Pulmonary effects of indoor- and outdoor-generated particles in children with asthma.

Most particulate matter (PM) health effects studies use outdoor (ambient) PM as a surrogate for personal exposure. However, people spend most of their time indoors exposed to a combination of indoor-generated particles and ambient particles that have infiltrated. Thus, it is important to investigate the differential health effects of indoor- and ambient-generated particles. We combined our recently adapted recursive model and a predictive model for estimating infiltration efficiency to separate personal exposure (E) to P[M.sub.2.5] (PM with aerodynamic diameter [less than or equal to] 2.5 [micro]m) into its indoor-generated ([E.sub.ig]) and ambient-generated ([E.sub.ag]) components for 19 children with asthma. We then compared [E.sub.ig] and [E.sub.ag] to changes in exhaled nitric oxide (eNO), a marker of airway inflammation. Based on the recursive model with a sample size of eight children, [E.sub.ag] was marginally associated with increases in eNO [5.6 ppb per 10-[micro]g/[m.sup.3] increase in P[M.sub.2.5]; 95% confidence interval (CI), -0.6 to 11.9; p = 0.08]. [E.sub.ig] was not associated with eNO (-0.19 ppb change per 10 [micro]g/[m.sup.3]). Our predictive model allowed us to estimate [E.sub.a]g and [E.sub.ig] for all 19 children. For those combined estimates, only [E.sub.ag] was significantly associated with an increase in eNO ([E.sub.ag]: 5.0 ppb per 10-[micro]g/[m.sup.3] increase in P[M.sub.2.5]; 95% CI 0.3 to 9.7; p = 0.0g; [E.sub.ig]: 3.3 ppb per 10-[micro]g/[m.sup.3] increase in P[M.sub.2.5]; 95% CI, -1.1 to 7.7; p = 0.15). Effects were seen only in children who were not using corticosteroid therapy. We conclude that the ambient-generated component of P[M.sub.2.5] exposure is consistently associated with increases in eNO and the indoor-generated component is less strongly associated with eNO. Key words: ambient air pollution, asthma, exhaled nitric oxide, infiltration, P[M.sub.2.5]. Environ Health Perspect 113:499-503 (2005). doi:10.1289/ehp.7511 available via http://dx.doi.org/[Online 10 January 2005]

**********

It is known that particulate matter (PM) air pollution is associated with both increased morbidity and mortality [Brunekreef 1997; Koenig 2000; Pope 2000; Sunyer 2001; U.S. Environmental Protection Agency (EPA) 2004]. In many residences, ambient fine particles readily penetrate indoors (Abt et al. 2000; Allen et al. 2003; Anuszewski et al. 1998; Long et al. 2001; Sarnat et al. 2002), where most people spend > 90% of their time. As a result, individuals receive a substantial fraction of their exposure to ambient-generated particles while they are indoors. Therefore, it is important to evaluate the differential health effect of particles generated outdoors from those generated indoors. This information is needed both for health risk estimates and regulatory control to protect public health.

Most health effects studies have tested for associations between measures of ambient PM and adverse health effects. Only a few studies have evaluated the relative toxicity of indoor versus outdoor PM. One study assessed the in vitro toxicity of paired indoor and outdoor P[M.sub.2.5] (PM with aerodynamic diameter [less than or equal to] 2.5 [micro]m) samples collected in homes in Boston, Massachusetts (Long et al. 2001). The in vitro test used rat alveolar macrophages and measured change in tumor necrosis factor [alpha] (TNF-[alpha]) as a marker for inflammation. P[M.sub.2.5] from both outdoor and indoor samples increased endotoxin-normalized TNF-[alpha] levels significantly; however, the increases were greater for indoor PM samples (mean, 952 [+ or -] 157 pg/endotoxin unit vs. 494 [+ or -] 96 pg/endotoxin unit).

Another study evaluated the influence of air conditioning on observed associations between outdoor PM and health outcomes (Janssen et al. 2002). Health data for hospital admissions for chronic obstructive pulmonary disease (COPD) and cardiovascular disease were obtained for 14 U.S. cities. Home air conditioning was associated with lower penetration of outdoor particles, and the associations between P[M.sub.10] and hospital admissions were lower in cities with a higher prevalence of air conditioning.

In a recent panel study of 16 subjects with COPD in Vancouver, Canada, Ebelt et al. (in press) developed separate estimates of exposures to ambient and nonambient (i.e., the sum of indoor-generated particles and particles generated from personal activities) particles of different size ranges (P[M.sub.2.5], P[M.sub.10-2.5], and P[M.sub.10]) based on time-activity data and the use of particle sulfate measurements as a tracer of ambient particles. Health outcomes were examined against these estimated exposures. Total and nonambient particle exposures were not associated with any of the health outcomes, whereas estimated ambient exposures and, to a lesser extent, ambient concentrations were associated with decreased lung function, decreased systolic blood pressure, increased heart rate, and increased supraventricular ectopic heart beats.

We recently described a technique for separating personal exposure to PM into its indoor- and ambient-generated components using hourly light scattering data and a recursive modeling technique (Allen et al. 2003). The data came from a large panel study in Seattle, Washington, that collected indoor, outdoor, and personal exposure data on 107 subjects over a 2-year period (Liu et al. 2003). The Seattle study also collected various health end points that included lung function and exhaled nitric oxide (eNO), a marker of airway inflammation, in a subset of children with asthma. In a previous article we reported eNO associations with 24-hr P[M.sub.2.5] concentrations measured outside the home [4.3 ppb increase in eNO per 10-[micro]g/[m.sup.3] increase in P[M.sub.2.5]; 95% confidence interval (CI), 1.4 to 7.2], inside the home (4.2 ppb; 95% CI, 1.0 to 7.4), and on subjects (4.5 ppb; 95% CI, 1.0 to 7.9) (Koenig et al. 2003). In this article we describe the results of analyzing further the health data to test the associations between health outcomes and estimates of indoor-generated exposure ([E.sub.ig]) and ambient-generated exposure ([E.sub.ag]) based on subject time-location data and estimated particle infiltration efficiency ([F.sub.inf]; the fraction of the outdoor concentration that penetrates indoors and remains suspended). We hypothesize that P[M.sub.2.5] of outdoor origin has more effect on respiratory outcomes per unit mass than particles of indoor origin.

Materials and Methods

This study was conducted between winter 2000-2001 and spring 2001 in Seattle, Washington, as part of a larger exposure assessment and health effect panel study (Liu et al. 2003). Nineteen children, 6-13 years of age, were recruited from a local asthma and allergy clinic. All had physician-diagnosed asthma and were prescribed asthma medications daily or regularly. Ten of the subjects were not using inhaled corticosteroid (ICS) medication; nine were. Each subject in the panel was asked to participate for a 10-day monitoring session. Trained technicians made daily home visits to subjects between 1700 and 2000 hr to take air and health effect measurements.

Pollutant concentration measurements. PM measurements were taken inside and outside of each subject's residence using the Harvard impactors for integrated P[M.sub.2.5] (H[I.sub.2.5]) concentrations and using the Radiance nephelometer (model 903; Radiance Research, Seattle, WA) at eight residences for continuous light-scattering measurements. Personal P[M.sub.2.5] measurements were collected from each subject using the Harvard personal environmental monitors. Detailed descriptions and evaluation of these samplers can be found in Liu et al. (2002). All integrated measurements were collected over 24 hr (~ 1600 to 1600 hr) for 10 consecutive days. In addition, NO concentrations were monitored continuously at the Beacon Hill central site using a chemiluminescence monitor operated by the Washington State Department of Ecology (Olympia, WA).

Measurement of NO. Exhaled breath measurements were collected offline daily in the children's homes into an NO inert and impermeable Mylar balloon for up to 10 consecutive days. Samples were collected in the afternoon or early evening at the child's residence. Children were asked to forgo food intake for 1 hr before collection of exhaled breath. Exhaled breath was collected before lung function measurements, because deep inspirations affect NO concentration (Deykin et al. 1998). NO was quantified within 24 hr of collection using an API (Advanced Pollution Instrumentation, Inc., San Diego, CA) chemiluminescent nitrogen oxides (N[O.sub.x]) monitor (model 200A). We have tested the stability of NO in the Mylar bags by running comparisons of values immediately after collection and at 24 and 48 hr after collection and found NO values varying by < 2 ppb (n = 8). A complete description of the methods has been published (Koenig et al. 2003).

Measurement of lung function. During the daily visits, coached spirometry values consistent with American Thoracic Society criteria (American Thoracic Society 1995) were obtained with MicroDL spirometers (Micro Medical, Lewiston, ME). Spirometry measurements included forced expiratory volume in 1 sec (FE[V.sub.1]), forced vital capacity (FVC), and mid-expiratory flow (MEF). In addition, symptom forms were completed by subjects and medication use during the previous 24 hr was reviewed and collected. Subjects also filled out a time--location--activity diary (TAD) with a 15-min resolution.

Estimation of PM exposure components. We previously described the use of a recursive mass balance model (RM) to estimate the average [F.sub.inf] for individual residences (Allen et al. 2003). The RM estimates of [F.sub.inf] agreed well with those estimated with the sulfur tracer method ([R.sup.2] = 0.78; n = 14 residences) (Sarnat et al. 2002). We also published estimates of [E.sub.ag] and [E.sub.ig] for P[M.sub.2.5] among a subset of the Seattle panel study subjects (Allen et al. 2004). We estimated the 24-hr average [E.sub.ag] and [E.sub.ig] for each subject using the RM [F.sub.inf] estimates from the indoor/outdoor nephelometer measurements, the indoor ([C.sub.i]) and outdoor ([C.sub.o]) P[M.sub.2.5] concentrations measured with H[I.sub.2.5], and the fraction of the day ([F.sub.o]) that the subjects reported being outdoors or in transit based on the TAD:

[1] [E.sub.ag] = ([F.sub.o])[C.sub.o] + (1 - [F.sub.o])([C.sub.o] x [F.sub.inf])

[2] [E.sub.ig] = (1 - [F.sub.o])[[C.sub.i] - ([C.sub.o] x [F.sub.inf])]

Because nephelometer measurements were only valid at 8 of the 19 subjects' residences, a predictive model based on RM [F.sub.inf] estimates from 62 residences in the Seattle panel study, residence type, outdoor temperature, average daily rainfall, and the use of air cleaners was constructed to estimate [F.sub.inf] in the remaining 11 homes (Table 1). The estimated [F.sub.inf] values from the predictive model were compared against those from the RM and validated against the conventional sulfur method (Allen et al. 2003), which uses the regression slope of indoor versus outdoor sulfur concentrations for each residence as the estimated [F.sub.inf]. As a result of calculating [F.sub.inf] using both the RM and the predictive model, three groups of [E.sub.ag] and [E.sub.ig] estimates were created: a) those using the RM [F.sub.inf] values (n = 8 unique subjects), b) those using the predictive model [F.sub.inf] values (n = 11 unique subjects), and c) a combination of the above two--that is, RM [F.sub.inf] values when available and the predictive model [F.sub.inf] for the remaining subjects (henceforth called the combined modal; n = 8 + 11 = 19 subjects).

Statistical analysis. We used a linear mixed effects model with random intercept to test for within-subject associations between eNO and various P[M.sub.2.5] exposure estimates. The model included an interaction term between medication use and PM, a term for the within-subject, within-session (10-day monitoring period) effects, and a term for the subject between-session effects. We adjusted for the confounding variables of temperature, relative humidity, and, in the model for eNO, ambient NO measured at the Beacon Hill site. We also adjusted for subject age and body mass index (BMI). Our primary interest was the within-subject and within-session effect of PM. Analyses were conducted with all children from both winter and spring sessions. STATA 7.0 (Stata Corp., College Station, TX) was used for all health analyses, and SAS statistical package (version 8.0; SAS Institute, Cary, NC) using PROC Genmod with a repeated statement was used for the predictive model [F.sub.inf] modeling. All three [E.sub.ag]/[E.sub.ig] data sets (recursive, predictive, and combined) were examined with a focus on the combined data set.

The model used for the eNO analysis was as follows:

[3] E[Y] = [B.sub.0] + [b.sub.i] + [B.sub.1]([X.sub.ids] - [[bar.X].sub.is]) + [B.sub.2]([[bar.X].sub.is]-[[bar.X].sub.i])

+ [B.sub.3][[bar.X].sub.i] + [B.sub.4][med.sub.i] + [B.sub.5][med.sub.i]

x ([X.sub.ids] - [[bar.X].sub.is]) + [B.sub.6]([Z.sub.ids] - [[bar.Z].sub.is])

+ [B.sub.7]([[bar.Z].sub.is] - [[bar.X].sub.i]) + [B.sub.8][[bar.Z].sub.i] + [B.sub.9]Age

+ [B.sub.10]BMI + [B.sub.11]Temp + [B.sub.12]RH,

where RH is relative humidity and BMI is body mass index. This basic model was used previously in the original analysis of the relationship between eNO and PM in the children with asthma (Koenig et al. 2003), where [X.sub.ids] is the P[M.sub.2.5] reading for individual i on day d during session s, [[bar.X].sub.is] is the mean P[M.sub.2.5] reading for a subject during a session, [[bar.X].sub.i] is the mean P[M.sub.2.5] reading for a subject during one or two sessions, [med.sub.i] is an indicator for medication use (constant for each subject), [Z.sub.ids] is the ambient NO reading for individual i on day d during session s, [[bar.Z].sub.is] is the mean ambient NO reading for a subject during a session, and [[bar.Z].sub.i] is the mean ambient NO reading for a subject during all sessions.

We also analyzed the data using generalized estimating equations (GEE) with an exchangeable working correlation matrix and robust SEs to adjust for autocorrelation in the data. The GEE model produced similar effect estimates.

Results

Nineteen children with asthma participated in this panel study in Seattle. All subjects completed one 10-day monitoring session, and 10 subjects completed two sessions. During this study, the home indoor and outdoor P[M.sub.2.5] concentrations averaged 9.5 and 11.1 [micro]g/[m.sup.3], respectively (Table 2), whereas personal exposure to total P[M.sub.2.5] averaged 13.4 [micro]g/[m.sup.3]. The total personal P[M.sub.2.5] exposure was then separated into indoor- and outdoor-originated components using the RM for eight residences with nephelometer measurements and a predictive model for the remaining 11 residences. The predictive model for [F.sub.inf] employed two important home characteristics, residence type, and the use of air cleaner, as well as outdoor temperature and precipitation as surrogates for changes of home ventilation conditions (Table 1). This predictive model agreed well with the RM ([R.sup.2] = 0.60) and the sulfur tracer [F.sub.inf] estimates ([R.sup.2] = 0.66) (Figure 1). The average [F.sub.inf] for the 19 subjects was 0.56 [+ or -] 0.15 (range, 0.23-0.86). The average [E.sub.ag] and [E.sub.ig] from the RM model were not significantly different from those estimated from the predictive model (Table 2). Thus, we pooled the [E.sub.ag] and [E.sub.ig] estimates from both models for the following health effect assessment. We examined the [E.sub.ag] and [E.sub.ig] estimates from the combined model for their associations with increase in eNO. Table 3 shows distributions for the health end points. In this analysis we found that eNO was associated with [E.sub.ag] estimated among subjects not on prescribed ICS medication (5.0 ppb per 10-[micro]g/[m.sup.3] increase in estimated exposure; 95% CI, 0.3 to 9.7; Table 4). There was no association between eNO and [E.sub.ig] (Table 4). In contrast to our findings with eNO, associations between changes in lung function and estimated exposures were found for [E.sub.ig] but not for [E.sub.ag]. Furthermore, the results were not statistically significant across all lung function measures. FE[V.sub.1] and FVC were both significantly negatively associated with [E.sub.ig] in children not using ICS (FE[V.sub.1], p = 0.01; FVC, p = 0.00), whereas MEF was negatively, but not significantly, associated with [E.sub.ig] (p = 0.35). No significant associations were seen between lung function changes and the combined model estimates of [E.sub.ag].

[FIGURE 1 OMITTED]

Table 5 shows associations between the eNO and measured P[M.sub.2.5] on subjects (Harvard personal environmental monitor) and at home indoors and outdoors in the same 19 children included in the combined model. As shown in Table 5, associations were found between eNO and measured outdoor, indoor, and personal P[M.sub.2.5] (p = 0.01-0.03). In all cases, the changes were seen only in children not using ICS medications.

Discussion

Our study has shown that, for eNO, ambient-generated particles are more potent per unit mass than indoor-generated particles. This [E.sub.ag] effect on eNO using the combined model estimates also agreed well with the estimates from both the RM and the predictive model. The increases in eNO associated with [E.sub.ag] were 5.6 ppb for the RM estimates (p = 0.08), 5.3 ppb for the predictive model estimates (10 = 0.04), and 5.0 ppb for the combined model (p = 0.04). Corresponding changes with [E.sub.ig] were not significant (p = 0.41, 0.12, and 0.15, respectively). In this respect, our results agree with those of Ebelt et al. (in press), who found that outdoor-generated particles were associated with health outcomes, whereas nonambient particles were not in a group of subjects with COPD in Vancouver. These two studies demonstrate the usefulness of separating total personal particle exposures into indoor- and outdoor-generated components and the relative potency of indoor- and outdoor-generated particles.

Our conclusion that eNO is associated more strongly with outdoor-generated particles than indoor-generated particles is supported by the internal consistency of the results. For subjects with combined model estimates of [F.sub.inf], the estimated increase in eNO per 10-[micro]g/[m.sup.3] increase in P[M.sub.2.5] was 5.0 ppb (p < 0.04) for [E.sub.ag], which was greater than the 3.9 ppb for outdoor measured P[M.sub.2.5] (p = 0.01) because [E.sub.ag] takes into account personal activities and particle infiltration efficiency to arrive at a more accurate estimate of exposure to ambient-originated PM (Table 5). The effect of measured total indoor P[M.sub.2.5], a combination of indoor- and outdoor-generated particles, on eNO was 4.1 ppb/10 [micro]g/[m.sup.3] P[M.sub.2.5] (p = 0.01) in Table 5, which was reduced to a nonsignificant 3.3 ppb/10 [micro]g/[m.sup.3] P[M.sub.2.5] (p = 0.15) for [E.sub.ig] when the ambient PM contribution was removed from the total exposures. In all three exposure models, [E.sub.ag] was more strongly associated with eNO than was [E.sub.ig]. Also, [E.sub.ag] showed an interaction with ICS use, as did our original study with outdoor, indoor, and personal measured P[M.sub.2.5] (Koenig et al. 2003).

Our lung function results show that exposure to particles generated indoors, but not outdoors, was associated with decrements of lung functions except for MEF. Furthermore, the association was not consistent across all three exposure models. Both combined (n = 17 subjects) and predictive models (n = 9 subjects) showed similar results for FE[V.sub.1] and FVC, whereas the recursive model estimates for eight subjects showed nonsignificant association between these lung function measures and [E.sub.ig]. The fact that some lung function decrements were associated with indoor-generated particles indicates that the relationship between respiratory health and PM is complex. It was not surprising that the P[M.sub.2.5] associations with eNO and lung function were not consistent. This disagreement between eNO increases and lung function changes has been reported in clinical literature that consistently shows either no correlation or a negative correlation between changes in eNO and changes in FE[V.sub.1] among subjects with asthma (Dal Negro et al. 2003; Li et al. 2003; Nightingale et al. 1999; Steerenberg et al. 2003).

Outdoor particle concentrations are associated with a wide spectrum of respiratory health effects including respiratory symptoms in children with asthma (Delfino et al. 1998), lung function decrements in children with asthma (Delfino et al. 2002; Koenig et al. 1993), hospital admissions in the general population (Schwartz 1996; Sheppard et al. 1999), and mortality in the general population (Dockery et al. 1993; Schwartz 2000). On the other hand, there are also studies showing adverse respiratory health effects associated with indoor-generated particles including allergens, dust mites, fungal spores, endotoxins, and viruses (Long et al. 2001; Majid and Kammen 2001; Simoni et al. 2002; Smedbold et al. 2002; Wan and Li 1999).

Our results for eNO appear to be biologically plausible because asthma is an inflammatory disease and perturbations in asthma are expected to be associated with markers of airway inflammation. Several studies show relationships between eNO and outdoor exposure to PM or other air pollutants. One study found an association between exhaled NO values and high levels of outdoor carbon monoxide and NO, but not PM, in the Netherlands in healthy nonsmoking subjects (van Amsterdam et al. 1999, 2000). More recently, eNO levels were associated with exposure to P[M.sub.10], black smoke, nitrogen dioxide, and ambient NO in a panel study of children in the Netherlands (Steerenberg et al. 2001) and in a panel of adults with respiratory disease (Jansen et al. 2004). Adamkiewicz et al. (2004) presented data showing an association between measures of air pollution and eNO values in a panel of elderly nonsmoking subjects with cardiac disease in Steubenville, Ohio (USA). Their analysis found a 1.5-ppb increase in eNO (95% CI, 0.3 to 2.6) for a mean interquartile range increase in P[M.sub.2.5].

Model limitations. It is challenging to model personal exposure among children partly because of the elevated personal cloud and children's movement between several indoor microenvironments (Liu et al. 2003; Wu et al. in press). Children in the Seattle panel study spent an average of 66% of their time indoors at home and 21% indoors away from home (primarily at school), whereas the adults in the larger panel study in Seattle spent an average of 83-88% of their time indoors at home (Liu et al. 2003). Because we only collected stationary indoor measurements and estimated [F.sub.inf] in the subjects' residences, we made a strong assumption that all indoor environments encountered by the subject were represented by their residence. This assumption may have resulted in uncertainties in the exposure estimates because of the considerable fraction of time that this group spent in unmonitored indoor environments, especially school.

To make the most efficient use of our eNO and spirometry data, we developed a predictive model to estimate [F.sub.inf] (and therefore [E.sub.ag] and [E.sub.ig]) in residences for which nephelometer data were not available (Table 1). Although the predicted [F.sub.inf] estimates were validated with an independent estimate of [F.sub.inf] (Figure 1), the predictive model is derived from the estimates produced by the recursive model, and as a result the predictive model estimates include errors introduced by a two-step modeling procedure. Nevertheless, the consistency of the associations between [E.sub.ag] and eNO for the RM and the combined model exposure estimates provides evidence of the reliability of the combined model's [F.sub.inf] estimates.

Conclusion

Our eNO results support our hypothesis that P[M.sub.2.5] of outdoor origin could be more potent per unit mass than particles of indoor origin. However, our lung function data indicate that P[M.sub.2.5] of indoor origin might be more potent per unit mass in resulting in decrements of lung functions, although the results across functional tests were not consistent. If outdoor particles are more strongly associated with adverse health outcomes than particles generated indoors, the fact that outdoor particles readily penetrate indoors would partially explain why epidemiologic time series studies consistently find associations between health outcomes and PM measured at outdoor fixed sites despite the fact that people spend most of their time indoors.

This is a preliminary study using a newly developed exposure source model that we hope will be useful to air pollution epidemiology. We tentatively conclude that partitioning personal exposure into indoor- versus outdoor-generated particles is useful in understanding the health effects of sources of personal P[M.sub.2.5] and that the effects of indoor- versus outdoor-generated particles differ for different health end points.

Table 1. Results of regression analvsis for [F.sub.inf]
(n = 62 residences).

Parameter                                       Estimate         SE

Intercept                                         0.41          0.07
Residence type
  Private home (reference)
  Private apartment                               0.03          0.05
  Group home                                      0.19          0.06
Air cleaner
  None (reference)
  Ion generator                                  -0.07          0.05
  Filter                                         -0.08          0.07
  Electrostatic precipitator                     -0.11          0.06
Average outdoor temperature ([degrees]C) (a)
  < 4 (reference)
  4-8                                             0.19          0.07
  8-12                                            0.32          0.07
  [greater than or equal to] 12                   0.45          0.07
Average daily rainfall (inches) (b)
  < 0.5 (reference)
  0.05-0.1                                       -0.07          0.05
  > 0.1                                          -0.15          0.06

Parameter                                        95% CI        p-Value

Intercept                                     0.28 to 0.54     < 0.001
Residence type
  Private home (reference)
  Private apartment                          -0.08 to 0.14       0.61
  Group home                                  0.07 to 0.31     < 0.01
Air cleaner
  None (reference)
  Ion generator                              -0.16 to 0.02       0.14
  Filter                                     -0.22 to 0.05       0.23
  Electrostatic precipitator                 -0.22 to 0.00       0.05
Average outdoor temperature ([degrees]C) (a)
  < 4 (reference)
  4-8                                         0.06 to 0.32     < 0.01
  8-12                                        0.18 to 0.45     < 0.001
  [greater than or equal to] 12               0.31 to 0.58     < 0.001
Average daily rainfall (inches) (b)
  < 0.5 (reference)
  0.05-0.1                                   -0.16 to 0.02       0.13
  > 0.1                                      -0.26 to -0.04    < 0.01

The regression coefficients are used to predict [F.sub.inf] in
residences without nephelometer data ("predictive model").

(a) At Beacon Hill Central Site. (b) At Sand Point Way National
Weather Service station.

Table 2. Distributions of residential indoor and outdoor
concentrations and personal [E.sub.ig] and [E.sub.ag]
([micro]g/[m.sup.3]).

                              Total no. of
                               monitoring      No.
Model         Concentration    events (a)    (days)    Mean    Minimum

               Home indoor      27 (19)        248      9.5      2.3
              Home outdoor                             11.1      2.8
Recursive      [E.sub.ag]       11 (8)         101      7.0      1.8
               [E.sub.ig]                               2.1      0.0
Predictive     [E.sub.ag]       16 (13)        147      6.0      1.3
               [E.sub.ig]                               4.0      0.0
Combined       [E.sub.ag]       27 (19)        248      6.4      1.3
               [E.sub.ig]                               3.2      0.0

Model         Concentration       25%        Median     75%    Maximum

               Home indoor        5.7          7.6     10.8      36.3
              Home outdoor        6.3          9.5     14.6      40.4
Recursive      [E.sub.ag]         4.2          5.9      9.2      22.6
               [E.sub.ig]         0.0          1.2      2.3      17.2
Predictive     [E.sub.ag]         3.4          5.0      7.5      22.6
               [E.sub.ig]         0.9          2.2      4.9      33.0
Combined       [E.sub.ag]         3.7          5.5      7.8      22.6
               [E.sub.ig]         0.5          1.7      4.2      33.0

Abbreviations: 25%, 25th percentile; 75%, 75th percentile.

(a) Number of unique subjects in parentheses.

Table 3. Descriptive statistics of health outcomes.

Health            No. of subjects   Person-
measurement       (no. sessions)      days     Mean    Minimum

eN0 (ppb)             19 (29)         240      15.4       5
[FEV.sub.1] (L)       17 (29)         269       1.8       0.5
MEF (L/min)           17 (29)         269     113        21
FVC (L)               17 (29)         269       2.3       0.7

Health
measurement             25%      Median     75%     Maximum

eN0 (ppb)               9.7       12.5      18.0      79.8
[FEV.sub.1] (L)         1.4        1.9       2.2       3.4
MEF (L/min)            71        107       149       320
FVC (L)                 1.9        2.4       2.7       3.5

Abbreviations: 25%, 25th percentile; 75%, 75th percentile.

Table 4. Associations between eN0 (ppb) and outdoor- versus
indoor-generated particles in children with asthma: recursive
model (n = 8), predictive model (n = 11), and combined model (n = 19).

                                  Change
                        Use       per 10
                        of      [micro]g/
                       medi-    [m.sup.3]
                        ca-     estimated
Exposure       Model   tion    [PM.sub.2.5]        95% CI       p-Value

[E.sub.ig]   Combined   No         3.29        -1.14 to 7.73     0.15
                        Yes       -4.94       -10.94 to 1.06     0.11
[E.sub.ag]   Combined   No         4.98         0.28 to 9.69     0.04
                        Yes        1.67        -3.77 to 7.12     0.55
[E.sub.ig]   Recursive  No        -0.19        -8.37 to 8.00     0.97
                        Yes       -0.47        12.03 to 11.10    0.94
[E.sub.ag]   Recursive  No         5.63        -0.62 to 11.88    0.08
                        Yes       -4.30       -14.60 to 6.01     0.41
[E.sub.ig]   Predictive No         3.46        -0.90 to 7.83     0.12
                        Yes       -4.99       -11.01 to 1.04     0.11
[E.sub.ag]   Predictive No         5.33         0.31 to 10.35    0.04
                        Yes        1.66        -3.75 to 7.06     0.55

Table 5. Results of eNO analyses with indoor, outdoor, and personal
monitors for 19 children included in the combined model.

                              Change per
                             10 [micro]g/
                               [m.sup.3]
Measure          Use of        estimated
               medication    [PM.sub.2.5]       95% CI       p-value

Personal (a)       No            4.48         0.95 to 8.00     0.01
                  Yes           -0.49        -2.95 to 1.98     0.70
Outdoor            No            3.90         0.91 to 6.88     0.01
                  Yes            1.00        -2.10 to 4.09     0.53
Indoor             No            4.13         0.87 to 7.38     0.01
                  Yes           -1.37        -5.44 to 2.70     0.51

(a) Two sessions removed from personal PM analysis
because of insufficient data.

REFERENCES

Abt E, Suh HH, Allen G, Koutrakis P. 2000. Characterization of indoor particle sources: a study conducted in the metropolitan Boston area. Environ Health Perspect 108:35-44.

Adamkiewicz G, Ebelt S, Syring M, Slater J, Speizer FE, Schwartz J, at al. 2004. Association between air pollution exposure and exhaled nitric oxide in an elderly panel. Thorax 59:204-209.

Allen R, Larson T, Sheppard L, Wallace L, Liu L-JS. 2003. Use of real-time light scattering data to estimated the contribution of infiltrated and indoor-generated particles to indoor air. Environ Sci Technol 37:3485-3492.

Allen R, Wallace L, Larson T, Sheppard L, Liu L-JS. 2004. Estimated hourly personal exposures to ambient and non-ambient particulate matter among sensitive populations in Seattle, WA. J Air Waste Manage Assoc 54:1197-1211.

American Thoracic Society. 1995. American Thoracic Society guidelines for standardization of spirometry. Am J Respir Crit Care Mad 152:1107-1138.

Anuszewski J, Larson TV, Koenig JQ. 1998. Simultaneous indoor and outdoor particle light-scattering measurements at nine homes using a portable nephelometer. J Exp Anal Environ Epidemiol 8:483-493.

Brunekreef B. 1997. Air pollution and life expectancy: is there a relation? Occup Environ Mad 54:781-784.

Dal Negro R, Micheletto C, Tognalia S, Turco P, Rossetti A, Cantini L. 2003. Assessment of inhaled BDP-dose dependency of exhaled nitric oxide and local and serum eosinophic markers in steroids-naive nonatopic asthmatics. Allergy 58:1018-1022.

Delfino RJ, Zeiger RS, Seltzer JM, Street DH. 1998. Symptoms in pediatric asthmatics and air pollution: differences in effects by symptom severity, anti-inflammatory medication use, and particulate averaging time. Environ Health Perspect 108:751-761.

Delfino RJ, Zeiger RS, Seltzer JM, Street DH, McLaren C. 2002. Association of asthma symptoms with peak particulate air pollution and effect modification by anti-inflammatory medication use. Environ Health Perspect 110:A607-A617.

Deykin A, Halpern O, Massaro AF, Drazen JM, Israel E. 1998. Expired nitric oxide after bronchoprovocation and repeated spirometry in patients with asthma. Am J Respir Crit Care Mad 157:759-775.

Dockery DW, Pope CA III, Xu X, Spengler JD, Ware JH, Ray ME, et al. 1993. An association between air pollution and mortality in six U.S. cities. N Engl J Med 329:1753-1759.

Ebelt ST, Wilson WE, Brauer M In press. A comparison of health effects from exposure to the ambient and non-ambient components of particulate matter. Epidemiology.

Jansen K, Koenig JQ, Larson TV, Fields C, Mar TF, Stewart J, et al. 2004. Nitric oxide in subjects with respiratory disease is associated with PM2.5 and black carbon in Seattle [abstract]. Am J Respir Crit Care Med 189:A282.

Janssen NAH, Schwartz J, Zanobetti A, Suh HH. 2002. Air conditioning and source-specific particles as modifiers of the effect of P[M.sub.10] on hospital admissions for heart and lung disease. Environ Health Perspect 110:43-49.

Koenig JQ. 2000. Health Effects of Ambient Air Pollution: How Safe Is the Air We Breathe? Boston:Kluwer Academic Publishers.

Koenig JQ, Jansen K, Mar TF, Lumley T, Kaufman J, Trenga CA, et al. 2003. Measurement of offline exhaled nitric oxide in a study of community exposure to air pollution. Environ Health Perspect 111:1625-1929.

Koenig JQ, Larson TV, Hanley QS, Rebolledo V, Dumler K, Ceckoway H, et al. 1993. Pulmonary function changes in children associated with fine particulate matter. Environ Res 63:26-38.

Li AM, Lex C, Zacharasiewicz A, Wong E, Erin E, Hansel T, et al. 2003. Cough frequency in children with stable asthma: correlation with lung function, exhaled nitric oxide, and sputum eosinophil count. Thorax 58:974-978.

Liu L-JS, Box M, Kalman D, Kaufman J, Koenig J, Larson T, et al. 2003. Exposure assessment of particulate matter for susceptible populations in Seattle. Environ Health Perspect 111:909-918.

Liu L-JS, Slaughter JC, Larson TV. 2002. Comparison of light scattering devices and impactors for particulate measurements in indoor, outdoor, and personal environments. Environ Sci Technol 36:2977-2986.

Long CM, Sub HH, Kobzik L, Catalana PJ, Ning YY, Koutrakis P. 2001. A pilot investigation of the relative toxicity of indoor and outdoor fine particles: in vitro effects of endotoxin and other particulate properties. Environ Health Perspect 109:1019-1026.

Majid E, Kammen DM. 2001. Quantifying the effects of exposure to indoor air pollution from biomass combustion on acute respiratory infections in developing countries. Environ Health Perspect 109:481-488.

Nightingale JA, Rogers DF, Barnes PJ. 1999. Effects of inhaled ozone on exhaled nitric oxide, pulmonary function, and induced sputum in normal and asthmatic subjects. Thorax 54:1061-1069.

Pope CA III. 2000. Epidemiology of fine particulate air pollution and human health: biologic mechanisms and who's at risk. Environ Health Perspect 108(suppl 4):713-723.

Sarnat JA, Long CM, Koutrakis P, Coull BA, Schwartz J, Suh HH. 2002. Using sulfur as a tracer of outdoor fine particulate matter. Environ Sci Technol 36:5305-5314.

Schwartz J. 1996. Air pollution and hospital admissions for respiratory disease. Epidemiology 7:20-28.

Schwartz J. 2000. Assessing confounding, effect modification, and thresholds in the association between ambient particles and daily deaths. Environ Health Perspect 108:563-568.

Sheppard L, Levy D, Norris G, Larson TV, Koenig JQ. 1999. Effects of ambient air pollution on nonelderly asthma hospital admissions in Seattle, Washington, 1987-1994. Epidemiology 10:23-30.

Simoni M, Carrozzi L, Baldacci S, Scognamiglio A, Pede FD, Sapigni T, et al. 2002. The Po River (North Italy) indoor epidemiological study: effects of pollutant exposure on acute respiratory symptoms and respiratory function in adults. Arch Environ Health 57:130-136.

Smedbold HT, Ahlen C, Nilsen AM, Norback D, Hilt B. 2002. Relationships between indoor environments and nasal inflammation in nursing personnel. Arch Environ Health 57:155-161.

Steerenberg PA, Janssen NA, de Meer G, Fischer PH, Nierkens S, von Loveren H, et al. 2003. Relationship between exhaled NO, respiratory symptoms, lung function bronchial hyper-responsiveness, and blood eosinophilia in school children. Thorax 58:242-245.

Steerenberg PA, Nierkens S, Fischer PH, van Loveren H, Opperhuizen A, Vos JG, et al. 2001. Traffic-related air pollution affects peak expiratory flow, exhaled nitric oxide, and inflammatory nasal markers. Arch Environ Health 56:167-174.

Sunyer J. 2001. Urban air pollution and chronic obstructive pulmonary disease: a review. Eur Respir J 17:1024-1033.

U.S. EPA. 2004. Air Quality Criteria for Particulate Matter. EPA/600/P-99/002abF. Washington, DC:U.S. Environmental Protection Agency.

Van Amsterdam JG, Nierkens S, Vos SB, 0pperhuizen A, van Lovernen H, Steerenberg PA. 2000. Exhaled nitric oxide: a novel biomarker of adverse respiratory health effects in epidemiological studies. Arch Environ Health 55:418-423.

Van Amsterdam JG, Verlaan BPJ, van Lovernen H, Elzakker BBV, Vos SG, 0pperhuizen A, et al. 1999. Air pollution is associated with increased level of exhaled nitric oxide in nonsmoking healthy subjects. Arch Environ Health 54:331-335.

Wan G-H, Li C-S. 1999. Indoor endotoxin and glucan in association with airway inflammation and systemic symptoms. Arch Environ Health 54:172-179.

Wu C-F, Delfino RJ, Floro JN, Quintana PJE, Samimi BS, Kleinman MT, et al. In press. Exposure assessment and modeling of particulate matter for asthmatic children using personal nephelometers. Atmos Environ.

Jane Q. Koenig, (1) Therese F. Mar, (1) Ryan W. Allen, (1) Karen Jansen, (1) Thomas Lumley, (2) Jeffrey H. Sullivan, (1) Carol A. Trenga, (1) Timothy V. Larson, (3) and L.-Jane S. Liu (1)

(1) Department of Environmental Health and Occupational Sciences, (2) Department of Biostatistics, and (3) Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington, USA

Address correspondence to J.Q. Koenig, Department of Environmental Health, Box 357234, Room F561A, 1705 NE Pacific, University of Washington, Seattle, WA 98195 USA. Telephone: [phone]. Fax: [phone]. E-mail: Jkoenig@ u.washington.edu

We thank L. Tuttle, T. Gould, M. Drudge, and the field/lab technicians who worked on this project. We owe a great deal to our study subjects.

This work was funded by the U.S. Environmental Protection Agency (EPA; CR82717701), the Northwest Research Center for Particulate Air Pollution and Health (U.S. EPA grant CR827355), and National Institute of Environmental Health Sciences grant P30 ES07033. Mention of trade names or commercial products does not constitute an endorsement or recommendation for use.

The authors declare they have no competing financial interests.

Received 18 August 2004; accepted 10 January 2005.