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Air Pollution, the Automobile, and Public Health (1988)

Chapter: Assessment of Human Exposure to Air Pollution: Methods, Measurements, and Models

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Suggested Citation:"Assessment of Human Exposure to Air Pollution: Methods, Measurements, and Models." National Research Council. 1988. Air Pollution, the Automobile, and Public Health. Washington, DC: The National Academies Press. doi: 10.17226/1033.
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Suggested Citation:"Assessment of Human Exposure to Air Pollution: Methods, Measurements, and Models." National Research Council. 1988. Air Pollution, the Automobile, and Public Health. Washington, DC: The National Academies Press. doi: 10.17226/1033.
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Suggested Citation:"Assessment of Human Exposure to Air Pollution: Methods, Measurements, and Models." National Research Council. 1988. Air Pollution, the Automobile, and Public Health. Washington, DC: The National Academies Press. doi: 10.17226/1033.
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Suggested Citation:"Assessment of Human Exposure to Air Pollution: Methods, Measurements, and Models." National Research Council. 1988. Air Pollution, the Automobile, and Public Health. Washington, DC: The National Academies Press. doi: 10.17226/1033.
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Suggested Citation:"Assessment of Human Exposure to Air Pollution: Methods, Measurements, and Models." National Research Council. 1988. Air Pollution, the Automobile, and Public Health. Washington, DC: The National Academies Press. doi: 10.17226/1033.
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Suggested Citation:"Assessment of Human Exposure to Air Pollution: Methods, Measurements, and Models." National Research Council. 1988. Air Pollution, the Automobile, and Public Health. Washington, DC: The National Academies Press. doi: 10.17226/1033.
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Suggested Citation:"Assessment of Human Exposure to Air Pollution: Methods, Measurements, and Models." National Research Council. 1988. Air Pollution, the Automobile, and Public Health. Washington, DC: The National Academies Press. doi: 10.17226/1033.
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Suggested Citation:"Assessment of Human Exposure to Air Pollution: Methods, Measurements, and Models." National Research Council. 1988. Air Pollution, the Automobile, and Public Health. Washington, DC: The National Academies Press. doi: 10.17226/1033.
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Suggested Citation:"Assessment of Human Exposure to Air Pollution: Methods, Measurements, and Models." National Research Council. 1988. Air Pollution, the Automobile, and Public Health. Washington, DC: The National Academies Press. doi: 10.17226/1033.
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Suggested Citation:"Assessment of Human Exposure to Air Pollution: Methods, Measurements, and Models." National Research Council. 1988. Air Pollution, the Automobile, and Public Health. Washington, DC: The National Academies Press. doi: 10.17226/1033.
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Suggested Citation:"Assessment of Human Exposure to Air Pollution: Methods, Measurements, and Models." National Research Council. 1988. Air Pollution, the Automobile, and Public Health. Washington, DC: The National Academies Press. doi: 10.17226/1033.
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Suggested Citation:"Assessment of Human Exposure to Air Pollution: Methods, Measurements, and Models." National Research Council. 1988. Air Pollution, the Automobile, and Public Health. Washington, DC: The National Academies Press. doi: 10.17226/1033.
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Suggested Citation:"Assessment of Human Exposure to Air Pollution: Methods, Measurements, and Models." National Research Council. 1988. Air Pollution, the Automobile, and Public Health. Washington, DC: The National Academies Press. doi: 10.17226/1033.
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Suggested Citation:"Assessment of Human Exposure to Air Pollution: Methods, Measurements, and Models." National Research Council. 1988. Air Pollution, the Automobile, and Public Health. Washington, DC: The National Academies Press. doi: 10.17226/1033.
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Suggested Citation:"Assessment of Human Exposure to Air Pollution: Methods, Measurements, and Models." National Research Council. 1988. Air Pollution, the Automobile, and Public Health. Washington, DC: The National Academies Press. doi: 10.17226/1033.
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Suggested Citation:"Assessment of Human Exposure to Air Pollution: Methods, Measurements, and Models." National Research Council. 1988. Air Pollution, the Automobile, and Public Health. Washington, DC: The National Academies Press. doi: 10.17226/1033.
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Suggested Citation:"Assessment of Human Exposure to Air Pollution: Methods, Measurements, and Models." National Research Council. 1988. Air Pollution, the Automobile, and Public Health. Washington, DC: The National Academies Press. doi: 10.17226/1033.
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Suggested Citation:"Assessment of Human Exposure to Air Pollution: Methods, Measurements, and Models." National Research Council. 1988. Air Pollution, the Automobile, and Public Health. Washington, DC: The National Academies Press. doi: 10.17226/1033.
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Suggested Citation:"Assessment of Human Exposure to Air Pollution: Methods, Measurements, and Models." National Research Council. 1988. Air Pollution, the Automobile, and Public Health. Washington, DC: The National Academies Press. doi: 10.17226/1033.
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Suggested Citation:"Assessment of Human Exposure to Air Pollution: Methods, Measurements, and Models." National Research Council. 1988. Air Pollution, the Automobile, and Public Health. Washington, DC: The National Academies Press. doi: 10.17226/1033.
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Suggested Citation:"Assessment of Human Exposure to Air Pollution: Methods, Measurements, and Models." National Research Council. 1988. Air Pollution, the Automobile, and Public Health. Washington, DC: The National Academies Press. doi: 10.17226/1033.
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Suggested Citation:"Assessment of Human Exposure to Air Pollution: Methods, Measurements, and Models." National Research Council. 1988. Air Pollution, the Automobile, and Public Health. Washington, DC: The National Academies Press. doi: 10.17226/1033.
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Suggested Citation:"Assessment of Human Exposure to Air Pollution: Methods, Measurements, and Models." National Research Council. 1988. Air Pollution, the Automobile, and Public Health. Washington, DC: The National Academies Press. doi: 10.17226/1033.
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Suggested Citation:"Assessment of Human Exposure to Air Pollution: Methods, Measurements, and Models." National Research Council. 1988. Air Pollution, the Automobile, and Public Health. Washington, DC: The National Academies Press. doi: 10.17226/1033.
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Suggested Citation:"Assessment of Human Exposure to Air Pollution: Methods, Measurements, and Models." National Research Council. 1988. Air Pollution, the Automobile, and Public Health. Washington, DC: The National Academies Press. doi: 10.17226/1033.
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Suggested Citation:"Assessment of Human Exposure to Air Pollution: Methods, Measurements, and Models." National Research Council. 1988. Air Pollution, the Automobile, and Public Health. Washington, DC: The National Academies Press. doi: 10.17226/1033.
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Suggested Citation:"Assessment of Human Exposure to Air Pollution: Methods, Measurements, and Models." National Research Council. 1988. Air Pollution, the Automobile, and Public Health. Washington, DC: The National Academies Press. doi: 10.17226/1033.
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Suggested Citation:"Assessment of Human Exposure to Air Pollution: Methods, Measurements, and Models." National Research Council. 1988. Air Pollution, the Automobile, and Public Health. Washington, DC: The National Academies Press. doi: 10.17226/1033.
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Suggested Citation:"Assessment of Human Exposure to Air Pollution: Methods, Measurements, and Models." National Research Council. 1988. Air Pollution, the Automobile, and Public Health. Washington, DC: The National Academies Press. doi: 10.17226/1033.
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Suggested Citation:"Assessment of Human Exposure to Air Pollution: Methods, Measurements, and Models." National Research Council. 1988. Air Pollution, the Automobile, and Public Health. Washington, DC: The National Academies Press. doi: 10.17226/1033.
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Suggested Citation:"Assessment of Human Exposure to Air Pollution: Methods, Measurements, and Models." National Research Council. 1988. Air Pollution, the Automobile, and Public Health. Washington, DC: The National Academies Press. doi: 10.17226/1033.
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Suggested Citation:"Assessment of Human Exposure to Air Pollution: Methods, Measurements, and Models." National Research Council. 1988. Air Pollution, the Automobile, and Public Health. Washington, DC: The National Academies Press. doi: 10.17226/1033.
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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

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.

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

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. . . . . . . . . .

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;

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

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

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

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

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

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

218 Human Exposure to Air Pollution themselves, but the role, if any, of this metabolite in the toxicity of tobacco smoke is unknown (National Institute of Environ- mental Health Science 1984~. Biological monitoring has three major advantages over environmental measure- ments for estimating health risks. First, only the pollutants that cross the boundary and enter the body are included in the analysis. Second, biological markers are more directly related to the biological pro- cesses from which the health consequences arise. And third, biological monitoring can serve as the basis for total risk estimates from multiple chemicals because it takes into account absorption by all routes and integrates exposures from all sources (Wo- gan and Gorelick 1985~. However, the avail- ability of biological exposure markers does not obviate the need for measurements of air pollution concentrations. They should com- plement air pollution measurements rather than replace them. For example, without data on the nature of the relationship between air pollution concentrations and their corre- sponding biological indicators, biological measurements by themselves are not suff~- cient to establish realistic air quality standards to protect public health. Research Recommendations Time-Activity Patterns. Available data on time budgets, activity patterns, and commuting behavior lack the specificity needed to make them useful for exposure estimation. For example, information is typically not available about the time spent in critical microenvironments, and about the presence of emissions sources such as smokers and unvented combustion appli- ances. Moreover, existing data are not coded so that important determinants of exposure, such as the amount of time spent indoors and outdoors, can be readily tabu- lated. In addition, available data analyses and summaries emphasize average values rather than distributions. Studies are needed to investigate the spa- tial and temporal distribution of human populations as they relate to exposure. These studies could be carried out indepen- dently or in conjunction with exposure measurements. They should define distri buttons of time spent in important micro- environments, air pollution sources present in these microenvironments, time of day that people are in specific locations, and differences in time-activity patterns accord- ing to demographic and socioeconomic fac- tors. It is especially important to know, for example, whether potentially susceptible groups such as asthmatics, young children, and the elderly have time-activity patterns that differ substantially from those of the general population. It is also important to determine which population subgroups are likely to experience high exposures to cer- tain air pollutants because of their specific time-activity patterns. Two types of studies are needed: first, studies relating time-activity patterns (in- dependent variable) to exposures (depen- dent variable); and second, studies relating factors such as age, gender, occupation, socioeconomic status, and pollutant sus- ceptibility (independent variables) to time- activity patterns (dependent variable). Data of both types should be collected for rep- resentative samples that can support infer- ences for the general population. Studies should be designed to measure regional and seasonal differences because climate and weather affect the amount of time people spend outdoors as well as the characteristics of indoor microenvironments, through fac- tors such as building weatherization, natu- ral and forced ventilation, and the burning of wood, coal, and kerosene. Prior to performing such studies, a con- sensus must be reached about what kinds of information are needed (important micro- environments and activities, presence of indoor pollution sources, etc.), how the information should be obtained (by trained interviewers, through self-administered questionnaires or diaries, or with auto- mated trackers, etc.), and what statistical analyses and data summaries are most ap- propriate. Once the appropriate survey in- struments have been designed and vali- dated, they must be standardized to facilitate comparisons among studies and to enable results from many studies to be pooled effectively in a common data base. ~ Recommendation 1. Time-Activity Patterns. Studies should be undertaken to

Sexton and Ryan 219 provide information on the spatial and tem- poral distributions of human populations as they relate to exposures. The goal of these studies should be to construct frequency distributions of time spent in important microenvironments, to identify the air pol- lution sources in those microenvironments, to specify the time of day that people are in particular microenvironments, and to de- termine the differences in time-activity pat- terns associated with demographic and so . . c~oeconom~c tactors. Breathing Patterns. The relation among exposure, dose, and health outcome is complicated by the fact that respiration rate and mode (mouth or nose breathing, depth of inspiration) vary with a person's activ- ity. The health effect of an inhaled pollutant is strongly influenced by how much actu- ally crosses one of the body's boundaries and reaches a target tissue, which in turn is strongly affected by the manner and rate of a person's breathing. Respiration rate and mode are affected by whether a person is sedentary, standing, sitting, sleeping, exer- cising, or talking conditions that most certainly are not statistically independent of time-activity patterns. Therefore, changes in respiration associated with these and other important human activities must be measured or otherwise taken into account before the health effects of air quality can be satisfactorily related to measurements of pollutant concentration. · Recommendation 2. Breathing Pat- terns. Respiration rate and mode are im- portant determinants of air pollutant dose and therefore affect the health consequences of a measured exposure. The changes in . . . . . respiration assoc~atec . wit n repose, exercise, standing, sitting, sleeping, talking, and other important human activities should be measured or estimated. Measurements During the past 15 years, several studies have been undertaken to assess the extent and magnitude of human exposure to a variety of air pollutants. Most of these studies addressed the adequacy of outdoor measurements for exposure assessment. Although no comprehensive evaluation is available yet for any specific pollutant, the results from these preliminary investiga- tions provide evidence of a consistent pat- tern. Outdoor measurements are weakly correlated, or not correlated at all, with individual exposure to most air pollutants, especially those with indoor sources. Fur- thermore, for airborne contaminants such as formaldehyde and many other volatile organic compounds, NO2, CO, and respi- rable particles, individual exposures are sig- nificantly higher than measured outdoor concentrations alone would imply (World Health Organization 1982; Spengler and Sexton 1983; Ott 1985; Spengler and Soc- zek 1985~. The realization that outdoor monitors often are inappropriate to estimate air pol- lution exposure creates an anomalous situ- ation. Compliance with National Ambient Air Quality Standards (NAAQS) is deter- mined exclusively by measurements at out- door, stationary monitoring sites. Thus, a substantial portion of the population resid- ing in an area that is nominally in compli- ance with NAAQS might still be exposed to concentrations that are either above or below the standards, (Sexton et al. 1983; Ott 198~84; Letz et al. 1984; Wallace et al. 1985a; Liu et al. 1986~. Similarly, epidemi- ologic studies that rely on outdoor moni- tors to define exposures are subject to sys- tematic and random bias, as well as to misclassification errors, which may lead to spurious conclusions about public health risks (Goldstein and Landovitz 1977a,b; Shy et al. 1978; Goldstein et al. 1979; Sexton et al. 1983; Spengler and Sexton 1983; Lippman and Lioy 1985; National Research Council 1985; Leaderer et al. 1986; Ozkaynak et al. 1986~. Most of the published exposure studies used either the direct or indirect approach, and focused on a small group of pollutants, including respirable particles, CO, NO2, and, more recently, 20 to 40 individual volatile organic compounds (for example, toluene, benzene, and xylenes). A few studies also were carried out to examine exposures to Pb and O3. Biological moni- toring for environmental exposures is still

220 Human Exposure to Air Pollution Table 4. Summary of Direct, Personal Monitoring Studies Carried Out in the United States Pollutant Individual Studies Number of Subjects Summary of Findings RSP CO NO2 Binder (1976) Dockery and Spengler (1977) Spengler et al. (1980) Dockery and Spengler (1981) Sexton et al. (1984a) Spengler et al. (1985) Cortese and Spengler (1976) Jabara et al. (1980) Ziskind et al. (1982) Ackland et al. (1985) Dockery et al. (1981) Quackenboss et al. (1982) Silverman et al. (1982) 20 22 46 37 48 101 66 98 1,083 Personal exposure did not correlate with outdoor measurements, and in most cases was substantially higher than outdoor concentrations would predict. Exposure to passive tobacco smoke was a major de terminant of RSP exposure. Time spent in transit was the primary deter minant of personal exposure. Highest CO 9 exposures were due primarily to motor vehicle exhaust. 9 66 18 Quackenboss et al. (1986) 350 Wallace et al. (1982) Wallace et al. (1984) Wallace et al. (1985a) Pb Azar et al. (1975) Outdoor monitors overestimated exposures for people not exposed to indoor sources, but underestimated exposures for people who reside in homes with Invented com- bustion appliances. 17 12 355 Outdoor measurements did not correlate well with personal exposures. Personal exposure and in-home concentrations tended to be higher than outdoor concen- trations for many volatile organic com- pounds. 150 Highest Pb exposures were experienced by taxi drivers. All subjects except office workers experienced highest exposure at work. NOTE: CO = carbon monoxide; NO2 = nitrogen dioxide; Pb = lead; RSP = respirable particles; VOCs = volatile organic compounds. in its infancy for most pollutants although wide-scale measurements of blood Pb lev- els are available (Annest et al. 1983~. Air Monitoring Direct Approach to Exposure Estimation. Information about direct, personal moni- toring studies carried out in the United States is summarized in table 4. The paucity of data on individual and population expo- sures is evident from the fact that fewer than 3,000 people have carried personal monitors for all pollutants combined. Most of these subjects were middle-class adult volunteers from urban areas. Respirable particles. Personal monitor- ing studies indicate that individual expo- sure (24 hr) typically is higher than mea- sured outdoor concentrations (see figure 3~. Furthermore, exposures correlated only weakly with outdoor concentrations. Ex- posure to environmental tobacco smoke accounted for a substantial portion of the difference between personal and outdoor values (Dockery and Spengler 1981; Sexton et al. 1984b). Since virtually all of the published studies on respirable particles fo- cused solely on particle mass, there are insufficient data to evaluate the differences in particle composition (Colome et al. 1982; Sexton et al. 1986b, c). Carbon monoxide. Elevated CO expo- sures have been associated with time spent in or near heavy traffic or in microenviron- ments affected by motor vehicle exhaust (for example, parking structures or lots, and gas stations). In-transit activity appears to account for most personal exposures (see table 5~. Average CO exposures tend to be

Sexton and Ryan 4sF 40 E - 35 _ O 30 ~ 25 UJ <' 20 8 ,L l5 cn . ~ 10 _ / ', Personal - \ persona' .d /. \ i' \ ~\ Indoors at home On ~6 lo\ Outdoors / / \ / \. \/ FRI SUN TUE THUR SAT MON WED FRI SUN TUE THUR SAT MON WED -29 1-31 2-02 2-04 ~06 2~8 2 10 2-26 2-28 3-02 3~4 ~06 3-08 3~0 DATE A, - \ a' \ \ i_ - -d / I '\ ~ / I / 6_ ,' _ _ /../ / Figure 3. Daily variations in mean outdoor, indoor at home, and personal exposure to respirable particles (RSP) for 46 nonsmoking volunteers from 23 residences in Waterbury, Vt. (Sexton et al. 1984b). below the NAAQS of 35 ppm/hr, but 1 to 3.5 percent of the subjects tested in Denver, Colorado, and Washington, D.C., received higher exposures (Akland et al. 1985~. Therefore, outdoor monitors are likely to underestimate exposures for individuals who experience the highest concentrations. Nitrogen dioxide. Outdoor NO2 con- centrations tend to be higher than measured personal exposure when there are no sig- nificant indoor sources (for example, un- vented gas-fired appliances). When individ- uals are exposed indoors (for example, in residences with gas ranges or kerosene Table 5. Summary of Mean CO Concentrations in Two U.S. Cities 221 heaters), outdoor measurements may sig- nificantly underestimate actual exposures (Quackenboss et al. 1982, table 6~. Because people typically spend more time outdoors during the summer when natural ventila- tion in buildings also is highest, outdoor measurements are most likely to approxi- mate individual exposure during the sum- mer months (Spengler et al. 1983; Quack- enboss et al. 1986~. Volatile organic compounds. The U. S. Environmental Protection Agency (EPA) has studied personal exposure to volatile organic compounds (VOCs) in several and Time Spent in Selected Microenvironmenes Denver, Colo. Washington, D.C. Mean Concn. Mean Concn. Microenvironment (ppm) Time (min)b (ppm) Indoors, parking garage In transit, car In transit, other vehicle Outdoors, near roadway In transit, walking Indoors, restaurant Indoors, office Indoors, store Indoors, residence 19 8 8 4 4 4 3 3 2 14 71 66 33 28 58 478 50 975 11 s 4 3 2 2 2 3 1 a Mean concentration in the specific microenvironment during the time that subjects were there. b Median time spent by subjects in specific microenvironments. SOURCE: Adapted with permission from Akland et al. 1985, and from the American Chemical Society. Time (min)b 11 79 49 20 32 45 428 36 1,048

222 Human Exposure to Air Pollution Table 6. Comparison of Outdoor Concentrations, Indoor Concentrations, and Personal NO2 Exposures in Portage, Wisconsin NO2 Concn. (,ug/m3) Cooking Fuel Sample Type No. of Samples Mean Std. Dev. Gas Outdoors 9 5 1.8 Indoors (kitchen) 9 67 29 Personala 33 34 16 Electric Outdoors 10 12 4.5 Indoors (kitchen) 10 8 4.9 Personalb 33 14 6.4 a Occupants of the nine residences with gas stoves. b Occupants of the 10 residences with electric stoves. SOURCE: Adapted with permission from Quackenboss et al. 1982, and from Pergamon Press. U.S. cities (Ott 1985; Wallace et al. 1985a,b). Findings from the Total Exposure Assess- ment Methodology (TEAM) project indicate that 11 compounds~hloroform, benzene, carbon tetrachloride, ethylbenzene, among others are commonly present in indoor and outdoor air. Personal exposure to these chemicals was consistently higher than re- corded outdoor concentrations, sometimes by an order of magnitude. In addition, night- time indoor concentrations in residences were higher than matched outdoor samples (see table 7~. Concentrations of these same compounds in exhaled breath correlated with personal exposure measurements (Wallace et al. 1985a). Lead. Personal exposure for Pb is not well represented by outdoor measure- ments, since exposures occur primarily during commuting periods (Fugas et al. 1972~. Highest Pb levels are encountered near streets with heavy traffic patterns. Therefore, individuals who spend a great deal of time in associated microenviron- ments, such as taxi drivers, experience the highest exposures (Azar et al. 1975~. Ozone. Few personal monitoring stud- ies have been undertaken for O3. Available Table 7. Comparison of Indoor and Outdoor Concentrations of Selected Volatile Organic Compounds for 85 New Jersey Residences Concn. (,ug/m3)~ Outdoor Air Compound Indoor Airb Median Maximum Median Maximum Chloroform 0.7 22 2.9 220 1, 1,1-Trichloroethane 4.2 40 16 880 Benzene 7.0 91 13 120 Carbon tetrachloride 0.8 14 1.4 14 Trichloroethylene 1.3 15 2.0 47 Tetrachloroethylene 2.6 27 5.6 250 Styrene 0.7 11 1.8 54 m,p-Dichlorobenzene (isomers) 0.8 13 2.8 920 Ethylbenzene 3.2 20 6.1 320 o-Xylene 3.0 27 5.0 46 m,p-Xylene (isomers) 9.9 70 16 120 a 12-hr averages. b Bedroom. SOURCE: Adapted with permission from Wallace et al. 1985a, and from Pergamon Press.

Sexton and Ryan 223 evidence suggests that outdoor O3 values significantly overestimate personal expo- sure for most people because so much time is typically spent indoors where levels are substantially lower (Stock et al. 1985~. Indirect Approach to Exposure Estima- tion. Given the diversity of microenvi- ronments that people move through each day (see table 2), application of the indirect approach to exposure assessment is not straightforward. Its utility depends on identification of and sampling in the micro- environments with the greatest potential to influence human exposure. The costs and practical difficulties of monitoring in all, or even most, of the locations where people are likely to spend their time limits the scope of indirect measurements. Most studies are designed in conjunction with direct measurements and typically include sampling only of community outdoor air and of air inside private residences. A few studies have investigated in-vehicle expo- sure (Ott and Willits 1981; Flachsbart and Brown 1985~. Most air pollution measurements have been made in the outdoor community air microenvironment. Nevertheless, because most people are outside for such a small fraction of the time, and because the amount of air pollution that penetrates in- doors is modified by building characteris- tics, outdoor measurements are of marginal value in estimating the actual exposures of humans to many airborne contaminants. Information on important microenviron- ments and sources that affect exposure to air pollutants is given in table 8. In-home concentrations of NO2 (Sexton et al. 1983; Quackenboss et al. 1986; Ryan et al. 1987), respirable particles (Dockery and Spengler 1981; Spengler et al. 1981, 1985; Sexton et al. 1984a,b), and many volatile organic compounds (Wallace et al. 1982, 1984; Wal- lace et al. 1985a) are the most important determinants of personal exposure for these pollutants. Outdoor or in-vehicle concen- trations in areas of heavy traffic are the primary determinants of personal exposure for CO (Ott and Flachsbart 1982; Nagda and Koontz 1985) and Pb (Azar et al. 1975; Fugas 1975~. Nonoccupational exposures to formaldehyde are likely to occur primar- ily indoors, especially in newer manufac- tured residences, because of emissions from building materials and furnishings (Envi- ronmental Health Perspective 1984; Sexton et al. 1986a). Among the other pollutants for which indoor sources are the principal cause of personal exposure are environ- mental tobacco smoke, asbestos, radon, and various microorganisms (National Re- search Council 1981; Spengler and Sexton 1983~. Research Recommendation Exposure Monitoring. Past exposure as- sessment studies.generally have investigated the magnitude of exposures compared to outdoor concentrations. Participants were often volunteers willing to undergo the in- convenience of personal or indoor monitor- ing. In only a few studies was a statistically random sample of participants selected (Akland et al. 1985; Wallace et al. 1985a; Sexton et al. 1986a; Ryan et al. 1987~. These exploratory studies indicated that fixed-site, outdoor monitors do not ade- quately estimate human exposure to most air pollutants, and that accurate extrapola- tion to people beyond the sample popula- tion often is not possible. Furthermore, the high degree of variability in pollutant con- centrations within individual buildings, within neighborhoods, and within cities will require substantial work to determine the distribution of exposures across a pop- ulation (for example, frequency distribu- tion for a city). In general, exposure assess- ment has not been not undertaken as part of a comprehensive and coordinated effort to define exposure distributions for a specific group of people. · Recommendation 3. Exposure Moni- toring. Studies are required to provide data on human exposures and to investigate the link between measured exposures and adverse health effects. These efforts will require (a) the development of suitable in- struments (for example, personal and in- door monitors) and measurement tech- niques (for example, noninvasive biological tests); (b) the application of appropriate

224 Human Exposure to Air Pollution Table 8. Important Indoor and Outdoor Sources of Nonindustrial Air Pollution Exposure Exposures Resulting Primarily.from Outdoor Sources Pollutant Sources Ozone (03) Secondary product of photochemical reactions between hydrocarbons and ni trogen oxides (NOx) Sulfur dioxide (SO2) Stationary sources, such as coal-fired power plants Particle-phase lead (Pb) Mobile sources Particle-phase sulfate Secondary product of atmospheric reactions between SO2 and other com pounds Pollens Trees, grass, weeds, plants Exposures Resulting Primarily from Indoor Sources Pollutant Sources Formaldehyde Particleboard, plywood, insulation, furnishings, adhesives, tobacco smoke Asbestos Insulation, fire retardants, texture paints, building construction materials Radon Underlying soil, building construction materials, well water Pesticides Consumer products (for example, insecticides, fungicides) Microorganisms (bacteria, viruses, fungi) Environmental tobacco smoke Air-cleaning equipment, humidifiers, flush toilets, carpets, pets, plants, people Cigarettes, cigars, pipes Exposures Resulting from Both Indoor and Outdoor Sources Pollutant Indoor Sources Outdoor Sources Volatile organic compounds (VOCs) Respirable particles (RSP) Carbon monoxide (CO) Nitrogen dioxide (NO2) Solvents, adhesives,synthetic building materi- als, aerosol sprays, pesticides, paint, tobac- co smoke, cooking, metabolic processes Tobacco smoke, cooking, resuspended house dust, aerosol sprays, condensation of va- pors, Invented combustion appliances Unvented combustion appliances (gas-fired cooking stove, furnace, or hot-water heat- er, kerosene heater) Unvented combustion appliances (gas-fired cooking stove, furnace, or hot-water heat- er, kerosene heater) Mobile and stationary sources Mobile and stationary sources. secondary reactions in the atmosphere Primarily mobile sources Mobile and stationary sources, secondary reaction product in the atmosphere statistical survey design methods; (c) the creation of extensive data bases on personal exposures, pollutant concentrations in im portant microenvironments, and human time-activity patterns; and (d) the develop ment and application of appropriate models to estimate human exposure. The respective roles of air monitoring (direct and indirect approaches) and biolog . . . . ca monitoring in exposure assessment studies must be defined and detailed. Direct pollutant concentrations in a representative measurements of personal exposure are sample, and determine to what extent the straightforward but costly, time-con- indirect approach complements direct ex suming, and labor-intensive. Indirect ap- posure measurements. preaches, which combine data on pollutant . . . . . concentrations 1n Important mlcroenvlron . . . . . . meets Wit n tlme-actlvlty 1ntormatlon, are likely to be less costly, but also less accu- rate. Moreover, direct measurements are essential to validate the results of indirect (microenvironmental) exposure studies. Future studies should evaluate the utility of the indirect approach, which depends on the identification of the important micro- environments and the ability to measure .. . . .

Sexton and Ryan 225 Both the direct and the indirect measure- ment schemes should be coupled with a biological monitoring program to investigate the relationship between air concentrations, biological exposure markers, and related health consequences. This linkage is critical for regulatory purposes, since the airborne levels of a specific contaminant are typically the focus of health-based regulations. Biological Monitoring Most of the biological data on exposure markers are related to occupational envi- ronments. Lead workers, for example, are tested routinely for clinical signs and symp- toms of Pb absorption, using markers such as porphyrins, B-aminolevulinic acid, and aminolevulinate dehyratase. Biological mar- kers for other metals such as cadmium, arsenic, mercury, selenium, tellurium, manganese, thallium, and zinc also are used widely (Ho and Dillion 1986~. Biological tests for acute exposure to CO (carboxyhe- moglobin in serum), trichloroethylene (trichloracetic acid in urine), and organo- phosphates (cholinesterase in serum) are well established (Miller 1983~. The rele- vance of available analytical techniques for biological monitoring of occupationally ex- posed workers was reviewed by Linch (1974), Baselt (1980), and Lauwerys (1983~. Biological monitoring has not been used extensively outside the workplace. Among the exceptions are the analysis of adipose tissue and other body compartments to determine residues for chlorinated hydro- carbons (Hayes 1975), of human milk to assess exposure to organochlorine pesti- cides (Savage et al. 1981) and polychlorina- ted biphenyls (Rogan and Gladen 1983; Rogan et al. 1983), of urine for the presence of cotinine, a marker of exposure to envi- ronmental tobacco smoke Jarvis et al. 1 984), of plasma to assess exposure to benzo~aipyrene (Hutcheon et al. 1983), and of exhaled breath to measure alveolar CO levels (Hartwell et al. 1984; Johnson 1984) and concentrations of selected volatile or- ganic chemicals (Wallace et al. 1985a). Techniques for the biological monitoring of human exposure to carcinogenic and mutagenic agents are a relatively recent development (Goldstein 1981; Berlin et al. 1984; National Institute of Environmental Health Science 1984; Wogan and Gorelick 1985~. Examples of techniques that have been used to detect human exposures to carcinogenic materials include determina- tion of the degree of hemoglobin histidine alkylation as a measure of ethylene oxide exposure (Osterman-Golkar et al. 1976; Calleman et al. 1978~; detection of muta- genic activity in the urine of anesthesiolo- gists who use halogenated anesthetic gases (McCoy et al. 1977), of patients treated with anticancer drugs (Yamasaki and Ames 1977), and of cigarette smokers (Kreibel and Commoner 1980~; the measurement of chromosome abnormalities and aberrations in workers exposed occupationally to Pb (Nordstrom et al. 1978a,b), vinyl chloride (Purchase et al. 1975; Szentesi et al. 1975; Hansteen et al. 1976), benzene (Tough et al. 1970), and organophosphates (Kiraly et al. 1976~; and the use of radioimmunoassay to detect a metabolite of taminobiphenyl Johnson et al. 1980) or aflatoxin B (Sizaret et al. 1982) in the urine of exposed individ- uals. Research Recommendation Biological Markers of Exposure. Biolog- ical monitoring has evolved rapidly, espe- cially for applications in the industrial workplace. Recent advances suggest that available biological measurement tech- niques can be applied to a wider range of environmental contaminants, including community and indoor air pollution. Stud- ies are needed first to determine the associ- ations among the air monitoring data, the biological markers of exposure, and the ultimate health outcome; and second to establish, prior to the general use of a particular biological measurement tech- nique, its sources of error, the validity of sample collection methodology, the appro- priateness of internal and/or external stan- dards, and the adequacy of methods for quality control. In addition, specific studies might be undertaken to identify and mon- itor potential exposure markers in surviv- ing animals throughout the course of large- scale chronic animal studies, to evaluate

226 Human Exposure to Air Pollution human materials including placentas, abor- tuses, and autopsy and biopsy reports as potential markers of exposure, and to reex- amine the long-term or ongoing epidemi- ologic cohort studies to determine whether indicator or precursor lesions can be iden- tified retrospectively (National Institute of Environmental Health Studies 1984~. a Recommendation 4. Biological Mark- ers of Exposure. Research is required to adapt available biological measurement techniques to community air pollution studies. Studies that better define the nature of the relationship between exposure and dose, and between dose and health out- come, are needed. It is especially important to establish in controlled human popula- tions (a) the sources of error for a particular biological measurement technique, (b) the validity of sample collection methodology, (c) the appropriateness of internal and ex- ternal standards, and (d) the adequacy of methods for quality control. Modeling Human Exposure to Air Pollution Models are useful tools to quantify the relationship between air pollutant exposure and important explanatory variables (for example, time-activity patterns), as well as to estimate exposures in situations where measurements are unavailable. In the United States, there are approximately 240 million people, about 82 million residences, and some 15 million commercial and public buildings. It is impractical to have everyone carry a personal monitor or to undertake pollutant measurements inside all build- ings. Exposure models obviate the need for such extensive measurement programs by providing estimates of population expo- sures that are based on a small number of representative measurements. The chal- lenge is to develop appropriate models that allow for extrapolation from relatively few exposure measurements to a much larger population. Human exposure to air pollution can be thought of as a physical system in the same . .. . way that the forces acting on a falling object are a physical system. Newton's laws of motion might be selected as an appropriate model, for example, to de- scribe the motion of an object falling in a gravitational field. Subsequent to selecting this model, it is necessary to verify it through experimentation. Experimental data might indicate that certain important determinants, such as air resistance, have been left out of the model. But even if all the critical parameters are included, some error is always associated with the pre- dicted value because of measurement er- rors. Statistical models are those for which predicted values are approximations subject to uncertainties introduced by factors de- liberately omitted in the model, as well as by measurement errors. Techniques of sta- tistical data analysis a sophisticated form of curve-fitting may be used instead of modeling the contributing physical, chem- ical, and biological processes, even if the processes are known and understood. For simplicity and clarity, the following discussion distinguishes between statistical exposure models developed from a stochas- tic or probabilistic perspective, and physi- cal exposure models, defined as those that are developed from an understanding of the underlying physical processes. This is an artificial distinction that focuses on the ori- entation, either statistical or physical, from which the model was constructed. In truth, all exposure models are statistical to some degree because the physical laws that con- strain human activities and pollutant con- centrations do not take into account abso- lutely every contributing process and factor. Physical-stochastic models, which combine the concrete aspects of the physi- cal approach with the probabilistic aspects of the statistical approach, are also dis- cussed. All three classifications of exposure models are compared in table 9. Statistical Modeling The statistical approach requires the collec- tion of data on human exposures and the factors thought to be determinants of ex- posure. These data are combined in a sta

Sexton and Ryan 227 Table 9. Comparison of Different Approaches to Air Pollution Exposure Modeling Model Type Parameter Statistical Physical Physical-Stochastic Method of formulation Required input Collected data on human exposure Hypothesis testing Advantages Makes use of real data in the model-building process Disadvantages Physical laws Knowledge of important pa- rameters and their values in the system to be modeled True model developed from a . . pnor1 conslc .eratlons Requires data on hand for model building; extrapolation beyond data base is difficult Includes researcher's biases; must be validated Physical laws and statistics Knowledge of important parameters and their distributions in the sys- tems to be modeled Model developed from a . prlor1 conslc .eratlons; stochastic part allows uncertainty to contrib . · . ute, reaucmg 1mpor- tance of research biases Requires much knowledge of system; must be vali- dated tistical model, normally a regression equa- tion or an analysis of variance (ANOVA), to investigate the relationship between air pollutant exposure (dependent variable) and the factors contributing to the mea- sured exposure (independent variables) (see table 10~. In principle, the results are appli- cable only to the data used to produce the model. If the study population constitutes a representative sample, however, then ex- trapolation of results to a broader group may be justified. Furthermore, selection of factors that influence exposure has a sub- stantial effect on the outcome of the analy- sis. Spurious conclusions may be drawn, for example, from statistical models that include parameters that are correlated with, but not causally related to, air pollution exposure. Initial statistical analysis of the data set usually focuses on defining the range of deviation associated with each variable and examining the bivariate and multivariate correlations between and among the inde- pendent variables. A lack of sufficient vari- ability for one or more of the parameters in the model will limit the development of a useful predictive tool, whereas inclusion of independent variables that are highly col- linear may obscure the importance of crit- ical exposure determinants. Once this type of exploratory analysts is completed, model building can begin. One of the simplest statistical models is the categorical comparison, in which exposure data are compared for two or more catego- ries of a specific independent variable. An example of this approach is a comparison of NO2 exposures inside residences with and without gas ranges (Speizer et al. 1980~. Once it has been verified that NO2 expo- sures are significantly higher in the homes with gas ranges, then it is feasible to divide the study population into two groups, peo- ple who live in homes with gas ranges (high exposure) and those who live in homes with electric ranges (low exposure) and compare symptom and illness rates on the basis of this simple dichotomous vari- able. Such categorical analyses often sug- gest additional avenues of investigation that further refine the nature of the relationship between exposure and important explana- tory variables. ANOVA provides a more quantitative method for categorical comparisons. A generalized linear model is developed that quantifies the relationship between the ex- posure and the selected explanatory varia- bles in terms of the proportion of explained variance. The use of ANOVA allows for an investigation of the marginal effect of in- cluding new variables in the model, and thereby provides a means to determine the importance of identified exposure determi- nants.

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

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

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

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

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

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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"The combination of scientific and institutional integrity represented by this book is unusual. It should be a model for future endeavors to help quantify environmental risk as a basis for good decisionmaking." —William D. Ruckelshaus, from the foreword. This volume, prepared under the auspices of the Health Effects Institute, an independent research organization created and funded jointly by the Environmental Protection Agency and the automobile industry, brings together experts on atmospheric exposure and on the biological effects of toxic substances to examine what is known—and not known—about the human health risks of automotive emissions.

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