outdoor

Personal Exposure to Nitrogen Dioxide: Relationship to Indoor/Outdoor Ah Quality and Activity Patterns James J. Quackenboss,*~tJohn D. Spengier,t Marty S. Kanarek,§ Richard Lek,$ and Colin P. DuffyQ

Division of Respiratory Sciences, Health Sciences Center, University of Arizona, Tucson, Arizona 85724, Department of Environmental Science and Physiology, Harvard School of Public Health, Boston, Massachusetts 021 15, and Department of Preventive Medicine and The Institute for Environmental Studies, University of Wisconsin, Madison, Wisconsin 53705

rn Personal NO2 exposures and indoor and outdoor concentrations were measured for nearly 350 individuals in the Portage, WI, area. Concentrations in homes with gas stoves averaged 18 pg/m3 higher in the summer (median indoor/outdoor ratio 2.4) and 36 pg/m3 (median indoor/outdoor ratio 3.2) higher in the winter than outdoor levels. Personal exposures were closely related to indoor averages for households with gas stoves (r = 0.85 summer, r = 0.87 winter) and with electric stoves (r = 0.68 summer, r = 0.61 winter); more than 65% of the average day was spent a t home while about 15% was spent outdoors in summer and less than 5% in winter. The association between personal exposure and outdoor levels of NOz was weakest during the winter for both gas (r = 0.20) and electric (r = 0.28) stove groups. The measures of exposure and time allocation indicate that there is a wide range of variability in personal exposures to NO, that may not be adequately accounted for by simple stratifications based on cooking fuel type.

Introduction In the course of daily activities, individuals move about from location to location, breathing samples of the air from each. The amount sampled is determined, in part, by the duration of time spent there, while the magnitude of pollutant present in each location during the specific time periods is a complex function of the ambient background level, of the proximity to sources of pollutant generation and release, and of various mechanisms for removal or dilution. Nitrogen dioxide (NO,) is formed as a byproduct of high-temperature combustion. Outdoor concentrations are closely related to the proximity of the sampled location to major sources such as motor vehicle traffic and fossil fuel power plants, to meteorological factors influencing the transfer and dilution of NO,, and to atmospheric conversion reactions (1). Indoor concentration profiles are related to these outdoor levels as well as to usage patterns for unvented or improperly vented indoor combustion applicances (e.g., gas stoves and unvented kerosene space heaters), to air infiltration and ventilation, and to chemical ~~

‘University of Arizona, Harvard School of Public Health. University of Wisconsin.

*

0013-936X/86/0920-0775$01.50/0

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reactions and adsorption or absorption of the gas on indoor surfaces (2, 3). Until recently, epidemiological studies of the health effects of air pollution have relied on measurements of “ambient” air quality that are obtained from fixed location monitoring stations, rather than on the actual exposures of the individuals whose health status is being tested, to draw inferences regarding possible exposure-response relationships (4-7). Several recent reports have indicated that such exposure measurements fail to adequately assess individual exposure (2, 5, 7, 8-12). This inadequacy is acutely apparent for NOz, since several reports have consistently shown that NOz concentrations consistently exhibit a declining gradient as one moves from kitchens with gas stoves to nonkitchen areas in these homes, to outdoor locations nearby, and then to kitchen and nonkitchen locations inside households that use electricity for cooking (13-18). The importance of these departures from fixed station ambient air monitoring results is underscored by summaries of human activity pattern studies (19), indicating that on the average 90% of each day is spent indoors by employed men, while homemakers spent nearly 95% of their time indoors (2, 20-23). Physically, exposure may be defined as the pollutant concentration present at the exchange boundaries of a receptor during specified times. Ott (21) has incorporated both space and time dimensions by conceptualizing exposure as the event that “a person comes into contact with a pollutant”. Contact is then specified as the intersection of an individual and a pollutant in the same location at the same time. Location may be narrowly defined in terms of a three-dimensional coordinate system or be aggregated by existing environmental zones such as rooms or floors within a household or, more broadly, as inside the home itself. With each level of aggregation or averaging, some within zone variability may be lost, although these differences are likely to be smaller than those between zones (e.g., between homes, or between indoor and outdoor locations) and are probably of minimal health significance (24). At a more general level, Duan (25, 26) aggregates locations with homogeneous pollutant concentrations at specified times into microenvironments with concentrations yj, and then groups these to form a reduced number of microenvironment types for sampling and modeling purposes. Exposure may then represented as a linear combination of the individual’s “sample” average concen-

0 1986 American Chemical Society

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775

tration for each microenvironment type (ck),weighted by his or her time allocation to microenvironments of that type (tk):

E = CCktk

(It = 1, ...,K microenvironment types) (1)

This model is a generalization of that proposed by Fugas (4) for deriving a “weighted weekly exposure” (WWE) estimate of total exposure by using the concentration measured at one location ( T ~to ) represent the levels sampled by individuals in all microenvironments of the same type (ck). This approach does not incorporate variability among samples collected by different individuals at distinct times and is sensitive to a potential lack of independence between these individual sample concentrations and individual time allocations. This potential may be realized when pollutants are generated as a result of individual activities such as cooking or smoking that are likely to increase in frequency as more individuals are present at a given location. Personal exposure monitoring implicitly incorporates both the time spent in various microenvironments of each type (tk) and the variability in air samples obtained by different individuals from each microenvironment type (ck). This allows for direct evaluation of both variability and central tendency for human exposures, and for estimation of average sample concentrations ( c k ) for each microenvironment type (26). The development of a small, inexpensive, and reliable passive monitor for NO, by Palmes et al. (27) has made a large-scale general population study of personal exposures to NO2 practical. This potential, combined with concerns regarding the adequacy of current exposure assessment practices and with several recent reports advocating personal exposure studies as part of epidemiological investigations into the health effects of air pollution (2, 5,8-11,28), have prompted our investigations. The objectives for this study were the following: (1)to examine the relationship between personal exposure and indoor and outdoor NO, concentrations; (2) to explore the association between household members in personal exposures; (3) to determine those household and individual characteristics that are most important in explaining variability in personal exposure; (4) to determine what types of measurements are needed to adequately assess personal NOz exposures. Previous personal exposure studies have demonstrated differences between indoor, outdoor, and individual exposures for NO, using smaller study populations (29), especially for gas homes (30,31) or those using unvented space heaters (32). Mean personal exposures for families with gas stoves have been shown to be closer to indoor than to outdoor levels, while those using elective stoves were indistinguishable from ambient outdoor measures. In addition, an individual’s exposure to NO, was found to be closely related to that of other family members (30). Materials and Methods

Individual exposure to NO,, time spent in various locations, and household NO, concentrations were measured for nearly 350 volunteers residing in 82 homes in the Portage, WI area for 1week during both the summer and winter of 1981-1982 as a component of the exposure assessment activities for a longitudinal air pollution/health study (33). Households were separated into groups of approximately 10 homes each that were sampled during the same week-long period to allow for travel time and instructional visits to each home. The protocol for these visits included a description of study objectives and in776

Environ. Sci. Technol., Vol. 20, No. 8, 1986

structions for using the passive diffusion NO, monitors and for completing an activity diary. An example diary page was filled out for that day; this was checked for completeness and corrected with each subject to reduce recording errors. All household residents were asked to participate, although this was not always possible. During the winter phase, short visits were made to each home to return their summer results and to encourage continued participation. Sample Selection. The target population selected was that of families from the Portage area whose school-aged children were participating in the Harvard Six Cities Study. The basic sampling design was a stratified cluster sample (34) in which the primary sampling unit (cluster) was the family and the secondary sampling units (elements within cluster) were individual family members. Households were stratified by type of cooking fuel used (gas or electricity). Letters describing the study and requesting volunteers were sent out to obtain approximately equal numbers of gas- and electric-cooking fuel homes. A postage-paid response postcard was provided, and nonrespondents were contacted by telephone. A more detailed description of the study was sent to those indicating a willingness to consider participating in the study. From this pool of household units, approximately equal numbers of each cooking fuel type were randomly allocated (without replacement) into a group of homes that were then visited and sampled during the same week. NOz Monitoring. Passive diffusion NO, dosimeters developed by Palmes et al. (27) were used to determine the week-long average NO2 concentrations for fixed household locations and the average exposures of individuals. These monitors are simple to use, have a good shelf life before and after exposure, and give results that are both accurate and reproducible (35). Extensive use has been made of these devices for monitoring both indoor and outdoor levels of NOz (14-16, 18, 36, 37). Household monitors were placed by project staff outside near the home, in at least one bedroom on each floor, and in the kitchen at 4-6 ft (1.2-1.8 m) in height. Kitchen monitors were placed between 8 and 10 f t (2.4-3.05 m) from the stove. Indoor monitors were placed to avoid windows, corners, and heating vents; outdoor monitors were located on the shady side of the house, away from driveways, roads, and exhaust vents. Each participant was also assigned a monitor, his or her name was written on tape affixed to the top end, and the monitor identification number was recorded on their activity diary. Volunteers were instructed to wear their monitor at approximately breathing height by clipping it onto their shirt collar or lapel, to keep it outside of coats or jackets, and to keep their monitor nearby when not wearing it. Integrated average NO, concentrations measured by the kitchen monitors and by bedroom monitors on the same floor were grouped together to give one compartment average. When appropriate, NO, measurements from nonkitchen floors were aggregated to give a second compartmental average. These two within home averages were subsequently combined to yield a single household average for indoor NO, concentrations. During the winter phase, integrated average NO2 concentrations were measured inside of 17 schools that were attended by participating children. Monitors were placed in one classroom on each floor, and a school-wideaverage was estimated from these for the time period sampled. To allow for direct comparison with student’s personal monitors, a time-weighted average for each week-long time period during which a group of households was concur-

rently sampled was calculated. Monitors were prepared and analyzed at the Wisconsin State Laboratory of Hygiene by using methods based on Palmes et al. (27) with modification by Wolfson (38). Analyses of replicate pairs of these monitors, used for household measurements, has given a precision estimate of 1.68 pg/m3 (18). Their sensitivity has been estimated as approximately 1128 (pg/m3) h, with an accuracy of within 10% for these preparation and analysis procedures (39, 40). Survey Materials. For each day of the week-long sampling period, participants recorded the time periods they spent in each of five general location categories: (1) inside of their home, (2) outside (any where), (3) inside of a motor vehicle, (4)inside at work or school, and (5) inside at other indoor locations. The reporting format was developed to derive week-long totals for the proportion of time spent in each category and was field-tested during a pilot study (31). In addition, time spent “cooking or helping to cookn and “near people who are smoking” was also recorded to obtain information about time spent near potential sources of NOP. However, the lack of a clear relationship between time spent near stoves or smokers and the possible NOz emission rate for each (per unit of fuel or per cigarette) makes interpretation of these values difficult. During the winter phase, time periods near smokers a t home were separated from those away from home, since the possible effect of smoking within the home on indoor NOz concentrations should already be included by the monitors located there. Participants were also instructed to record the amount of time each day that they did not wear their monitor as well as the location where it was left during this time period. These time periods were tabulated for several locations, and the individual’s monitored exposure was corrected by using the measured concentrations for that location, weighted by the time the monitor was left there (31). For each day, a marginal total for the amount of time spent in each location category was estimated; week-long totals and proportions were derived from these. Errors in recording leading to potential uncertainties in interpretation were also recorded, as a total number of hours in question. In order to assess the validity (41) of this reporting procedure for determining average time allocation, a separate format 24-h recall questionnaire derived from Chapin (42)was administered to a random sample of 12 subjects during the pilot study. For all five location categories, the correlation coefficients between the two responses for a l-day period exceeded 94%. All five correlations were statistically significant with an overall error rate of a = 0.025, using the Bonferroni method for nonorthogonal tests (43),indicating that these two methods give approximately the same results. Most discrepancies were due to the limitation of the diary format to l/z-h intervals. For longer term, week-long averages this reporting format should provide valid reports of time allocation. A subject questionnaire was administered during the initial visit to ascertain individual demographic characteristics, smoking habits, and commuting patterns. Ventilation, heating, and cooking systems for the workplace as well as potential sources of occupational NOz exposure (such as welding, gasoline or diesel engines, gas ovens or flames) were also requested. Characteristics of the schools were determined by interviewing the maintenance and engineering staff for each monitored school. Household characteristics were detailed by using an additional questionnaire that was completed by a parent

and returned by mail to our office. This included questions regarding (1) sources, such as the fuels used for cooking, heating water, and space heating, the presence of a stove or oven pilot light, and the household cooking patterns and (2) factors potentially influencing ventilation rates such as the kitchen exhaust fan’s venting and usage and the use of plastic on windows to reduce air infiltration, and neighborhood characteristics. This questionnaire was developed for the study conducted by Spengler et al. (18). Statistical Analyses. Personal exposures to NO2 were compared with indoor and outdoor measurements using the sample means for each cooking fuel group, and the Pearson product-moment correlation coefficients. As a result of the natural clustering of household members, the variance in individual exposures can be described by a standard model I1 (random effects) ANOVA layout (with varying numbers of observations in each family): G A ~+ where uA2 is the variance component for random differences between households and u2 represents individual variability within households (44). From this formulation, an expression for the intrafamily (intraclass) correlation between any two members of the same household was used to indicate the proportion of total variance that is associated with household factors that are shared by its members: p = S A z / ( S A 2 + Sz) (2) where S A z = (Sbz - S;)/([Cnj - (Cn,2)/CnjI/(N - 111 (3) is an estimate of bA2,s b z and Sw2are the between and within class mean squares, n, is the number of observations from the j t h household (j= 1, ..., a; a is the number of homes), and N is the total sample size (45). Variance of the sample mean was estimated by var(x) = s b z / N , the standard error is SE = [ v a r ( ~ ) ] l(45). /~ The relationship between personal NOz exposure and individual and household factors was evaluated by using stepwise multiple linear regression analysis (46). Logarithms of the NOz concentration measurements were taken to compensate for heterogeneity of their variances (47). A pattern of increasing spread with increasing NOz concentrations has been previously reported for indoor NO2 concentrations (28). Missing values for quantitative independent variables were filled to conform to a linear model (47,48) by using the BMDP program PAM (49),and the error degrees of freedom appropriately reduced. However, the frequency of missing values was very small, both for individual-level variables ( 4 % and ) for household-level variables ( 0.13. c p d a = number of households.

> 0.03.

loo.

HOME AVERAGE CONCENTRATION (uG/M3) Figure 2. Average personal NOp exposure for each home compared with average Indoor concentratlons for summer and winter.

homes in summer, while in the winter the corresponding correlations were only r = 0.20 (p > 0.13) and r = 0.28 (p > 0.03). These graphs and their associated correlation coefficients indicate that only a small portion of the variability in average personal NOz exposures between families can be accounted for by differences in measurements made outside their homes. In contrast, average indoor home NO2 concentrations demonstrated a much closer association with the average personal exposures of those living there, as is shown in Figure 2 and by the correlation coefficients in Table 11. This association was strongest in gas homes, r = 0.85 (p < 0.001) for summer and r = 0.87 in the winter.

Table 111. Intraclass Correlation ( p ) between Members of the Same Household for log-Personal NOz Exposure" phase summer winter

stove gas electric gas electric

P

SA2

S2

a

N

0.64 0.42 0.60 0.41

0.11 0.047 0.12 0.075

0.06 0.065 0.08 0.11

38 50 34 48

150 206 127 197

"SA* = estimated "among household" variance component; S2 = estimated "within household" variance component: a = number of households; N = number of valid personal exposures. Envlron. Sci. Technol., Vol. 20, No. 8 , 1986

779

Table IV. Mean Percent Time Spent in Various Locations for Three Population Groups phase summer

winter

location home (SD) outside (SD) motor vehicle (SD) work/school (SD) other indoors (SD) N home (SD) outside (SD) motor vehicle (SD) work/school (SD) other indoors (SD) N

workers

population group nonworkers

students

combined totals

59.3 (11.9) 12.3 (9.1) 5.8 (4.2) 15.5 (10.9) 7.0 (6.4) 137 66.1 (11.4) 3.3 (5.35) 5.6 (5.6) 18.6 (10.4) 6.4 (6.0) 127

75.2 (12.1) 12.9 (9.9) 4.4 (2.7) 0.2 (0.8) 7.2 (6.4) 32 83.3 (8.4) 1.9 (2.0) 4.3 (2.5) 3.0 (7.1) 7.6 (5.3) 26

68.3 (12.5) 15.0 (9.3) 3.3 (4.3) 4.4 (7.8) 9.0 (9.6) 177 66.1 (10.1) 3.9 (3.3) 3.3 (2.6) 19.5 (7.5) 7.3 (6.2) 176

65.4 (13.3) 13.7 (9.4) 4.4 (4.3) 8.4 (10.6) 8.1 (8.2) 346 67.5 (11.5) 3.5 (4.2) 4.2 (4.1) 17.9 (9.7) 7.0 (6.1) 329

Table V. Comparison between Partial Time-Weighted Average (TWA) Exposure Estimates and Personal Exposure (PE) Measurements for NOz (bg/ma) log-linear correlation phase summer

stove gas electric

winter

gas electric

a

One case removed: works near

gas

group

r

R2

student worker other student worker other student worker other student worker other

0.76 0.64 0.80 0.51 0.18 0.76 0.81

0.57 0.41 0.64 0.26 0.03 0.58 0.66 0.50 0.15 0.30 0.26

0.71

0.39 0.56 0.51 0.84

21.5 (1.5) 26.0 (1.4) 21.8 (1.7) 15.2 (1.4) 18.2 (1.3) 17.4 (1.5) 36.6 (1.5) 45.0 (1.6) 43.3 (1.4) 12.8 (1.4) 14.8 (1.4) 11.7 (1.5)

21.0 (1.5) 17.3 (1.6) 21.9 (1.4) 9.2 (1.5) 8.0 (1.4) 9.6 (1.8) 34.1 (1.5) 31.9 (1.7) 36.0 (1.3) 9.5 (1.5) 5.9 (1.8) 6.8 (1.7)

N

72 59 19 106a 81 14 62 52 13 107" 7Zb 13

oven. Three cases removed: possible occupational exposures (welding).

Intraclass correlation coefficients, shown in Table 111, indicate the proportion of variability in individual personal exposures that may be explained by common characteristics of those microenvironments shared by individuals from the same home. For gas stove homes, the degree of association between members of the same household for personal exposure was too great to assume that individual measurements were independent-an assumption necessary for least-squares regression analysis. Part of this association may be attributable to time spent at home relative to other locations. Mean percentages for time spent in each of five locations is summarized in Table IV for workers, nonworkers, and students during the summer and winter phases. Time spent inside at home was the largest mean proportion for all three groups, with overall mean percentages of 65.4% in the summer and 67.5% in the winter. This implies that the home is the principle location of exposure to air pollutants, although time spent in other locations cannot be neglected when considering total integrated exposure. Specifically, time spent at work or school accounted for nearly 20% of the average day during the winter for students and workers, making this an important microenvironment contributing to total exposure. Working with or nearby welding, gas space heaters, or gasoline engines may account for the winter phase personal NOz exposures of three individuals from electric stove homes that exceeded the levels measured for many of the cases from gas stove homes. In contrast, time spent away from gas-cooking fuel homes may account for some of the overlapping in personal exposures between the two cooking fuel groups. This activity data and NOz measurements were combined to form a partial time-weighted average (TWA) model. Home average indoor and outdoor levels were 780

0.71

geometric mean (SD) PE TWA

Environ. Sci. Technol., Vol. 20, No. 8, 1986

weighted by the proportion of time spent there; their sum is compared with the individual's actual exposure measurement in Table V. The Pearson correlation coefficients corresponding to the summer sampling period were r = 0.76 for studenh and r = 0.64 for workers from gas cooking fuel homes and r = 0.51 for students and r = 0.18 for workers in the electric stove group. School exposures were estimated by weighing the school average indoor concentration by the time spent there for elementary, middle, and high school students in the winter phase; this was added to the estimates for home and outdoor exposures. For students, correlations of these TWA estimates were r = 0.81 and r = 0.56 from the gas- and electric-fuel groups, respectively. Nonstudent correlations were 0.71 for workers from gas stove homes and 0.55 for those having electric stoves. Personal exposures were generally underpredicted by the TWA estimates, especially for the working participants, as shown by the group geometric means in Table V. Individual and household characteristics were evaluated separately by using a two-stage, stepwise multiple regression analysis (Table VI). Significant personal characteristics during the summer phase were (a) full-time worker (vs. student or part-time), (b) commuting distance to and from work each day, (c) sex is female, and (d) working with or near gas furnaces, boilers, ovens, or flames. In the winter phase, significant predictors included (a) working with or near welding or cutting torches (arc or flame) and (b) the individual's age. Household characteristics were evaluated by using the adjusted average personal exposures for each home as the response variable. The presence of a stove pilot light, outdoor NO2 concentration, and having a gas clothes dryer were significant predictors for the summer data set, explaining 45% of the

Table VI. Summary of Two-Stage Stepwise Regression Analysis phase

stage 1 (individual characteristics)

stage 2 (household characteristics)

R2,%

summer

full-time worker distance to work/school female gas furnace, boiler, oven, or flames at work welding, gas space heaters, or gasoline engines at work age of participant

pilot light outdoor NOz gas clothes dryer

45

gas stove

67

winter

Table VII. Comparison of Types of Measurements Used in Regression Models phase

types of measurements

R2,%

RMS

geometric SE

summer

(1)outdoor NOz and geog. classification (2) indoor NO2 (3) home characteristics (1) outdoor NOz and geog. classification (2) indoor NO2 (3) home characteristics

11.7 67.3 44.5 9.0 82.9 67.1

0.11

0.041 0.070 0.36 0.068 0.13

1.39 1.23 1.30 1.82 1.30 1.43

winter

variation in the adjusted household averages for the logtransformed personal exposures (RMS = 0.07, GSE = 1.30). During the winter, the use of gas as the cooking fuel was the significant home-level predictor, with R2 = 67.1 % (RMS = 0.13, GSE = 1.43). To compare the ability of distinct types of measurements to represent personal exposures, two additional models were fit. The first assumes that only outdoor NO2 concentrations and simple geographic classifications (urban vs. rural) are available; the second assumes that indoor averages for kitchen and nonkitchen zones and overall indoor average NO2 concentrations are available. A comparison between these models and that discussed previously is given in Table VII. For the summer data, the first model had an R2 of only 11.7%, compared with an R2 of 67.3% for the second model and with an R2 of 44.5% for the model based on home characteristics. Measurements of NO2 outside the home gave an R2 = 9.0% during the winter, compared with R2 = 82.9% for the model using indoor NO2 measurements and with R2 = 67.1% for the home characteristics model, The coefficient of variation (CV) for 93 replicate pairs of Palmes’ tubes was 4.52% for the summer phase. Absolute differences were less than 5 pg/m3 in 98% of these pairs, with a precision estimate (square root of 112 the variance of the difference scores) of 1.0 pg/m3. For the winter monitoring phase the CV was 4.99%, and the precision estimate was 1.32 pg/m3 for 81 replicate pairs. Absolute differences were less than 5 pg/m3 in 95% of these. Interlaboratory comparisons gave a difference between means for the Wisconsin and Harvard laboratories of 1.16 pg/m3 or 3.3% (SD of difference scores, 1.6 pg/m3) for summer and 3.29 pg/m3 or 6.51% (SD of difference scores, 3.56 pg/m3) for the winter phase, indicating generally good agreement between these labs. Discuss ion The relationships between mean indoor and outdoor NO2 concentrations observed in this study compare favorably with several previous investigations in demonstrating that there is a close association between the use of gas for cooking and elevated levels of NO2 inside of homes and that the magnitude of departure from outdoor concentrations is greatest during the winter when both intentional ventilation and unintentional infiltration rates may be reduced, and stove use is likely to be increased (13-16,18,51). Conversely, average indoor NO2 concentrations in electric stove homes were below those measured outdoors. This may be due to the chemically reactive

nature of NO2 (3). This difference was also increased in during the winter, suggesting that for NOz being inside these homes may be protective against exposure to outdoor levels, Given the proportion of time spent at home by our study population, the levels of NO2 they are exposed to there contributes the major time-concentration component to their total personal exposures. This impact is most apparent both in the mean comparisons and in the correlations between indoor and personal NO2exposures for the gas stove households. By comparison, outdoor measurements are a poor estimator of personal exposures, especially during the winter when more time was spent indoors and the levels of NO2 found there differ the most from those found outdoors. These results have several consequences for epidemiological studies of the health effects of air pollutants, from both outdoor and indoor sources. By use of only the outdoor component of exposure, several key potentially confounding variables are omitted from consideration. These have been identified in our regression analyses for personal characteristics such as occupational status (worker, nonworker, student) and exposures (welding, gas-fired equipment) and such as age and sex. For household characteristics, indoor NO2 concentrations were closely associated with individual exposure. The variability in these concentration measurements could only be partially represented by source (gas stove, pilot light, gas dryer) and ventilation-related characteristics of the home as related to personal exposures. Errors in the determination of the independent variable, exposure, may have especially serious consequences for the ability of health studies to derive an exposureresponse relationship, since these errors may bias exposure estimates to falsely imply the presence or absence of an effect. Misclassification of relative exposure levels of population members in an epidemiological study is a potential consequence of failure to take painstaking efforts to assess personal exposures (52). Thus, members of a group under study could be classified as exposed to “high” or “low” levels of an air pollutant based on an outdoor monitoring station reading, whereas certain members of the group are actually exposed to much higher or lower levels relative to the group because of indoor or other unmeasured sources. Thus, comparisons of health effects between these “high” and “low” exposure groups may be biased due to substantial misclassification within one or both of the groups. In many cases, errors in exposure assessment in air pollution and other environmental epidemiology studies result in a bias toward no observed effect ( 5 3 , 5 4 ) . HowEnviron. Sci. Technol., Vol. 20, No. 8 , 1986

781

ever, the potential exists for the direction of the bias resulting from exposure misclassification to be toward creating a spurious effect as well as to mask real differences between the groups (52). The potential for exposure misclassification must be kept in mind in assessing studies attempting to demonstrate health effects associated with the use of gas as opposed to electric stoves. Although the group means for personal NOz exposure diverge, this study demonstrates that there is a considerable overlap between these two groups. This overlap might be attributable to variability in exposures experienced in other locations, which in turn is related to the individual's own pattern of activities. Such misclassification of individual exposures will reduce the sensitivity of these studies. It also may explain some of the inconsistencies in their results and of suggested modification of health impacts over time. Biological changes in children as they grow older may make them less susceptible to environmental challenges, but social changes may modify their exposures by altering their mobility patterns. As less time is spent a t home and more is allocated to school and other locations, this will alter the individual's total integrated exposure even though he or she remains classified as having a gas or electric stove. Acknowledgments

The interest, cooperation, and hospitality of the participating families are greatly appreciated, as is the assistance given by the staff of the Pardeeville, Portage, and Rio school systems. We acknowledge the efforts of Mark Lindsay for his assistance with monitoring and data processing, of Mari Palta for her suggestions for data analysis, and Gary Hoffman at the Wisconsin State Laboratory of Hygiene for his assistance in the preparation and analysis of the monitors. Registry No. NO2, 10102-44-0. Literature Cited National Academy of Sciences Nitrogen Oxides;National Academy of Sciences: Washington, DC, 1977. National Academy of Sciences Indoor Pollutants;National Academy of Sciences: Washington, DC, 1981. Yocom, J. E. J.Air Pollut. Control Assoc. 1982,32,500-520. Fugas, M. In Proceedings of the International Conference on Environmental Sensing and Assessment; Las Vegas, NV, Sept, 1975; IEEE 75-CH1004-1 ICESA, Institute of Electrical and Electronics Engineers Inc.: New York, NY, 1976; Vol. 2, paper 38-5. Morgan, M. G.;Morris, S. C. J.Air Pollut. Control Assoc. 1977,27,670-671. Sexton, K.; Letz, R.; Spengler, J. D. Environ. Res. 1983, 32, 151-166. Lebowitz, M. D. Annu. Rev. Public Health 1983,4,203-221. National Academy of Sciences Energy in Transition 1985-2010; W. H. Freeman: San Francisco, CA. 1979. U.S. Environmental Protection Agency Air Monitoring Strategy for State Implementation Plans; U S . Environmental Protection Agency. Office of Air Quality Planning and Standards: Research Triangle Park, NC, 1977; EPA 450/2-77-010. Epidemiology of Respiratory Diseases Task Force Report, July 1979;U.S.Department of Health and Human Services. U.S. Government Printing Office: Washington, DC, 1980; NIH Publication No. 81-2019, Chapter 111. Young, G. S.; Hagopian, J. H.; Hoyle, E. R. Potential Health Effects of Residential Energy Conservation Measures; Arthur D. Little, Inc.: Cambridge, MA, 1981; GRI-7970097. Hauser, T. R.; Scott, D. R.; Midgett, M. R. Environ. Sei. Technol. 1983,17, 86A-96A. Wade, W. A,, 111; Cote, W. A.; Yocom, J. E. J. Air Pollut. Control Assoc. 1976,25,933-939. Environ. Sci. Technol., Vol. 20, No. 8, 1986

(14) Palmes, E. D.; Tomczyk, C.; DiMattio, J. Atoms. Environ. 1977,ll , 869-872. (15) Melia, R. IT. W.; Florey, C. du V.; Darby, S. C.; Palmes, E. D.; Goldstein, B. D. Atmos. Environ. 1978,12,1379-1381. (16) Goldstein, B. D.; Melia, R. J. W.; Chinn, S.; Florey, C. du V.; Clark, D.; John, H. H. Znt. J . Epidemiol. 1979, 8, 339-345. (17) Keller, M. D.; Lanese, R. R.; Mitchell, R. I.; Cote, R. W. Environ. Res. 1979, 19, 495-515. (18) Spengler, J. D.; Duffy, C. P.; Letz, R.; Tibbitts, T. W.; Ferris, B. G. Environ. Sci. Technol. 1983, 17, 164-168. (19) Szalai, A., Ed. The Use of Time: Daily Activities of Urban and Suburban Populations in Twelve Countries;Mouton Publishers: The Hague, Netherlands, 1972. (20) Moschandreas, D. J.; Zabransky, J.; Pelton, D. J. Comparison of Indoor and Outdoor Air Quality;Electric Power Research Institute: Palo Alto, CA, 1981; EPRI EA-1733. (21) Ott, W. R. "Models of Human Exposure to Air Pollution"; Technical Report 32, SIAM Institute for Mathematics and Society, Stanford University: Standford, CA, 1980. (22) Dcckery, D. W.; Spengler, J. D. J.Air Pollut. Control Assoc. 1981, 31, 153-159. (23) Moschandreas, D. J. Bull, N.Y. Acad. Med. 1981, 57, 845-859. (24) Moschandreas, D. J.; Zabransky, J.; Pelton, D. J. Comparison of Indoor-Outdoor Concentrations of Atmospheric Pollutants. Final Report for the Electric Power Research Znsitute; GEOMET Inc.: Gaithersburg, MD, 1980. (25) Daun, N. Micro-environment Types: A Model for Human Exposure to Air Pollution;Rand Corp.: Santa Monica, CA, 1981; Working Draft, WD-908-1-HHS. (26) Duan, N. Models for Human Exposure to Air Pollution; Rand Corp.: Santa Monica, CA, 1982; N-1884-HHS/RC. (27) Palmes, E. D.; Gunnison, A. F.; DiMattio, J.; Tomczyk, C. Am. Znd. Hyg. Assoc. J. 1976, 37, 570-577. (28) Morris, S. C . Environ. Int. 1981, 5, 69-72. (29) Silverman, F.; Corey, P.; Mintz, S.; Olver, P.; Hosein, R. Environ. Znt. 1982, 8, 311-316. (30) Dockery, D. W.; Spengler, J. D.; Reed, M. P.; Ware, J. Environ. Znt. 1981, 5, 101-107. (31) Quackenboss, J. J.;Kanarek, M. S.; Spengler, J. D.; Letz, R. Environ. Znt. 1982, 8, 249-258. (32) Nitta, H.; Maeda, K. Environ. Znt. 1982,8, 243-248. (33) Ferris, B. G.; Speizer, F. E.; Spengler, J. D.; Dockery, D. W.; Bishop, Y. M. M.; Wolfson, M.; Humble, C. Am. Rev. Respir. Dis. 1979, 120, 767-779. (34) Kish, L. Survey Sampling; Wiley: New York, NY, 1965. (35) Palmes, E. D. Environ. Znt. 1981, 5, 97-100. (36) Palmes, E. D.; Tomczyk, C.; March, A. W. J. Air. Pollut. Control Assoc. 1979,29, 392-393. (37) Melia, R. J. W.; Florey, C. du V.; Morris, R. W.; Goldstein, B. D.; Clark, D.; John, H. H. Znt. J. Epidemiol. 1982,11, 155-163. (38) Wolfson, M. J. Modificatiom to the Palmes Diffusion Tube Preparation and Analysis Methods; Harvard Six City Study Quality Assurance Document. Harvard School of Public Health Boston, MA, 1980; Vol. iI. (39) Apling, A. J.; Stevenson, K. J.; Goldstein, B. D.; Melia, R. J.; Atkins, D. H. F. Air Pollution in Homes: Validation of Diffusion Tube Measurements of NO,; Warren Spring Laboratory: Herfordshire, England, 1979; Report LR311 (AI'). (40) Cadof, B. C.; Knox, S. F.; Hodgeson, J. A. Personal Exposure Samplers for NOz. Draft Report; National Bureau of Standards: Washington, DC, 1979. (41) Gordis, L. Am. J. Epidemiol. 1979, 109, 21-22. (42) Chapin, F. S. Human Activity Patterns in the City; Wiley-Interscience: New York, NY, 1974. (43) Neter, J.; Wasserman, W. Applied Linear Statistical Models; Richard D. Irwin, Inc.: Homewood, IL, 1974. (44) Snedecor, G. W.; Cochran, W. G. Statistical Methods, 7th ed.; Iowa State University Press: Ames, IA, 1980. (45) Rosner, B.; Donner, A.; Hennekens, C. H. Biometrics 1979, 35, 461-471. (46) Draper, N. R.; Smith, H. Applied Regression Analysis, 2nd ed.; Wiley: New York, NY, 1981.

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Weisberg, S. Applied Linear Regression; Wiley: New York, NY, 1980. Donner, A. Am. Stat. 1982, 36, 378-381. Dixon, W. J.;' Ed. BMDP Statistical Software 1981; University of California Press: Berkeley, CA, 1981. Daniel, C.; Wood, F. S. Fitting Equations to Data, 2nd ed.; Wiley: New York, NY, 1980. Cote, W. A.; Wade, W. A.; Yocom, J. E. A Study of Indoor Air Quality. Final Report; U.S. Environmental Protection Agency. U.S. Government Printing Office: Washington, DC, 1974; EPA-65014-74-04. Shy, C. M.; Kleinbaum, D. G.; Morgenstern, H. Bull. N.Y. Acad. Med. 1978,54, 1155-1165. Gladen, B.; Rogan, W. J. Am. J . Epidemiol. 1979, 109,

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Received for review June 4, 1985. Revised manuscript received January 13,1986. Accepted February 18,1986. This study was supported primarily by a grant from Wisconsin Power and Light Co., Madison Gas and Electric Co., and Wisconsin Public Service Corp. and through cooperative agreement CR 808536010 between the Environmental Monitoring Systems Laboratory (EMSL)of the US.EPA and the Harvard School of Public Health. General support for this study was provided by National Institute of Environmental Health Science Grant E501108 and Electric Power Research Institute Contract RP 1001.

Measured and Calculated Evaporation Losses of Two Petroleum Hydrocarbon Herbicide Mixtures under Laboratory and Field Conditions James E. Woodrow, James N. Selber," and Yong-Hwa Kim Department of Environmental Toxicology, University of California, Davis, California 956 16

Evaporation rates of two weed oils were measured under laboratory and field conditions. Rates were also calculated by assuming first-order evaporation of the oil components (represented by hydrocarbon references). Beacon selective and Chevron nonselective weed oils exhibited evaporation rates 1.4-1.9 and 0.9 times the calculated rates, respectively, for 8-10 mg/cm2 on inert surfaces in the laboratory. The relative rates were increased to 3-15 (Beacon) and 1.6 (Chevron)under a slight breeze (0.43 m/s) with turbulence. The half-life of Beacon oil applied at 6-7 mg/cm2 to moist soil in an unplanted field was 51 min (10-20 "C), while the calculated half-life was 57 min. In an alfalfa field, 90% of the Chevron oiI from a deposit of 0.15-0.22 mg/cm2 (20-40 "C) evaporated in 26-45 and 53-127 min from glass plates and paper filters, respectively; average calculated time was 40 min. Evaporation rates from alfalfa foliage and glass plates compared well.

Introduction Vapors of nonsynthetic petroleum hydrocarbon mixtures used as pesticides may enter the atmosphere during spraying and by postapplication evaporation from soil and plant surfaces. Their extensive use in California as dormant sprays, harvest aids, herbicides, and insecticides, the level of use (average of 5.3 X lo6 kg/year for 1981-1983) (I), and vapor pressures commonly greater than mmHg a t ambient temperatures suggest that these oil mixtures may contribute significantly to the hydrocarbon emissions, and ultimately to air pollution by ozone (2, 3), in some agricultural areas of California. This report summarizes the results of a study to determine the rate of evaporation of Beacon oil, a typical selective weed oil, and Chevron oil, a typical nonselective weed oil, under normal use conditions. Beacon oil is commonly applied to fields containing young carrots (three frond stage) for weed control, while the Chevron oil is a broad-spectrum herbicide commonly applied to alfalfa as a seed harvest aid. Beacon oil consists primarily of aliphatics (C9-CI2),and Chevron oil contains predominately alkyl-substituted benzene and naphthalene derivatives, along with some aliphatics (C9-CI9). Laboratory and field measurements of relative volatility of these oils from inert, soil, and foliage substrates were made. Air sampling and 0013-936X/86/0920-0783$01.50/0

analysis techniques were developed for determining oil concentration in air, and these techniques were used to obtain representative air samples in and near agricultural treatment sites. Included in this study is a model, derived from flux measurements of pure compounds, describing evaporation of the oil mixtures; evaporation rates were calculated for comparison with measured laboratory and field rates.

Experimental Section Laboratory. Beacon selective weed oil no. 5 (Beacon Oil Co., Hanford, CA) was applied to Teflon and soil surfaces by spraying at field rates (470-930 L/ha) with droplet diameters of 150 pm volume mean diameter (VMD) generated by a glass TLC sprayer. Droplet diameter was determined by using a Particle Size Measuring System, Inc., Model ll-C laser sizing device. Immediately after being sprayed, the treated surfaces were placed in a 4-cm diameter Teflon cylinder (SAVILLEX Corp., Minnetonka, MI). Air, prefiltered through charcoal, was allowed to flow over the surface for 1h at 1L/min (0.013 m/s), and the samples were extracted with solvent for analysis by gas chromatography; temperature of the laboratory air was determined by using a thermograph. The Beacon oil and nonselective Chevron weed oil (Chevron, Richmond, CA) were applied to Teflon and polyethylene surfaces (8.8-10.4 mg/cm2) using a glass pipet. Samples were removed for extraction with solvent and gas chromatography at time intervals from 0.5 to 7.4 h postapplication under static wind conditions. Also, polyethylene surfaces treated separately with Beacon and Chevron Oil (8.8-10.4 mg/cm2) were subjected to an air flow of 40 L/min (0.43 m/s) and solvent-extracted at intervals from 0.5 to 7.3 h posttreatment. Field 1: Beacon Oil. A square plot, consisting of mostly bare soil with a few ground-hugging weeds, 9.14 m on a side (area = 83.5 m2) located at the University of California, Davis, was divided into four subplots. Into each subplot was placed a glass plate covered with 14 microscope slides (19.0 cm2 each). A fifth set of glass slides was placed at the center of the plot (Figure 1). Using a flour sifter, finely ground Yo10 silt loam soil was dusted over each set of slides, followed by a light application of distilled

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