of inspiration that may cause two individuals with the same measured exposure to receive vastly different doses. Differences in dose at equivalent exposures, coupled with varia- tions in individual susceptibility, introduce a large measure of uncertainty in the ex- trapolation from air pollutant measure- ments to the effects on human health. Thus there is an acute need for methods that provide better information about the inter- relationships of exposure, dose, and health effects. Biological monitoring is the measure- ment of environmental contaminants or their biological consequences after the con- taminants have crossed one of the body's surfaces and entered tissues or fluids. There are two kinds: measurements of environ- mental contaminants or their metabolites and derivatives in body fluids or excrete (exposure markers); and measurements of biological responses in cells and tissues (exposure markers and effects markers). Examples of the first type include direct chemical analyses, immunoassays, and bioassays specific for mutagenicity; these methods can be used to measure chemicals in the blood, urine, breast milk, saliva, and semen. Examples of the second category include immunologic and chemical meth- ods to detect and quantify covalently bound derivatives formed between acti- vated chemicals and cellular macromol- ecules such as nucleic acids and proteins, as well as observations of mutation, sister chromatic exchange, and chromosome ab- errations (Wogan and Gorelick 1985~. Biological measurements enable the de- velopment of exposure markers related qualitatively or quantitatively to measured air pollution concentrations (Goldstein 1981; Miller 1983; Berlin et al. 1984; Na- tional Institute of Environmental Health Science 1984; Wogan and Gorelick 1985; Ho and Dillion 1986~. Exposure markers are not necessarily closely correlated with subsequent health effects for two reasons: first, the site and mechanism of toxic action associated with adverse health effects are not always fully understood; and second, some identified sites of toxic action are not accessible for analysis. For example, coti- nine is a metabolite of nicotine that can be detected in the blood of infants whose mothers smoke as well as in the mothers

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228 Human Exposure to Air Pollution Table 10. Different Approaches to Air Pollution Exposure Modeling Model Type Examples References Statistical Various epidemiologic studies, such as Ferris et al. (1979) Harvard Air Pollution/Lung Health Speizer et al. (1980) Study Physical National Exposure Model (NEM) Biller et al. (1981, 1984) Johnson and Paul (1983a,b) Richmond and McCurdy (1985) Stepwise physical models which include Tosteson et al. (1982) physical parameters in stepwise regres- Sexton et al. (1984b) signs Spengler et al. (1985) Quackenboss et al. (1986) Physical model of indoor air quality Ryan et al. (1983) Sexton et al. (1983) Nazaro~and Cass (1986) Physical-stochastic Simulation of Human Air Pollution Ex- Ott (1981) posure (SHAPE) Ott and Willits (1981) Ott (1983-1984) Simulation System (SIMSYS) Letz et al. (1984) Ryan et al. (1986, 1987) Statistical techniques such as factor anal- ysis and cluster analysis can be used to elucidate the basic, underlying processes that determine air pollution exposure. These methods allow exposure to be parti- tioned into factors or clusters of correlated independent variables that tend to act to- gether. Such analyses are useful for inves- tigating correlations among independent variables and for understanding the relative contribution of specific factors or clusters of variables to the measured exposure. Physical Modeling The physical approach is based on the investigator's interpretation of the underly- ing processes that determine air pollution exposure. This interpretation is expressed as a quantitative description-mathematical formula, computer program, numerical ta- bles, or graph of the relationship between exposure and the determinants thought to be important. Since the model is chosen by the investigator, it may produce biased results because of the inadvertent inclusion of inappropriate parameters or the im- proper exclusion of critical determinants. In the physical modeling approach, the modeler begins with certain a priori as- sumptions about the underlying physical processes that determine air pollution ex posure. These assumptions are the basis for constructing a quantitative formulation that constitutes a physical exposure model. References and examples of the physical modeling approach are given in table 10. A simple physical model can be con- structed by assuming that personal expo- sure to air pollution is a strict function of the outdoor, or ambient, concentration. The mathematical form of this statement can be expressed as E = iamb (~2) where E is exposure for a specific air con- taminant, f denotes "a function of," and Camb is the ambient (outdoor) concentra- tion of the pollutant. This model would be most appropriate for air pollutants that result primarily from outdoor sources (see table 8~. An example of this basic model, which assumes that exposure can be approximated by a linear function, is E = aCamb + b (3) where E is exposure, a is the slope of the line relating exposure to the ambient con- centration, Camb is the measured ambient concentration, and b is the exposure when the ambient concentration is zero. Several groups have combined this model with data about personal exposures and ambient

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Sexton and Ryan 229 concentrations to estimate values for a and b in equation 3 (Tosteson et al. 1982; Ryan et al. 1983; Sexton et al. 1983; Spengler et al. 1985~. Further analysis has been carried out to delineate the relationship between the model parameters a and b and the physical processes such as the air-exchange rate, the first-order pollutant losses from physicochemical processes, and the indoor sources of air pollution (Ozkaynak et al. 1982; Ryan et al. 1983; Sexton et al. 1983; Letz et al. 1984~. The microenvironmental approach, dis- cussed earlier in the Methods section under the Indirect Approach to Exposure Assess- ment, is a more complex model based on similar ideas. Pioneered by Fugas (1975), this approach assumes that a person's time- weighted, integrated exposure is the prod- uct of the air pollution concentration in identified microenvironments and the time spent in those microenvironments (see equation 1~. Although this approach allows comparison of the contributions of selected microenvironments to the measured expo- sure, the identification and monitoring of . . . . ~ . . . po ~ .utants In critical m~croenv~ronments Is often difficult and expensive. The NAAQS Exposure Model (NEM) is a physical model that uses the microenvi- ronmental approach (Biller et al. 1981, 1984; Johnson and Paul 1983a; Richmond and McCurdy 1985~. The NEM also incor- porates the concept of a population cohort (a group of individuals having a statistical factor in common, such as, live in the same neighborhood or have the same commut- ing pattern) an assumption that is analo- gous to the requirement for spatial and temporal uniformity of pollutant concen- trations within a specific microenviron- ment. The model is designed to estimate the effect on population exposure that re- sults from changes in air quality standards. The NEM has been applied to CO Johnson and Paul 1983a,b), SO2 (Biller et al. 1984), and O3 (Richmond and McCurdy 1985~. A common shortcoming of the physical models described above is that while they do estimate expected exposure, they do not estimate the associated uncertainty. Evi- dence suggests that there is substantial . . . . . . variation In t le time spent In venous microenvironments (Sexton et al. 1984b; Clausing et al. 1986; Quackenboss et al. 1986), as well as in the pollutant concen . . . . . tratlons wltnln eac :n microenvironment (Spengler et al. 1983; Sexton et al. 1984a; Akland et al. 1985; Sexton et al. 1986a,b). Letz and his colleagues (1984) attempted to estimate the uncertainty in predicted exposure by including estimates of the variance in each model parameter. The variance in predicted exposure is estimated by a Taylor-series expansion. Results of this approach correlate well with findings from personal monitoring studies. Physical-Stochastic Modeling The physical-stochastic approach can be thought of as a third type of exposure model, even though it is a computational method. It combines elements of both the physical and the statistical approaches to estimate exposure. A mathematical model that describes the physical basis for air pollution exposure is first constructed. Then a random or stochastic component that takes into account the imperfect knowledge of the physical parameters that determine exposure is introduced into the model. The inclusion of the random com- ponent limits the effect of investigator- induced bias and allows for estimates of population distributions of air pollutant exposure. Misleading results can still be produced if model parameters are selected ineptly. In addition, the required knowl- edge about distribution characteristics may be difficult and expensive to obtain. By the introduction of a stochastic com- ponent into a physical model, the physical- stochastic approach attempts to account for the probabilistic nature of the physi- cal processes that determine exposure. In this way, the inherent uncertainty associ- ated with a mathematical abstraction of air pollution exposure is taken into ac- count. Two models, the Simulation of Human Air Pollutant Exposure (SHAPE) model and the Simulation System (SIMSYS) model, are representative of the physical- stochastic approach. Both models use the

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230 Human Exposure to Air Pollution microenvironment concept discussed ear- lier and use similar statistical approaches. They differ primarily in their application and intended use. The SHAPE model focuses on estimat- ing personal exposures (Ott 1981, 1983- 1984; Ott and Willits 1981~. Statistical tech- niques are used to select the appropriate characteristics of the individuals in the study and the microenvironments through which they move. Time-activity data are generated by selecting the type of activity as well as the duration of activity from probability distributions. Air pollutant ex- posure is modeled as the sum of 1-min exposures that are experienced throughout the course of an individual's daily activities. The SHAPE model has two distinct ad- vantages: less-detailed information on ti- meactivity patterns is needed because one must know only the probability of going from one activity to another; and a small number of microenvironments (14 in the published version) is required to estimate exposure. Disadvantages of this approach include the potential bias introduced by the modeler's selection of relevant microenvi- ronments; the need for accurate data on the probabilities of transitions between micro- environments and the time spent in specific microenvironments; and the difficulty of obtaining the distribution of pollutant con- centrations in important microenviron- ments. The SIMSYS model focuses on estimat- ing the distribution of air pollutant expo- sures within a population, with emphasis on the contribution of specific microenvi- ronments to the integrated exposure (Letz et al. 1984; Ryan et al. 1986, 1987~. The SIMSYS model is based on a physical de- scription of exposure similar to equation 3. Estimates of the probability distributions for the model parameters are obtained from the literature or from field studies. Basi- cally, the SIMSYS approach is similar to the SHAPES model and therefore shares the same generic disadvantages. The ad- vantage of this model is that it provides a means of evaluating the effects on human exposure of reducing air pollutant levels in ., . . specific microenvironments Source Apportionment Before it is feasible to evaluate the adequacy and cost-effectiveness of air pollution con- trol strategies, it is necessary to obtain more and better information about the rel- ative contributions of indoor and outdoor emission sources to measured personal ex- posures. Models such as SHAPE and SIMSYS are useful tools that aid in under- standing where and how exposures occur. They begin to address the issue of the extent to which public health is protected by the NAAQSs, which apply only to air outside buildings. As pointed out by Sexton and Hayward (1986), informed decisions about appropri- ate resource allocation to control air pollu- tion require more than just data on health effects. They depend also on adequate in- formation about important emission sources (source identification), chemical and physical properties of emissions (emis- sions characterization), and the effects of important source categories on indoor and outdoor air quality, as well as on personal exposures (that is, source apportionment). Although the major emission sources, indoors as well as outdoors, have been identified and work is progressing on the characterization of airborne discharges, the relative impact of indoor and outdoor emissions on personal and population ex- posures has not been addressed systemati- cally and comprehensively. Several types of source apportionment models have been applied to outdoor (am- bient) air, but their application to air pol- lution inside buildings or to personal expo- sures is just beginning. Consequently, insufficient data are available to determine the relative contributions of indoor and outdoor sources to measured personal ex- posures. This lack of information seriously hinders attempts to evaluate the costs and benefits of alternative control options (Sexton and Hayward 1986~. Validation and Generalization The models described in the preceding sec- tions are mathematical abstractions of

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Sexton and Ryan 231 physical reality that may or may not pro- vide adequate estimates of air pollution exposure. The only way to be sure that a model is capable of providing useful and accurate information is by validation- comparing model predictions with mea- surements independent of the measure- ments used to develop the model. More- c~ver model validation is a necessary precondition for the generalization of model results to a different or larger popu- lation. In the statistical modeling approach, data collection is an integral part of model con- struction. If the data are known to be from a statistically representative sample of the population, there is no need for further validation. If the results are to be extrapo- lated beyond the range for which the exist- ing data base provides a statistical descrip- tion, validation is necessary. The physical and physical-stochastic modeling ap- proaches must be validated with actual data from separately conducted field studies. Care must be taken that the data used to validate a model are not biased with respect to crucial model parameters. The validation step is useful only to the degree that the sample population is representative of the group to which results will be extrapolated. . , Research Recommendation Exposure Modeling. Attempts to model human exposures to air pollutants are rela- tively recent. Models vary widely in com- plexity and have not been validated ade- quately. The lack of data on the variability and covariance of time-activity patterns among individuals is a critical hindrance to model development. Perhaps the most pressing need associ- ated with modeling human exposure is the necessity for the external review and vali- dation of existing models. It is not clear, for example, whether current exposure models are adequate, or if a new generation of mod- els needs to be developed. The validation of existing models, using data sets other than those from which they were generated, is essential to answer this question. Source apportionment of ambient air pollution is a growing research field. Many investigators are now studying ambient air pollution to determine which pollution sources are affecting which receptor and to what degree. The work should go further and determine which sources most directly affect specific human populations. Future studies should focus on determining the relative contributions of indoor and out- door emission sources to personal exposures. Recommendation 5. Exposure Mod- eling. Research is needed to assess the ade- quacy of current exposure models through external review and validation. Validation of existing models is essential to determine whether these models are adequate or if a new generation of models should be devel- oped. In addition, how to apportion con- tributions of specific emission sources to individual exposures requires further study. The relevance of existing models (outdoor air) for source apportionment of personal exposures and of indoor air pollu- tion needs to be evaluated and new models need to be developed if existing models are shown to be deficient. Summary anc! Conclusions In its examination of the state of the art in air pollution exposure assessment, this chapter describes the general methods avail- able to determine exposure, the published studies that report on measurements of actual exposures, and the models that are used to estimate individual and population exposures. The goals are to help the reader understand the rudiments of this emerging field and to highlight the critical areas where further research is needed. In addi- tion, it attempts to impart an awareness of the importance of obtaining information about how, when, where, and why expo- sures occur. Evidence accumulated over the past few years indicates that adequate estimates of individual and population exposures for most air pollutants, including regulated and unregulated substances, cannot be derived

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232 Human Exposure to Air Pollution solely from measurements by traditional outdoor monitoring stations. Depending on the pollutant in question, exclusive re . fiance on outdoor measurements may over or underestimate the magnitude, duration, and frequency of exposures for the general population, as well as for many potentially susceptible subgroups. Although the rami f~cations of these findings for the develop ment and evaluation of air pollution controlHuman exposure data are obviously crucial strategies have not been explored fully, it isto the calculation of air pollution health clear that they raise policy issues thatrisks since this information is needed to should be taken into account in future regulatory decisions (Sexton and Repetto 1982; Sexton 1986~. Perhaps the most important lesson to be drawn from this chapter is the realization that accurate estimation of human expo sures is a prerequisite for realistic assess ment of air pollution health risks. Quanti tative risk assessment is rapidly becoming an integral part of the regulatory decisions that are aimed at protecting public health. Too often, however, the availability of suitable exposure data is taken for granted. The generalized form of the equation used to estimate health risks from environ . . . mental contaminants IS Health Risk (morbidity/mortality) = Potency x Exposure (dose/response) (concentration) x Exposed Population (number of people exposed) specify values for two terms in the equa- tion: exposure (including magnitude, dura- tion, and frequency) and exposed popula- tion. Moreover, exposure assessment is a critical element of epidemiologic studies, which are often used to develop values for the potency term in the equation. For ex- ample, epidemiologic studies that fail to account for indoor as well as outdoor ex- posures are prone to systematic and ran- dom bias and to the misclassification of exposures. Such errors can lead to spurious conclusions concerning dose/response rela- tionships for airborne contaminants, and, ultimately, to inappropriate estimation of public health risks. Summary of Research Recommendations HIGH PRIORITY Recommendation 1 Studies should be undertaken to provide information on the Time-Activity spatial and temporal distributions of human populations as they Patterns relate to exposure. The focus of these studies should be to construct the frequency distribution of time spent in important microenvi ronments, to identify the air pollution sources in those microenvi ronments, to specify the time of day that people are in particular microenvironments, and to determine the differences in time activity patterns associated with demographic and socioeconomic factors. Recommendation 3 Studies are required to provide representative data on human Exposure exposures and to investigate the link between measured exposures Monitoring and adverse health effects. These efforts will require (a) the devel opment of suitable instruments (for example, personal and indoor monitors) and measurement techniques (for example, noninvasive biological monitoring); (b) an application of the appropriate statis tical survey design methods; (c) the creation of extensive data bases on personal exposures, pollutant concentrations in important mi

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Sexton and Ryan 233 croenvironments, and time-activity patterns; and (d) the develop- ment and application of appropriate models to estimate human exposure. Recommendation 4 Research is required to adapt available biological measurement Biological Markers techniques to community air pollution measurement and control. of Exposure Studies that better define the nature of the relationship between exposure and dose and between dose and health outcome are needed. It is especially important to establish in controlled human populations (a) the sources of error for a particular biological measurement technique, (b) the validity of sample collection meth odology, (c) the appropriateness of internal and external standards, and (d) the adequacy of methods for quality control. Recommendation 5 Research is needed to assess the adequacy of current exposure Exposure Modeling models through external review and validation. Validation of existing models is essential to determine whether these models are adequate or if a new generation of models should be developed. In addition, how to apportion the contributions of specific emissions sources to individual exposures requires further study. The rele vance of existing models (outdoor air) for source apportionment of personal exposures and of indoor air pollution needs to be evalu ated and new models need to be developed if existing models are shown to be deficient. MEDIUM PRIORITY Recommendation 2 Respiration rate and mode (for example, mouth breathing versus Breathing Patterns nose breathing) are important determinants of air pollutant dose and therefore affect the health consequences of a measured expo sure. The changes in respiration associated with repose, exercise, standing, sitting, sleeping, talking, or any other important human activity should be measured or estimated. Acknowlecigments We thank the following people for their helpful comments on this manuscnpt: I. Bai- lar, I. Evans, and I. Spengler, Harvard Uni- versity; I. Goldstein, Columbia University; W. Ott and L. Wallace, EPA; and A. Wat- son, HEI. I. Schwartz and G. Raisbeck pro- vided editorial assistance. The manuscript was typed by M. E. Patten. Correspondence should be addressed to Ken Sexton, U. S. Environmental Protection Agency, Office of Health Research, Washington, DC 20460, or P. Barry Ryan, Harvard School of Public Health, Department of Environmental Science and Physiology, 665 Hun- tington Avenue, Boston MA 02115. References Akland, G. G., Hartwell, T. D., Johnson, T. R., and Whitmore, R. W. 1985. Measuring human expo- sure to carbon monoxide in Washington, D. C., and Denver, Colorado, during the winter of 1982-1983, Environ. Sci. Technol. 19:911-918. Andelman, J. B. 1985. Human exposures to volatile halogenated organic chemicals in indoor and out- door air, Environ. Health Perspect. 62:31~318. Annest, J. L., Pirkle, J. L., Makug, D., Neese, J. W., Bayse, D. D., and Kovar, M. G. 1983. Chronolog- ical trend in blood lead levels between 1976 and 1980, New Engl. J. Med. 308: 137~1377. Azar, A., Snee, R. D., Habini, K. 1975. Lead (T. F. Griffen and J. H. Knelson, eds.), Academic Press, New York, N.Y. Bartley, D. L., Doemeny, L. J., and Taylor, D. G. 1983. Diffusive monitoring of fluctuating concen- trations, Am. Ind. Hyg. Assoc. J. 44:241-247.

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