Articles

An Association between Fine Particles and Asthma Emergency Department

Visits for Children in Seattle

Gary Norris,1 Sharon N. YoungPong,2 Jane 0. Koenig,3 Timothy V. Larson,1 Lianne Sheppard,4 and

James W Stout5

1Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington, USA; 2CH2MHill, Portland,

Oregon, USA; 3Department of Environmental Health; 4Department of Biostatistics; and 5Department of Pediatrics and Health Services,

University of Washington, Seattle, Washington, USA

Asthma is the most common chronic illness of childhood and its prevalence is increasing, causing much concer for identification of risk factors such as air pollution. We previously conducted a study showing a relationship between asthma visits in all persons < 65 years of age to emergency departments (EDs) and air pollution in Seattle, Washington. In that study the most frequent zip codes of the visits were in the inner city. The Seattle-King County Department of

Public Health (Seattle, WA) subsequendy published a report which showed that the hospitalization rate for children in the inner city was over 600/100,000, whereas it was c 100,100,000 for

children living in the suburbs. Therefore, we conducted the present study to evaluate whether

asthma visits to hospital emergency departments in the inner city of Seattle were associated with

outdoor air pollution levels. ED visits to six hospitals for asthma and daily air pollution data

were obtained for 15 months during 1995 and 1996. The association between air pollution and

childhood ED visits for asthma from the inner city area with high asthma hospitalization rates

were compared with those from lower hospital utlization areas. Daily ED counts were regressed

against fine particulate matter (PM), carbon monoxide (CO), sulfur dioxide, and nitrogen dioxide using a semiparametric Poisson7 regression model. Significant associations were found

between ED visits for asthma in children and fine PM and CO. A change of 11 pg/m3 in fine

PM was associated with a relative rate of 1.15 [95% confidence interval (CI), 1.08-1.23]. There

was no stronger association between ED visits for asthma and air poilution in the higher hospital

utilization area than in the l6wer utilization area. These findings were seen when estimated

PM25 concentrations were below the newly adopted annual National Ambient Air Quality

Standard of 15 pg/m3. Key words. air pollution, asthma, carbon monoxide, children, emergency

departments, nitrogen dioxide, particulate matter. Environ Health Pertca 107:489493 (1999).

[Online 6 May 1999]

hp:/lehpnetl.niehs.nih.gov/docs/1999/107p489-493norrislabsact.html

Asthma is the most common chronic illness

in children and the cause of most school

absences (1). The rate of hospitalization for

asthma has been increasing in children < 18

years of age (2). The primary increases in

asthma morbidity are observed in minority

populations in urban areas (3). When focusing on the inner city, 8-12% of children

under 18 years of age have asthma (4). Many

airborne factors can aggravate asthma, including cigarette smoke (5), dust mites, molds,

cold air, animal dander (6), and cockroaches

(7). Additionally, increases in air pollution

are associated with exacerbation of asthma as

measured by decreased lung function values

and respiratory symptoms (8-10), shortness

of breath (11), emergency department (ED)

visits (12-18), and hospitalizations (19,20).

Thus there is concern that air pollution is

aggravating childhood asthma (21,22).

Seattle, Washington, is a hilly coastal

city with a moderate climate. It is currently

in compliance with all U.S. Environmental

Protection Agency (EPA) air pollution standards. In Seattle, three studies have been

conducted that found associations between

the exacerbation of asthma and airborne

particulate matter (PM). A study conducted during the heating season in a wood

smoke impacted area found a significant

association between decreased lung function in elementary school children with

asthma and dry light scattering (a55p), a

measure of fine PM (8). Another study carried out in Seattle from 1989 to 1990

found significant associations between both

PM < 10 pm in aerodynamic diameter

(PM0O) and light-scattering values (particles

approximately < 1.0 pm) and ED visits for

asthma for patients aged 65 years and

younger (12). Most recently, a study of

hospital admissions for asthma found an

estimated 4-5% increase in admission rate

associated with several measures of PM air

pollution [PM10, particulate matter < 2.5

pm in aerodynamic diameter (PM2 ), and

the coarse fraction PM1o-PM2? (20).

Sources of PM in Seattle inclu e wood

smoke, gasoline and diesel vehicles, resuspended road dust, and industry (23). A

source apportionment study of PMIO in

Seattle in 1991-1992 found that during

the heating season, 80% of PM in residential

areas originated from wood-burning devices

(24). Concentrations of all measured air pollutants have been decreasing in Seattle over

the last decade (25).

The Seattle-King County Health

Department (Seattle, WA) carried out a survey from 1987 to 1996 on asthma hospitalization rates for children in Seattle and surrounding areas using the Comprehensive

Hospital Abstract Reporting System and

found > 50% higher hospitalization rates in

central and southeast Seattle (the inner city)

as compared to other districts around Seattle

(26). The present study examined whether

there was a stronger association between air

pollution and ED visits for asthma for children in the inner city area than for other

children in Seattle using the same hospitals

and found that there was not.

Methods

Health data Daily ED visits for asthma were

obtained from six hospitals in central and

southeast Seattle from 1 September 1995 to

31 December 1996. Permission for use of

the hospital data was obtained from the

University of Washington Human Subjects

Office in Seattle. Four of the six hospitals

were downtown; the other two were located

within 12 km of central Seattle (Figure 1).

The total number of daily visits were compiled based on the International Classification

of Disease, Ninth Revision (World Health

Organization, Geneva) codes for asthma

(493.01-493.99). Only data for patients

under the age of 18 who lived in a 36-zip

Address correspondence to J.Q. Koenig,

Department of Environmental Health, 357234,

University of Washington, 561A Health Sciences

Building, Room 7, PO Box 357234, Seattle, WA

98195-7234 USA. Telephone: (206) 543-2026. Fax:

(206) 685-3990. E-mail: jkoenig@u.washington.edu

We thank N. Maykut for providing technical

advice and the Puget Sound Air Pollution Control

Agency for continuing to support air pollution

health effects research in the Puget Sound region.

We also thank the participating hospitals for their

assistance, A. Park of the Seattle King County

Department of Public Health for the population

data, and D.V. Bates for helpful comments regarding the manuscript.

Funding for this research was from the Puget

Sound Air Pollution Control Agency, Seattle,

Washington, and in part from NIEHS Center grant

1 P30 ES 07033.

Received 16 November 1998; accepted 26

February 1999.

Environmental Health Perspectives * Volume 107, Number 6, June 1999 489
Articles * Norris et al.

Figure 1. Map of study area showing the air pollution monitoring sites and the high and low emergency

department asthma utilization regions. Abbreviations: PM10, particulate matter < 10pm in aerodynamic

diameter; %s dry light scattering.

*Additional station that operated from February 1996 to January 1997.

code region were used. The zip code region

contained the six hospitals and was limited

to an area between the air monitoring stations to the north and south of the central

area identified in the hospitalization survey

(Figure 1). The 36 zip codes in that region

were divided into a high and low utilization

density based on the childhood ED visits for

asthma. The top 20th percentile zip codes

were designated as the high ED asthma utilization areas (seven zip codes). The rest of

the zip codes were designated as the low ED

asthma utilization areas (29 zip codes). The

association between air pollution and ED

asthma visits was evaluated for the entire

study region (36 zip codes), as well as separately for the high and low ED utilization

regions (Figure 1).

Atmospheric data. Air quality data were

obtained from the Puget Sound Air

Pollution Control Agency (Seattle, WA)

and the state of Washington Department of

Ecology (WDOE, Olympia, WA). Daily

PMIO and dry light scattering data (aQp)

were available at three sites-north, central,

and south Seattle. Dry light scattering coefficient obtained from an integrating nephelometer most efficiently measures particles

between 0.2 and 0.9 pim (27), which makes

it a measure of the concentration of PM < 1

pm. Carbon monoxide (CO) values were

obtained from four sites within the Seattle

study region. Sulfur dioxide (SO2) concentrations were measured at one site in central

Seattle. Nitrogen dioxide (NO2) and ozone

data were obtained from the WDOE for a

site in central Seattle and at a site 20 km

east of Seattle, respectively. Because the

health data collected for this study only

covered one ozone season (April-October)

there were sufficient ozone data for our

model. In addition, the photochemical belt

where ozone concentrations are measured is

20 km east of the area where children in

this study resided.

For pollutants measured at multiple sites,

a daily arithmetic average was calculated and

used in the time-series analyses. The appropriate averaging time for each pollutant was

based on national and state air quality standards. A 24-hr average was used for measures

of PM. A 1-hr average was used for S02 to

reflect the WDOE 1-hr standard of 400 ppb

for SO2. Because there is only an annual

National Ambient Air Quality Standard

(NAAQS) for NO2, we selected the daily

maximum 1 hr and daily average concentration for this pollutant. A number of studies

have used the daily maximum 1 hr NO,

concentration for time-series analyses

(14,15,28). We assumed that CO was a general indicator of the build up of air pollution

and we used an averaging time that matched

the PM measurements (24 hr). Dew point

temperature and average daily temperature

data were collected from the Seattle Tacoma

International Airport by the National

Oceanic and Atmospheric Administration

National Climatic Data Center (Figure 1);

24-hr averages were used for these meteorologic variables.

Statistical analysis. The ED visits for

asthma were regressed on predictor and

confounding variables using a semiparametric Poisson regression model, a method of

choice in recent studies (20,29). All analyses

were conducted with the S-PLUS statistical

package (StatSci, Seattle, WA) using a generalized additive model (30). Base models

were first constructed that adjusted for

potential confounding factors using day-ofweek indicator variables, smooth functions

for time trends, temperature, and dew point

temperature. The smooth function for time

trends used a smoothing spline (31) that

was approximately equivalent to a 2-month

moving average. The degrees of freedom for

the smoothing splines for temperature and

dew point temperature were selected based

on minimizing the degree of freedom

adjusted deviance (32). After the base models were created for each of the three utilization areas (high, low, and entire area), the

air pollution exposure variables were evaluated by adding them individually into the

model. The final models were evaluated for

overdispersion and autocorrelation.

Additionally, the assumption of a linear

dose response was evaluated using a smooth

function.

490 Volume 107, Number 6, June 1999 * Environmental Health Perspectives
Articles * Asthma and fine particles

The ED visits for asthma were assumed

to be precipitated by either the same-day

air pollution or air pollution levels up to 4

days before the visit (0- to 4-day lags).

These lag times are consistent with that

reported by Canny and colleagues (33)

who found 84% of the asthmatic children

had symptoms for 72 hr or less prior to

arrival to the ED.

Results

Table 1 shows a summary of pollutant concentrations in this study. Table 2 summarizes the correlations between the exposure

variables that were used. The PMIO and

light-scattering measurements were highly

correlated (r = 0.82). CO was also correlated with these PM measurements, but not

with NO2 or SO2. An additional nephelometer was placed in south Seattle prior

to this study to determine whether the

inner city area fine PM values correlated

with the other fixed PM monitors (Figure

1). The light-scattering measurements from

the inner city monitor were highly correlated with the other monitors in the network,

with a correlation coefficients ranging from

0.75 to 0.85.

The average number of ED visits for

asthma in our study for children < 18 years

of age was 1.8 per day, with a maximum of

nine visits on any day. This number is low

because we restricted the study area to the

inner city and surrounding areas. The age

distribution of the ED visits for asthma in

children < 18 years of age is shown in Table

3. The majority of the ED visits were for

children younger than 5 years of age. This

age group accounted for 55 and 54% of the

asthma visits for the high and low utilization

regions, respectively. When comparing asthma ED visits between the two study areas,

the high utilization area accounted for 41%

of all the asthma ED visits (seven zip codes).

The association between air pollution

and increased ED visits for asthma was evaluated in the three utilization regions.

Significant associations between increased

ED visits for asthma and air pollution were

found across the utilization regions (Table

4). Relative rates were calculated for an

interquartile range (IQR; 75-25th percentile) increase in pollutant concentration.

Fine particles measured as light-scattering

coefficient (a) were significantly associated

with increase ED asthma visits in children

from all three zip code areas with relative

rate increases ranging from 1.13 to 1.16

across the study regions. PMIo and CO had

similar relative rates over the study regions

and were significandy associated with ED visits for asthma in the low utilization area and

in the total study area. The daily 1-hr maximum SO2 and NO2 were not significantly

Table 1. Summary of air pollutants and health end points (1 September 1995 to 31 December 1996).

Variable Mean SD Minimum Maximum Missing (%)

Meteorology

Temperature (°F), daily average 52.0 10.6 1.0 80.0 0.0

Dew point (°F), daily average 43.5 9.2 7.5 62.0 0.0

Air pollutants

PM1 (pg/M3), three sites, daily average 21.7 10.0 8.0 69.3 0.0

a m-1), three sites, daily average 0.4a 0.3 0.1 2.7 0.0

S& (ppb), maximum 1-hr 16.0 14.0 2.0 84.0 2.5

S02 (ppb), daily average 6.0 3.0 1.0 21.0 2.5

NO2(ppb), maximum 1-hrb 34.0 11.3 8.0 94.0 2.0

N02 (ppb), daily averageb 20.2 7.1 5.0 47.0 2.0

Average CO (ppm), daily average 1.6 0.5 0.6 4.1 0.0

03(ppb), daily maximum 8-hr 30.4 14.9 2.5 83.1 45.0

Health end points (daily ED visits)

High utilization 0.8 0.8 0.0 5.0 0.0

Low utilization 1.1 1.2 0.0 7.0 0.0

Total 1.8 1.6 0.0 9.0 0.0

Abbreviations: CO, carbon monoxide; ED, emergency department, NO2, nitrogen dioxide; 03, ozone; PM10, particulate

matter < 10 pm in aerodynamic diameter; SD, standard deviation; SO2, sulfur dioxide; a,pI dry light scattering.

"Approximately 12 pg/m3 fine PM (<2.5 pm). bJanuary 1995 to December 1996.

Table 2. Bivariate correlation among exposure variables (1 September 1995 to 31 December 1996).

CO8 PM10ab aSpa No2c NO2a so2c S02a

Coa 1.00 0.74 0.74 0.47 0.66 0.15 0.32

PM10a.b 1.00 0.82 0.56 0.66 0.24 0.43

a 1.00 0.41 0.59 0.19 0.34

N82Cc1.00 0.85 0.22 0.37

NO2a 1.00 0.45 0.25

SO2C 1.00 0.82

so2a 1.00

Abbreviations: CO, carbon monoxide; NO2, nitrogen dioxide; PM10, particulate matter < 10 pm in aerodynamic diameter;

SO2, sulfur dioxide; a,,, dry light scattering.

'Daily average. bLight scattering. cDaily maximum 1-hr. Table 3. Emergency department visits (%) for asthassociated with an increase in ED visits for

asthma for any of the study areas. The association between ED visits for asthma and

ozone was only determined for the entire

study area because of the large number of

missing data (45% of the days). Ozone was

not significantly associated with ED visits

for asthma; the relative rate was 1.02 [95%

confidence interval (CI), 0.98-1.05] for an

IQR of 4.6 ppb in the maximum daily running 8-hr average.

Multipollutant models containing either

PM measurement (light scattering or

PM10), SO2, and NO2 were also analyzed

for the entire study region using a 1-day lag.

CO was excluded from the multipollutant

model because it was assumed to be a surrogate for stagnant conditions. The relative

rate for light scattering and PMIO remained

significant with relative rates of 1.17 (CI,

1.08-1.26) and 1.14 (CI, 1.04-1.26),

whereas the SO2 and NO2 terms were not

significantly associated with an increase in

ED visits for asthma (data not shown).

The highest PM concentrations were

observed during the winter heating season, as

was seen in three earlier studies in Seattle

(8,12,2() (Figure 2). Initial analyses showed

that 24 and 25 December 1995 were influential data points in the regression because of

the high measured PM concentrations. To

ma by age group and utilization area (September

1995-December 1996).

Utilization, All

Age (years) Highb Lowc zip codes

< 5 204 ( 55) 287 (54) 491 (55)

5-11 117(32) 172(33) 289(32)

12-17 50 (13) 70 (13) 120 (13)

Total 371 (100) 529 (100) 900 (100)

Daily visits 0.8 1.1 1.8

(average)

Utilizationa 23 8 11

(average)

'Utilization/day/10,000 population. bSeven zip codes. E1wenty-nine zip codes.

assess the impact of these data, we repeated

the analyses with and without these 2 days to

evaluate the effect of these atypically high

PM concentrations (Figure 2). The relative

rates for increased ED visits for asthma in the

entire study area induding and excluding 24

and 25 December were 1.11 (CI, 1.02-1.20)

and 1.14 (CI, 1.05-1.24), respectively, for

an 11.5-pg/m3 increase in PM1O. Relative

rates for inclusion and exclusion of those

dates for associations with light scattering

were 1.10 (CI, 1.04-1.17) and 1.15 (CI,

1.07-1.23) for an IQR increase. These

results show the relative rates remained significant with the indusion of the influential

dates, but the relative rate for light scattering

decreased 5% with the indusion of the two

Environmental Health Perspectives * Volume 107, Number 6, June 1999 491
Articles * Norris et al.

Table 4. Relative rates between emergency department visits for asthma for an IQR increase in pollutant

concentration.

Pollutant IQR High Low All

a a,b 0.3 (m-/104)b 1.13 (1.02-1.24) 1.16 (1.06-1.27) 1.15 (1.08-1.23) piOaa 11.6 pg/M3 1.11 (0.98-1.25) 1.14 (1.02-1.27) 1.14 (1.05-1.23)

coa 0.6 ppm 1.04 (0.93-1.16) 1.15 (1.05-1.28) 1.10 (1.02-1.19)

NO2C 12 ppb 1.05 (0.96-1.14) 1.06 (0.99-1.14) 1.05 (0.99-1.12)d

NO2a 9 ppb 0.88 (0.76-1.02) 1.10 (0.97-1.24) 0.99 (0.90-1.08)e

sO C 12 ppb 0.99 (0.89-1.10) 1.09 (1.00-1.19) 1.02 (0.95-1.09)d so2a 3 ppb 0.92 (0.83-1.03) 1.09 (1.00-1.19) 0.97 (0.91-1.04)e

Abbreviations: CO, carbon monoxide; IQR, interquartile range; NO2, nitrogen dioxide; PM10, particulate matter < 10 pm in

aerodynamic diameter; SO2, sulfur dioxide; osp dry light scattering. All associations are reported for the 1-day lag except

those stated otherwise.

&Daily average. bApproximately 9.5 pg/M3 of fine PM 1< 2.5 pm). cDaily maximum 1-hr. dZero-day lag. "Two-day lag.

70

60

so

.fi 40

0

U0

'p20

10

0lp -PP 0,M 45, IP' RI 0P \ /94

,C , 4,t <> 4,1 %0 '4% '4/4% 0/s

Date

Figure 2. Time series of PM10 and PM25 data. Abbreviations: PM25, particulate matter < 2.5 pm in aerodynamic diameter; PM10, particulate matter < 10 pm in aerodynamic diameter.

&Estimated from light-scattering data.

high concentration points. Additionally, the

inclusion of 24 and 25 December 1995

caused the dose-response relationships for

light scattering to show nonlinear behavior

above values approximately equal to 40

pg/m3 PM25. Because these 2 days were holidays, the use of the ED visits for asthma

may have been different from other days in

the study. Based on the high influence and a

potential holiday effect, these 2 days were

not included in our primary analysis.

Discussion

We found significant associations between ED

visits for asthma in children and PM1O, lightscattering measurement of fine PM, and CO.

Light scattering, which is a measure of fine PM

primarily < 1.0 pm in diameter, was significandy associated with ED visits for asthma in

all the analyses. Additionally, PM1O and CO

were significant predictors of ED asthma visits

in the low utilization and for the combined

utilization areas. The higher relative risk in this

study as compared to the earlier results by

Schwartz and colleagues (12 may be due to

the fact that our population was restricted to

individuals under the age of 18, a more susceptible group than the population at large.

We recognize that ED data from hospitals contain some misdiagnoses. Delfino and

associates (34) found a good association

between hospital summary data and chart

review. The majority of cases in our study

came from a single children's hospital specializing in diagnoses for childhood diseases.

The number of visits for the high utilization region (371) was less than the low utilization area (529) and likely did not achieve

statistical significance for PM1O and CO

because of the reduced number of events.

Otherwise, the high and low utilization areas

did not appear to have different associations

between air pollution and increased ED visits

for asthma. The estimated numbers of children under 18 years of age in the high utilization area and in the rest of Seattle were

6,921 and 100,895, respectively. However,

we cannot compare the absolute number of

visits for the two utilization areas because a

given relative rate increase in ED visits in the

high utilization area caused more absolute

visits than in the low utilization area.

In this study, relative rates for light

scattering in the low and high utilization

areas were 13 and 16% for an IQR increase

of approximately 10 pg/m3 of fine PM.

The light-scattering IQR was converted to

represent PM2 5 gravimetric mass based on

colocated nephelometer and PM2.5 monitors at the southernmost PM monitoring

site (224 days, r= 0.86). The average concentration for the 15-month period of this

study was approximately 12 [ig/m3 PM2 5'

a concentration below the new EPA annual

standard (15 pg/m3). PM l0 was associated

with a 14% increase in ED asthma visits

for an increase of 12 pg/m3 PM10.

The association between increased asthma visits and CO was investigated in

Anchorage, Alaska (3X), Reno, Nevada (36),

and Seattle (20). The Anchorage and Reno

studies did not find a significant association

between ED visits for asthma and CO; however, CO was associated with hospital admissions in Seattle (20). The Reno study used

the highest hourly maximum level in their

local air pollution network, and the

Anchorage study used the daily average 8-hr

maximum concentration during winter

months. The Seattle hospital admission study

(20) used the daily average of four monitoring stations. In the present study, we used a

24-hr average of four sites in our study region

for the entire 15 months. Because CO has no

biologically plausible mechanism for the exacerbation of asthma (3/) we interpret it as a

general indicator of air pollution. The significant association between increased ED visits

for asthma and CO found in this analysis

could result from the high correlation

between CO and PMIO (0.74) as well as light

scattering (0.74). To explore this possibility, a

factor analysis of the physical and chemical

nature of air pollution in Seattle was conducted on both the CO and particulate composition data collected previously (38) at the

southernmost PM site in this study. Factor

analysis with a varimax rotation has been

used to both examine the colinearity among

the various air pollutant variables and to identify important features of the variability in

these pollutants (Table 5) (39,40). Main et al.

(38) measured the composition of PM2.s or

fine soil and coarse PM (PM10-PM2.5).

Coarse and fine soil mass were reconstructed

by adding the mass of the oxides of soil

species (Si, Ca, Fe, and Ti) (41). Using these

data we derived three factors which explained

95% of the variance and show that the variability in PM2.5 composition is influenced by

three factors: a) incomplete combustion

products consisting of CO, elemental carbon,

organic carbon, and soluble potassium (wood

smoke marker); b) secondary aerosols consisting of ammonium and sulfate; and c) fine

and coarse soil (Table 5). The light scattering

492 Volume 107, Number 6, June 1999 * Environmental Health Perspectives
Articles * Asthma and fine particles

Table 5. Loadings from a factor analysis of the air

pollution concentrations.a

Factor 1 Factor 2 Factor 3

Percent of variance 55.2 25.2 15.2

Carbon monoxide 0.85 - -

Elemental carbon 0.93 - -

Organic carbon 0.95 - -

Soluble potassium 0.96 - -

Ammonium - 0.94 -

Sulfate - 0.96 -

Fine soil - - 1.00

Coarse soil - - 1.00

kLoadings < 0.5 are shown as dashes I-).

and PMIO data are correlated with the first

factor scores, with correlation coefficients of

0.81 and 0.77, respectively. Note that CO is

associated with the buildup of incomplete

combustion-derived products induding particulate organic carbon and particulate elemental carbon.

This study did not find a significant association between NO2, SO2, or ozone and

increased ED asthma visits. The NO2, SO2,

and ozone measurements are only taken at

one site in the network and may be subject

to a greater degree of exposure misclassification than the CO and PM pollutant measures that represent averages of three to four

sites in the study area. In addition, the SO2

concentrations were low, with a mean daily

1-hr maximum concentration of 6 ppb.

Other investigators have reported that the

peak hourly NO2 concentration was significantly associated with increased ED visits for

asthma during winter months in Northern

California (15) and with increased ED visits

and respiratory admissions in Athens,

Greece (28). Peak hourly NO2 was also

associated with increased ED visits for asthma during both the winter and summer

months in Barcelona, Spain (14). The average peak NO2 concentrations in the

Northern California, Athens, and Barcelona

studies were 69 ppb, 50 ppb, and 58 ppb,

respectively. These concentrations are significantly higher than the average peak hourly

NO2 concentration of 35 ppb observed in

this study. Our finding of no significant

association between ED asthma visits and

maximum hourly SO2 is consistent with the

earlier Seattle study (12) and the study in

Barcelona (14). The lack of a significant

association between ED visits for asthma and

ozone in this study may be due to the large

number of missing measurements.

In summary, this study found a small but

significant association between air pollution

and increased ED visits for asthma in children in Seattle. PM and CO concentrations

in this study were associated with increased

childhood ED visits for asthma and represent

the daily variation in incomplete combustion

products including elemental carbon and

organic carbon. Results from this study of a

susceptible subpopulation show significant

increases of ED asthma visits for children with

daily PM2.5 concentrations substantially below

the newly adopted National Ambient Air

Quality Standard of 15 1Ig/m annually (42).

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