Environ. Sei. Technol. 1985, 19, 700-707
Personal Exposures to Respirable Particulates and Implications for Air Pollution Epidemiology John D. Spengler,* Robert D. Treltman, Tor D. Tosteson, David 1. Mage,+and Mary Lou Soczek
Department of Environmental Science and Physiology, Harvard University School of Public Health, Boston, Massachusetts 021 15
Measurements of personal exposures to respirable particles (RSP) were obtained from nonsmoking adults living in two rural Tennessee communities. Personal exposure measurements were compared to simultaneously collected indoor (home) and outdoor concentrations. Personal exposures were higher than, had a greater variance than, and were uncorrelated with outdoor concentrations. Household smoking was found to be a substantial contributor to personal RSP exposure. Regressions of indoor concentrations on paired personal exposures explained 1147% of the variance in exposure depending on employment subgroup and household smoking. A deterministic, predictive model based on the time spent in four microenvironments and measured concentrations explained 64% of the variance in personal exposure. Ambient concentrations provide poor prediction of personal exposures to undifferentiated respirable size particles. Air pollution epidemiological investigations must consider the importance of indoor environments in estimating subject exposures. Further, the chemical/elemental compositions of indoor concentrations and personal exposures are likely to be different from ambient concentrations. This study indicates the potential for misclassification and misassociation of exposures that are likely to result in relying upon ambient, community-based particle measurements.
Introduction The extent to which outdoor air-quality meausurements reflect actual exposure levels has only recently been investigated (1,2). Given the wide variation in indoor environments and the fact that Americans typically spend an estimated 60-90% of their day indoors (3-6), the relationships among indoor, outdoor, and personal concentrations of contaminants need to be examined, especially when trying to link exposure data with epidemiologic health-effects studies. As part of the Harvard Air Pollution Health Study, a multiyear prospective epidemiological project measuring the respiratory health effects of air pollution (7), we have attempted to quantify personal exposures to respirable particulate matter and gases (8-11). This paper presents the results of a monitoring study designed to add to our knowledge concerning 24-h average exposures to respirable-suspended particulates in a diverse population. In large epidemiological air pollution studies involving several thousand subjects, it is essential to specify exposures, but direct measurements for each participant are impossible. Nevertheless, pollutant concentrations encountered at home, in transit, at work, or elsewhere and the variances in these concentrations can result in substantial misclassification of subject exposure. Shy et al. (12) and others (13,14) examined the effects of exposure misclassification on relative risk calculations and found On assignment from Environmental Monitoring Systems Laboratory, U.S.Environmental Protection Agency, Research Triangle Park, NC 27711. 700 Envlron. Scl. Technol., Vol. 19, No. 8, 1985
that, depending on the extent of misclassification, estimates of relative risk can change substantially. Therefore, improvement in exposure estimations, especially for pollutants with both indoor and outdoor sources, is essential. In the Harvard Study, respirable-suspended particulate (RSP) concentrations have been shown to have greater variability among indoor environments than among outdoor sites within the community. Furthermore, passive exposure to tobacco smoking, at home and at work, has been found to be the most important contributor to high personal RSP levels (11). The question that large epidemiologic studies must now address is the degree to which descriptor variables or additional fixed outdoor and indoor measurements can improve specifications of personal exposures.
Methodology Location. Kingston and Harriman, two towns in Rome County, TN, approximately 65 km west of Knoxville, were selected for this study. The Harvard Study had been in operation in the area since 1975; fixed-site monitors and a population base from which to solicit volunteers were readily available. The towns are in adjacent river valleys, separated by a ridge approximately 75 m high. A large, 1500-MW coalfired electric generating plant, operated by the Tennessee Valley Authority (TVA), is located in Kingston. All emissions are discharged through two 300-m stacks. This steam plant is the only significant industry in Kingston, which is a predominantly residential community of 4000 people. Harriman, with 8700 residents, is semiindustrial, with two hosiery mills, a scrap metal handling facility, and a paperboard factory within the town limits. In the valley, approximately 8 km from the downtown area, are an iron-forging plant and an electric-arc furnace. In addition, much of the coal used at the Kingston Steam Plant is brought in from strip-coal mines in large diesel trucks that are routed through downtown Harriman. The major employers in Roane County are the Oak Ridge National Laboratory/Union Carbide complex, the TVA, and the factories and mills in Harriman. The annual particulate emissions including total suspended particulate (TSP) area source emissions from industrial, commercial, and domestic sectors in Roane County are estimated at 5130 tons (15). In the vicinity of Kingston and Harriman, the single largest particulate emission source is the TVA Kingston Steam Plant, although the emissions are discharged from tall stacks and are less likely to impact the valley air quality than emissions from ground-level sources. On the basis of extrapolations from the Miller et al. (16) analysis of Roane County emissions data, emissions from low-level sources are actually greater in Harriman than in Kingston, 116 tons kn+!year1 in Harriman to 3 tons km2year-l in Kingston. Fugitive dust alone accounts for 33.5%. Motor vehicles account for 27.5%, while all other transportation sources account for another 27.5%. Fuel combustion and solid
0013-936X/85/0919-0700$01.50/0
0 1985 American Chemical Society
waste disposal represent 6.3% and 2.9% of the annual emissions, respectively. Emissions from sources outside the county may also have an influence on the level of suspended particulates in the Kingston-Harriman area. For example, analysis of particulate point sources within a 50-km radius of the Kingston-Harriman area revealed several point sources, including two major power plants: the TVA-operated Bull Run facility with emissions of 207 tons year1 and the Watts Bar Steam Plant with 121 tons year-l (15). Sample Design. The personal exposures study was designed around several objectives. One of these was to explore the practicality of personal monitoring as a means for assessing population exposures, ultimately testing the improvements in exposure prediction models using successively more detailed and difficult to obtain information on ambient levels, personal and home characteristics, and indoor RSP concentrations. Accordingly, the sample design received considerable attention, and a number of innovative procedures were introduced. However, the conduct of the study ultimately represented a compromise between statistical efficiency and logistical considerations. In order to simplify the design process, two practical study objectives were established. The first was to measure and compare exposures among individuals categorized by town of residence (Kingston or Harriman), by passive tobacco smoke exposure, and by occupational status (office worker, blue collar worker, and nonemployed). The second objective was to use these exposure data to test prediction models employing descriptive information as well as indoor and outdoor concentrations from stationary monitors. Therefore, information on the volunteers’ time-activity patterns, employment, and residences was collected. Two objectives guided the solicitation of volunteers and the scheduling of personal monitoring. Because comparisons were planned between specific population groups, an attempt was made to have equal numbers in the corresponding sample groups. Within each group, individuals were to be selected randomly to assure the representativeness of the subsamples. To characterize the variability of exposures within the target population, a design to increase the number of participants rather than the number of samples per individual was adopted. On Dec 15,1980, letters were sent to the 733 nonsmoking health study adults requesting their participation in the personal monitoring project. Because of a low response rate (30%), initial nonresponders were recruited by phone in early Feb 1981. The original sample design called for three 24-h samples from 120 people, with 10 in each of the six comparison groups in each town. To control for meteorological conditions and weekly activity patterns in the planned comparisons, a representative of each exposure group was to be studied on every sampling day. Five 6-day periods were scheduled with 2 days separating adjacent periods. Two subjects were to be randomly assigned from each group to each of the five periods, with one of them arbitrarily selected to begin an alternate day schedule within the period. For a variety of reasons, modifications to the original plan were necessary during field operations. In particular, difficulties were encountered in recruiting individuals in the blue collar occupational category, both because of the comparatively small size of this group and because of the possibility of the monitoring device interfering with job activities. In addition, the random scheduling of monitoring sessions caused many conflicts. Eventually, a decision was made to reduce the total sample size and to augment the deficient categories with individuals from
Table I. Distribution of Study Sample by Original Exposure Categoriesa
smoke exposureb Kingston Harriman other - yes no yes no yes no
group employment office nonoffice other nonemployed total
6 3 0 8 17
1
7 1 2 16 36
4 0 1 2 7
9 1 5 16 31
1 1 0 2 4
1 1 1 3 6
a Includes four field personnel and one smoking volunteer. “Yes” indicates cigarettes smoker(s) at home.
more readily available groups. Personal samples were taken on 38 days over a 46-day period beginning Feb 19, 1981. A total of 97 volunteers and four field personnel (community residents) participated in the project. The final numbers of subjects in the original exposure categories illustrated the changes made to the original design during field operations (Table I). Although in most cases the initial goals were not met, the design process did provide a certain structure to the final data set and valuable information was obtained on the feasibility of scientific sampling in personal monitoring studies. Equipment and Sampling Procedures. The Harvard/EPRI sampling system, a small, portable lightweight unit specifically designed to collect respirable particulate matter, was used (17). Millipore’s Fluoropore filter (1-pm pore diameter), composed of polytetrafluoroethylene on a polyethylene backing, has a low affinity for water vapor and a low pressure drop (less than 7.5 cm of H 2 0 across a 37-mm filter at 1.7 L of air/min). The 10-mm nylon cyclone preseparator passes 50% of the 3.5-pm aerodynamic diameter particles and none of the 10-pm diameter particles. All filters were initially weighed on a Cahn 21 Electro-Balance and then reweighed after 24 h of equilibration in a temperature- and humidity-controlled laboratory at the Harvard School of Public Health. Sampling and weighing procedures are described in our U.S.EPA approved Quality Assurance Project Plan (18). Personal and ambient monitoring equipment was independently audited by Research Triangle Institute (19). Laboratory equipment, procedures, and data management were system audited by Research Triangle Institute under U.S.EPA contract (20). Each participant was assigned two monitors: an indoor monitor that was located in an open area of a frequently used room on the first floor in the home and a personal monitor that was to be kept with the individual during each 24-h period. The participants kept Activity Diaries for each sampling day, logging times, location types (home, work, public places, travel, or other), the presence of smokers, and whether they were inside or outside. In each of the towns, the centrally located ambient monitoring stations operated by the Harvard Study were equipped to characterize the community ambient RSP, TSP, and size-selective particle concentrations. In Harriman, a trailer equipped with one automatic Beckman dichotomous sampler, four high volume (hivol) samplers (one of which was fitted with a size-selective inlet), four Harvard/EPRI samplers, and four pulsation-free cyclone respirable-suspendedparticulate samplers was situated two blocks off the main street. The trailer at the Kingston site, located 800 m north of the commercial district, had one manual dichotomous sampler, one high volume sampler, and two of each type of respirable mass monitors. The Environ. Sci. Technol., Vol. 19, No. 8 , 1985 701
Table 11. Quantile Descriptors of Personal, Indoor, and Outdoor RSP Concentrations, by Location
RSP sample group N
city Kingston
personal indoor outdoor personal indoor outdoor personal indoor outdoor
Harriman total"
RSP quantile, ~ g / m ~ 75% 50 % 25 %
95 %
133 138 40 93 106
99 110 28 122 129 34 113 119 33
21
249 266 71
47 47 22 54 45 23 48 46 23
34 31 16 35 27 15 34 29 17
26 20 12 24 18 13 26 20 13
5%
mean
SE
19 10 6 15 10 9 17
42 42 17 47 42 18 44 42 18
2.5 3.5 2.7 4.8 4.1 4.0 2.8 2.6
10
7
2.1
"Includes samples from 13 subjects living outside of Kingston and Harriman town limits and from four field personnel. Table 111. Personal and Indoor RSP Means, by Smoke Exposure and Employment (in pg/ma)
group total personal indoor nonemployed personal indoor all employed personal indoor office personal indoor nonoffice personal indoor other personal indoor
nonsmoke exvosed
smoke-exvosed N SE
N
8
SE
71 80
64 74
5.5 6.6
178 186
36 28
1.6 1.1
29 35
66 86
7.2 11.3
85 86
32 30
1.7 2.0
42 45
63 65
7.9 7.6
93 100
39 26
2.5 1.2
27 31
72
67
11.6 9.8
70 80
39 24
3.0 1.2
10 9
37 50
3.6 5.7
8 6
35 23
2.4 3.6
5 5
63 83
15.0 30.8
15 14
44 35
7.4 4.0
x
dichotomous and size-selective hivol samplers had 15-pm separating inlet heads. The dichotomous virtual impactors separated fine and coarse particles at 2.5-pm diameter. Samplers were operated daily at both sites, providing collocated comparison data for the Harvard/EPRI samplers.
Results The averages of the personal monitoring data obtained in the study are presented in Table 11. It is shown clearly that both towns had similar 24-h outdoor (ambient) RSP concentrations during the period of this study. Harriman averaged RSP concentrations only 1 pg/m3 higher than Kingston. The personal and indoor distributions did not differ between the two towns. The results of two statistical tests (the linear analysis of variance and the Wilcoxin two-sample rank sum) justify the pooling of samples for subsequent analysis. In both towns, the averages of the personal and indoor concentrations of RSP were approximately 25 pg/m3 higher than the outdoor RSP concentrations, suggesting the presence of significant indoor sources. Approximately 75% of the indoor samples and 95% of the personal samples were above the mean outdoor average of 18 pg/m3. This has important implications for epidemiological studies, as will be discussed later in this paper. Differences by Smoke Exposure and Employment Status. The personal exposure and indoor concentration data are presented by smoke exposure and employment status in Table 111. In households with at least one smoker, the mean indoor concentrations as well as the mean personal exposures are higher than those from the 702
Environ. Sci. Technol., Vol. 19, No. 8, 1985
Table IV. Mean Differences between Indoor and Outdoor RSP Concentrations by Smoke Exposure and Employment
group smoke exposed nonemployed employed nonsmoke exposed nonemployed emvloved
paired t test
mean difference (1- O), rg/m3
t
P
31 34
6.68 3.10
0.0003 0.006
7 13
2.82 3.14
0.01 0.005
nonsmoking households. In the nonsmoking households, the personal RSP exposure is always higher than the home concentration. This implies that these individuals encounter elevated concentrations away from home, and/or that home concentrations are elevated while they are at home and reduced while they are away. Comparing all employed with the nonemployed, the indoor means were higher in the nonemployed groups. Within the smoke-exposed group, the personal mean of the nonemployed subgroup was somewhat higher than that of the employed subgroup. In breakdowns within the nonsmoke-exposed group, the opposite is the case. To test for significance of differences in mean concentrations between or among test groups, a general linear model procedure for analysis of variance was applied. In tests for differences with two classification variables, in most cases a one-way analysis of variance was applied separately to each major classification group. The differences in personal exposures and indoor concentrations for both smoke exposure groups are significant at the p = 0.0001 level. No significant differences in personal exposures between employed and nonemployed was found for the smoke-exposed group; weak evidence for a difference by employment was found for the nonsmoke-exposed group (p = 0.02). Indoor-Outdoor Differences. The differences between indoor and outdoor concentrations are shown in Table IV to be statistically related to the presence of smoking. For the nonemployed subjects living in nonsmoking homes, the average RSP concentration in the indoor (home) environment was only 13 pg/m3 above that in the outdoor environment. For the nonsmoke-exposedemployed subjects, the increase was approximately 7 pg/m3 above the outdoor average. However, for the smoke-exposed groups, the indoor home concentration was over 30 pg/m3 above the outdoor average. Personal-Indoor Differences. The data were additionally analyzed for significant differences between indoor and personal RSP concentrations by smoke exposure and employment, as summarized in Table V. The exposures of subjects from nonsmoking homes were consistently and significantly higher than their home (indoor) concentra-
in Table VII. As expected, more time was spent at home on the weekends than on the weekdays. The employed group spent more time at work, traveling, and outside than did the nonemployed group. Predicting Personal Exposures. Until recently air pollution epidemiology has relied on ambient air monitoring and descriptor variables to characterize exposures of subjects. Often, data from a central outdoor station are used for all subjects living within a defined geographic area. The marginal value of successively more detailed and costly information about concentrations, housing, and demographic characteristics, among others, in characterizing personal exposures was assessed with these data (21). Regression models were evaluated with two statistics: the square of the multiple correlation coefficient (R2), which indicates the portion of variance explained in the dependent variable, and the root mean square error (RMSE), which expresses model uncertainty in units of the dependent variable. Predicted personal RSP exposures were regressed vs. measured personal RSP concentrations. Analyses of simple regressions of ambient and indoor concentrations on personal exposures were first undertaken. (Analyses presented are for individual data points. All models were tested on individual data, person-aggregated data, and log-transformed values; as no substantial differences in the estimated equations were found, we report only the results of regressions on individual data.) Regressions were performed on the total sample and for subgroups defined by smoke exposure and employment. Results are summarized in Table VIII. Ambient measurements alone provided poor prediction. In the sample as a whole, less than 1%of the variance in personal exposure was explained by the outdoor respirable particulate measure. Regressions for each of the sample subgroups showed that the ambient measure could not adequately explain personal exposure variance for any group better than for another. In comparison, the regressions of the more highly correlated personal and indoor concentrations indicate that, for the total population, 50% of the variance in personal
Table V. Mean Difference between Personal and Indoor RSP Concentrations by Smoke Exposure and Employment
group
paired t test
mean difference (P- pg/m3
t
-22 1
-2.08 0.40
3 14
5.50
n,
smoke exposed nonemployed employed nonsmoke exposed nonemployed employed
P 0.04 0.7
0.005 0.0001
2.87
tions (2 pg/m3 for the nonemployed; 14 pg/m3 for the employed). Conversely, nonemployed subjects from smoking homes had home concentrations significantly higher (22 pg/m3) than their personal exposures. The difference between the personal and indoor concentrations of the smoke-exposed employed group (1pg/m3) was not statistically significant. Paired t tests on the differences between the personal and indoor exposures for all groups confirmed these results. Personal, Indoor, and Ambient Measurements. Daily concentrations measured by the RSP sampling system at each central station were averaged and then matched by town to the personal exposure data. The correlations of the central site ambient RSP data with the personal and indoor RSP measures for the sample as a whole and as broken down by the smoke exposure and employment groups are shown in Table VI. All personal-ambient and indoor-ambient correlations are low and are not statistically significant. The personal-indoor correlations, also shown, however, are strong and statistically significant, most at the p = 0.0001 level. Activity Patterns. The overall daily time allocation patterns of our subjects were derived from their Activity Diaries. This information was then analyzed to determine if significant differences between the subjects existed that might help explain the variability in personal RSP exposure. Differences in time allocation were observed by employment group for weekdays and weekends, as shown
Table VI. Correlations of Personal RSP Exposures with Central Site Ambient and Indoor RSP Concentrations personal-ambient
personal-indoor r
group
N
r
P
N
total smoke exposed nonsmoke exposed employed nonemployed smoke exposed employed smoke exposed, nonemployed nonsmoke exposed, employed nonsmoke exposed, nonemployed
225 62 163 123 102 34 28 89 74
0.07 0.16 0.15 0.19 -0.15 0.28 -0.06 0.21 0.01
0.30 0.22 0.06 0.03 0.13 0.10 0.76 0.04 0.91
258 75 183 141 117 42 33 99 84
0.70 0.69 0.49 0.80 0.70 0.93 0.48 0.33 0.91
P 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.005 0.001 0.0001
Table VII. Time-Allocation Summary: Percentages of Day in Basic Locationsa weekdays
location home work public travel other all inside all outside (I
nonemployed (N = 81) x SD
weekends
x
employed ( N = 98) SD
x
SD
x
55 27 3 6 3
22.1 19.7 5.6 7.4 8.4
89 0 3 2 2
15.0 0.0 5.1 3.6 4.9
72
2
15.1 3.9 6.6 4.0 5.2
2
23.1 12.2 6.4 6.7 4.4
89 8
14.6 8.6
84 10
20.7 11.3
90 6
12.6 7.9
80 9
22.2 10.7
88 1 4 3
nonemployed ( N = 32)
5 5 5
employed ( N = 32) SD
Owing to small amounts of unaccounted for time, column percentages do not add to 100%. Environ. Sci. Technol., Vol. 19, No. 8, 1985
703
Table VIII. Summary Statistics from Regressions of Central Site Ambient and Indoor RSP Concentrations on Personal RSP Exposures
group
N
total smoke exposed nonsmoke exposed employed nonemployed smoke exposed, employed smoke exposed, nonemployed nonsmoke exposed, employed nonsmoke exposed, nonemployed
225 62 163 123 102 34 28 89 74
ambient RSP concentration R2 RMSE 0.005 0.02 0.02 0.04 0.02 0.08 0.004 0.04 0.0002
exposures was explained by the subjects’ indoor concentrations. This result is expected for 24-h averaged samples considering the substantial fraction of time spent at home. The weak correlation between personal exposures and indoor concentrations in nonsmoking households suggests that a substantial fraction of unexplained variance may be attributable to exposures outside the home, as J u and Spengler (22) indicate that respirable particulate concentrations inside nonsmoking homes show smaIl room-toroom differences. Stepwise multiple regression procedures were next utilized to analyze four predictive models. The four-level model structure represented an ordering of parameters successively more difficult to estimate in epidemiologic studies. The predictive performance of each model was evaluated overall and by subpopulation. For a set of independent variables, the stepwise regression procedure was set to select only those variables which contributed significantly to the model, that is, produced F statistics significant a t the p = 0.15 level upon entry into and completion of the model. The model structure and results are shown in Table IX. The first-level model consisted solely of ambient RSP as a predictor of personal RSP exposure. The stepwise regression significance tests applied to the model showed that ambient RSP has no predictive power in the population. Dichotomous variables indicating smoke exposure and employment were added to produce the second-level model. This model represents a “minimum-effort” data collection scheme with easily obtainable information on a subject’s exposure to tobacco smoke and employment status. Smoke exposure, with a coefficient of 30.8, and then ambient RSP, with a coefficient of 0.6, were selected. This model explained 16% of the variance in personal exposures. The RMSE was large, 31.7 pg/m3, as compared to the measured outdoor mean RSP value of 18 pg/m3. The third-level model was constructed with the addition of five time allocation measures obtained from the daily Activity Diaries: time at home, at work, in public places, in transit, and in other locations. This model represents a data collection scheme slightly more complex than that represented by the second-level model. Of the eight variables, smoke exposure was again the first predictor variable selected by the regression procedure, with a coefficient of 30.7. Time at work, ambient RSP, and travel time were then selected. This five-parameter model (four predictors plus the large 24.8 pg/m3 intercept) explained only 1% more variance in personal exposures than the three-parameter model identified in the analysis of the second-level model. The fourth-level model consisted of the eight previously analyzed variables plus the measured indoor RSP concentration. The indoor RSP measure was selected first, 704
Envlron. Sci. Technol., Vol. 19, No. 8, 1985
34.4 48.0 22.8 37.9 28.6 53.2 40.7 26.5 16.3
N
indoor RSP concentration R2 RMSE
258 75 183 141 117 42 33 99 84
0.50 0.48 0.24 0.64 0.49 0.87 0.23 0.11 0.83
25.6 36.8 19.4 24.6 20.5 21.4 34.1 24.9 6.3
Table IX. Statistics from Stepwise Regressions Identifying Significant Predictors in Ordered Sets of Epidemiological Variables”
N 225
R2 RMSE
regression equation
Level 1: Independent Variables; Ambient RSP variable not selected
Level 2: Independent Variables; Level 1 plus Smoke-Exposure, Employment Status 225 0.16 31.7 E = 24.5 + 0.6 (ambient RSP) + 30.8 (smoke exposure) Level 3: Independent Variables;Level 2 plus Time at Home, Time at Work, Time Traveling, Time in Public, Other Time 206 0.17 32.1 E = 24.8 + 0.6 (ambient RSP) + 30.7 (smoke exposure) - 5.5 (travel time) + 2.6 (work time) Level 4: Independent Variables; Level 3 plus Indoor RSP 202 0.51 24.9 E = 8.5 + 0.3 (ambient) + 3.1 (work time) + 5.4 (public time) + 0.6 (indoor RSP) aRegression selection criteria set at p = 0.15 for entry into and completion of the model.
explaining 47% of the variance in personal exposures; the coefficient was estimated to be 0.6. Time at work, time in public places, and ambient RSP were then selected. Smoke exposure dropped out of the model, apparently with the introduction of the indoor RSP value. This five-parameter model explained 51 % of the variance in personal exposures, an increase in explained variance of only 4%. The RMSE overpredicted reduced to 24.9 pg/m3. All models overpredicted when actual concentrations were low and underpredicted when actual concentrations were high. Stepwise regression on separate subgroups showed the fourth-level model including indoor RSP, and additional activity time or descriptor variables had more predictive power with certain subgroups than with the sample as a whole. Employed subjects from nonsmoking households only had 20% of their personal exposure variance explained by the multivariate regression model. In contrast, 84% of the personal exposure variance was explained for the nonemployed subjects from nonsmoking households. Stepwise regressions thus identified the predictive power of variables in the Kingston-Harriman data, but generalizations from this type of statistical procedure are questionable. I t may be concluded from this ordered analysis, however, that knowing the central site ambient RSP value and various descriptor variables of a population of adults generally allows prediction of less than 20% of the variance in personal exposures. The prediction errors associated with models of this type are as large as the observed variance in mean exposures. In our ordered model structure, the most difficult measure to obtain (not including the personal exposure), the actual 24-h home RSP concentration, provided the most information. Even
with direct measurement of indoor RSP concentrations, these basic regression models can, however, explain only half of the variance of personal RSP exposures. To expand the utility of fixed-location measurements, time-weighted or integrated exposure models were developed (23). Average personal exposures to air pollution can be calculated by a model of the form E = Ccit,/Cti (1)
-&
210
./. / .
1
1
where E (personal exposure) is the time-averaged concentration, Ci is the air pollution concentration associated with a specific location or activity, and tiis the time spent in that location or activity. However, in this study, only averaged values of C, over the complete interval Citi were available. By use of this basic model, personal exposures were estimated by the combination of four measured values: E = Chth Cot0 (2) where E is the estimated personal exposure to WP, C h the indoor RSP concentration, t h the time spent at home, co the central-site ambient RSP concentration, and to the time spent outdoors. (This model does not include terms for time or RSP concentrations in indoor environments other than the home.) In this estimation, six cases in which time-activity data were incomplete and one obvious outlier in which the disagreement between the personal and indoor concentrations from samplers reportedly collocated in the home was over 200 pg/m3 were deleted. The regression equation of estimated (E)on meaured exposures (P)for the total sample P = 17.7 0.9E (3) indicated that this time-weighted model explained 54% of the variance in personal exposures. The RMSE, however, was large, 24.5 pg/m3. Next, a model was developed that included an estimate for occupational RSP exposure: E = C h t h + Cot0 + ( c o + 34)tW + Cotother (4) where c h , th, Co, and toare defined as above, t, is the time spent at work, and totheris the time spent in public places and various other locations. The occupational exposure term, (C, 34)t,, was derived from the differences between the personal exposure measurements obtained for the employed and unemployed groups in this study and is in line with an analysis of data collected in an earlier personal monitoring study (23). This model, estimated for the total sample as P = 6.9 + 0.9E (5) explained 64% of the variance in mean personal exposures. The prediction error was 27.5 pg/m3. A plot of the measured personal exposures by the exposures predicted by this model is presented in Figure 1. The 95% confidence intervals for the model suggest the usefulness of such models for characterizing general population exposures, given data on indoor and outdoor concentrations and activity profiles. The obvious limitation of these predictive models is that the time variations of concentrations and activities (Ci in each subinterval t,) and the covariances are not available. More detailed data on Ci in each ti are necessary for further developments.
x
/
/'
/
/
a Y
o
*
Smoke-Exposed Subjects Non-Sm&-ExDosed Subiects -95% confidence interval
/ //'
-307A' -60-
I
l
l
l
l
l
l
l
l
l
-
L
+
+
+
Discussion The observed relationships between and among the particulate measures have several interesting implications for the analyses of the effects of particulate air pollution on human health. In almost all epidemiologic studies to
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RESPIRABLE PARTICULATE CONCENTRATION ( p g / m 3 )
Figure 2. Cumulatlve frequency distributions of central site ambient and personal smoke-exposed and nonsmoke-exposed RSP concentrations.
date, a single centrally located monitoring site has been used to characterize the personal exposures of the subjects in the vicinity. This study has shown, however, that ambient RSP pollutioh measurements in Kingston and Harriman, TN, underestimate personal RSP exposures by approximately 25 pg/m3 and that there is a disparity between smoke-exposed and nonsmoke-exposed groups. Personal RSP exposures are only weakly correlated with ambient RSP. The cumulative distributions of the ambient RSP concentrations and the personal RSP exposures of the smoke-exposed and nonsmoke-exposed groups (Figure 2) illustrate these differences and the importance of considering the indoor environment in evaluating personal exposures to respirable particles. The results of this study confirm earlier findings (24)that the presence of a smoker at home or at work adds to personal exposures. The strong influence of indoor environments on personal RSP exposures is clearly shown with the high indoorpersonal correlations and the predictive capability of the indoor RSP measure. In this study, all subjects kept a normal sleeplawake schedule, so for nearly all subjects, the personal monitor was collocated in the home with the indoor monitor for at least 8-10 h/day and is presumed to be measuring similar RSP concentrations at night during their collocation in the home. Therefore, the daytime home and work, or even outdoor, concentrations must have been higher, as indicated by the differences in the 24-h averaged concentrations. For instance, nonsmoke-exposed employed subjects averaged 12 pg/m3 higher RSP than nonsmoke-exposed nonemployed subjects which implies an average at-work exposure of 34 pg/m3 for an 8-h workday. Similarly, if the excess RSP in smokers' homes occurred during a 12-h period, the tobacco Environ. Sci. Technol., Vol. 19, No. 8, 1985
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smoke concentration might average 45 pg/m3. Personal concentrations are highly variable. Individuals spend varying amounts of time in a variety of environments. The concentrations in these environments vary with the number of smokers, presence of other sources, structural characteristics, and air-exchange rates. The most important variable identified in this study is the indoor respirable particle measurement. For some subgroups in this study, indoor concentrations explain in excess of 80% of the variance in personal exposures. The evaluation of the mechanistic time-weighted exposure models with measured exposure data indicates the value of the modeling approach. The fact that the timeweighted exposure models explain somewhat more of the variance in personal exposure than the custom-fit indoor regression models is encouraging. While time-weighted exposure models generally have only limited power for predicting exposures of individuals, they are useful to characterize the general distribution of exposures in a population (25). Estimating the distribution of exposures in a population will assist sample size determination and the calculation of misclassification error in epidemiologic studies. Further, the effectiveness of ambient emission control strategies on public exposures can be evaluated. Understanding personal exposures can be improved by knowing the covariance structure of activities and concentrations (26). Many of the indoor sources (home, work, and in transit) are associated with emissions generated by human activities. These daytime concentrations may be more relevant to assessing health effects because inhalation rates are greater when awake and active (10-20 lpm (liters per minute)) than asleep (4-8 lpm). Therefore, the time-weighted dose of RSP may disproportionately weight the day-time concentrations. The chemical composition of the average excess 25 pg/m3 RSP (personal-outdoor) is probably different than that of the outdoor RSP. The increased personal exposures were related to smoke exposure and employment. However, in the nonsmoking homes the average excess over the outdoor values was 10 pg/m3. Assuming a penetration factor of 0.6 (24),the excess indoor aerosol burden would be 18 pg/m3. Certainly the chemical composition of the RSP at a central location cannot represent the complete toxicity of the particulate matter affecting actual exposures. Where ambient concentrations are relatively low, only a fraction of the total particulate exposure will be of outdoor origin; the majority will be tobacco smoke constituents, fibers, organic compounds from cooking, resuspended dusts, spores, fungi, and other biological aerosols. An exception, however, will be the components of suspended particulate matter from predominately outdoor aerosols. Sulfate and nitrate particles are examples. The chemical nature of occupational and transit exposures are clearly dependent on the type of occupation and mode of transportation (27). The application of chemical/elemental analyses similar to source apportionment techniques applied to ambient aerosols (28)could improve the understanding of source contributions to indoor and personal exposures (29). This study reports data collected in two small rural communities during the months of February and March. Several conclusions developed assume that the concentrations measured are representative of annual averages. Potential differences due to the seasonal impacts on ambient concentrations, degree of structure tightness, and activity patterns may exist. Furthermore, one must be cautious when extrapolating to people living in different climates or areas with different population densities. 706
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Conclusion Air pollution epidemiological studies of suspended particles should consider indoor concentrations to avoid misclassification of exposures. This study indicates that personal exposures to RSP are greatly influenced by the home indoor concentratiom. To help differentiate exposures and potential effects, analysis of the chemical/elemental composition of size-selected particulate mass should be pursued. Future studies of personal exposures to particles could advance understanding by selecting a sample more representative of the larger population. Specifically, children and individuals from clerical, nonmanagerial employment categories should be included at the rates at which they occur in the population. Sampling equipment could be improved to obtain sharper particle size resolution at higher flow rates. Such equipment should ideally provide for sample integration over shorter time periods to characterize better the concentrations and ultimately the personal exposures typical in specific microenvironments. Acknowledgments We thank the volunteers from Kingston and Harriman, TN, and Ruth Venti, Marla Lindquist, Peggy Reed, George Allen, Pat Quinlan, David Godfrey, and Larry Killebrew for their help with this project. We also thank James Quackenboss for suggestions for this paper.
Literature Cited Mage, D. T. Public Health Rev. 1983, 11 (l),5-59. Spengler, J. D.; Soczek, M. L. Environ. Sci. Technol. 1984, 18, 269-280. Chapin, F. S., Jr. “Human Activity Patterns in the CityThings People Do in Time and Space”;Wiley: New York, 1974. Szalai, A., Ed. “The Use of Time: Daily Activities of Urban and Suburban Populations in Twelve Countries”; Mouton Publishers: The Hauge, 1972. Koontz, M. D.; Robinson, J. P. Environ. Monit. Assess. 1982,2, 197-212. Spengler, J. D.; Letz, R. E.; Ozkaynak, H.; Soczek, M. L. “Final Project Report. Kingston-Harriman, Tennessee Time Activity Study: Feasibility of Predicting Personal or Population Exposures Utilizing Ambient Air Quality Models and Human Activity Data”; U.S. Environmental Protection Agency: Research Triangle Park, NC, 1983. Ferris, B. G., Jr.; Speizer, F. E.; Spengler, J. D.; Dockery, D. W.; Bishop, Y. M. M.; Wolfson, J. M.; Humble, C. Am. Rev. Respir. Dis. 1979, 120 (4), 767-779. Dockery, D. W.; Spengler, J. D.; Reed, M. P.; Ware, J. Environ. Int. 1981, 5, 101-107. Dockery, D. W.; Spengler, J. D. J. Air. Pollut. Control Assoc. 1981, 31 (2), 153-159. Spengler, J. D.; Dockery, D. W.; Reed, M. P.; Tosteson, T. D.; Quinlan, P. 73rd Annual Meeting, Air Pollution Control Association, Montreal, Canada, June, 1980. Spengler, J. D.; Tosteson, T. D. Environmetrics 81, Society for Industrial and Applied Mathematics, Alexandria, VA, April, 1981. Shy, C. M.; Kleinbaum, D. G.; Morgenstern, H. h o c . N.Y. Acad. Med. 1978, 54 (11)) 1155-1160. Gladen, B.; Rogan, W. J. Am. J. Epidemiol. 1979,109 (5), 607-616. Ozkaynak, H.; Ryan, P. B.; Spengler, J. D.; Letz, R. E. Specialty Conference on Quality Assurance in Air Pollution Measurements, Air Pollution Control Association and American Society for Quality Control, Boulder, CO, Oct 1984. Environmental Protection Agency “Area Source Emissions Report for Year 1980”;National Emissions Data System: Research Triangle Park, NC, 1983. Miller, C. W.; Orton, T. H.; Barton; C. J.; Moore, R. E. “Emissions from Fossil-Fuel Combustion, Air Quality Data,
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and Meteorology for Roane County, Tennessee, During 1975”; Oak Ridge National Laboratory: Oak Ridge, TN,
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Assurance Project Plan”;Research Triangle Institute, U.S. Environmental Protection Agency Research Triangle Park, NC, 1981. Biddeson, D. L.; Eaton, W. C. “Performance and Systems Audit of the Respirable Particulate Exposure Study”;Research Triangle Institute, U.S. EnvironmentalProtection Agency: Research Triangle Park, NC, 1981. Eaton, W. C.; Arey, F. K.; McKee, J. L. “Systems Audit, 1982, Harvard School of Public Health Six-City Study”; Research Triangle Institute, U.S. Environmental Protection Agency: Research Triangle Park, NC, 1982. Quackenboss, J. J.; Karanek, M. S.; Spengler,J. D.; Letz, R. E. Environ. Int. 1982,8, 249-258. Ju, C.; Spengler, J. D. Environ. Sei. Technol. 1981, 15, 595-596.
Received for review June 14,1984. Revised manuscript received February 5,1985. Accepted March 5,1985. This work was funded by the US.Environmental Protection Agency under Cooperative Agreement CR808536010 and by general support provided to the Harvard Air Pollution Health Study under NIEHS Grant ES01108 and Electric Power Research Institute Grant RP-1001.
Characteristic Parameters of Particle Size Distributions of Primary Organic Constituents of Ambient Aerosols Luc Van Vaeck and Karel A. Van Cauwenberghe”
Department of Chemistry, University of Antwerp (U.I.A.), B-26 10 Wilrijk, Belgium Particle size distributions have been measured for nonvolatile organic constituents of ambient aerosols, sampled by Sierra and Andersen Hi-Vol cascade impactors, in suburban, rural, and seashore areas in Belgium and the Netherlands during the four seasons. The distributions of polycyclic and aza-heterocyclic aromatic hydrocarbons, n-aliphatic hydrocarbons, and carboxylic acids determined by gas chromatographic-mass spectrometric analysis are compared by using different formats. Results are discussed with respect to (1)generation and aerosol incorporation processes, which show condensation in the accumulation mode W2.5 pm) of anthropogenic combustion related compounds and natural contributions of the biosphere in the dispersion mode, and (2) aerosol aging processes, which show that the particle size distributions of its constituents tend to shift toward a larger size within the accumulation mode upon atmospheric transport. This is reflected in the mass median equivalent diameters of the partial cumulative distributions for the accumulation mode. Introduction The identification of toxic trace pollutants in ambient aerosols (e.g., heavy metals and polycyclic aromatic hydrocarbons) has led to the development of highly sensitive and specific analytical techniques for the quantitative determination of trace constituents in airborne particulate matter. Furthermore, the well-documented relationship between aerodynamic particle size and retention of aerosols in the respiratory tract resulted in the need for analyzing size fractionated samples, in order to better assess the health hazards involved with particle inhalation. Thus, the measurement of particle size distributions of individual pollutants became a major challenge to the environmental chemist. Detailed information can be found in the literature on the mechanisms of formation, aging, and removal of aerosol particles from the atmosphere. Number, surface, volume, 0013-936X/85/0919-0707$01.50/0
and mass size distributions of aerosols have been measured or calculated by using experimental data obtained with sophisticated fractionating equipment with high particle size resolution. An aerosol physical model has been proposed, which explains the presence of many organic compounds in aerosols by condensation and adsorption of combustion related gases of the second generation onto primary particles with high specific surface and thus predicts the enrichment of those organics in the accumulation mode (1-6). In this paper we discuss the particle size distributions of the organic constituents of ambient aerosols using different analytical formats and want to point out how well the generally accepted mechanisms of aerosol generation and aging described for the whole particle are reflected into the particle size distributions of its individual organic compounds. However, in view of the limited particle size resolution of the Hi-Vol cascade impactors commonly used for collecting large samples and their nonideal performance subject to systematic errors, the variations in particle size distributions of organics between different samples are expected to be more difficult to detect. For several years, the major goal of our research effort has been to build an extensive data base for the particle size distributions (psd) of a variety of organic trace constituents, both of anthropogenic and of natural origin. Ambient aerosols were collected in suburban, rural, and seashore areas. Chemical analysis of the different size fractions was performed for about 60 compounds, belonging to the series of n-alkanes and carboxylic acids as well as polycyclic and aza-heterocyclic aromatic hydrocarbons (PAH). In previous reports the sampling equipment and analysis methodology were described, and selected preliminary results were presented (7-12). This paper is restricted to a phenomenological discussion of the psd. A discussion of the toxocologically relevant parameters derived by applying a realistic model for particle
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