Articles



# An 

# Association between Fine Particles and Asthma Emergency Department 

# Visits for 

# Children in 

# Seattle

GaryNorris,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 

### themost 

### common chronic illness 

### ofchildhood and its

### prevalence is 

### increasing, caus

### ing

### much

### concer for identification ofrisk

### factors suchas 

### airpollution. We

### previouslyconducteda

### studyshowinga

### relationship betweenasthmavisits inallpersons < 65 years

### ofageto emer

### gency

### departments (EDs) and air

### pollution in

### Seattle, Washington. In that 

### studythe most 

### frequent zip codes of the visits were in 

### the inner 

### city. The 

### Seattle-King 

### County Department of Public Health 

### (Seattle, WA) 

### subsequendypublished areportwhich showedthat thehospitalization rate forchildren in the inner

### citywas over 

### 600/100,000, whereas itwas 

### c 

### 100,100,000 for children 

### living in 

### the suburbs. 

### Therefore, we 

### conducted the 

### present studyto evaluate whether 

### asthmavisits to

### hospital emergency

### departments inthe inner

### cityofSeattlewere

### associatedwith outdoor air 

### pollution 

### levels. ED 

### visits to six 

### hospitals for 

### asthma and 

### dailyair

### pollution data

were obtained for 15 

### months during 1995 and 

### 1996. 

### The association

### betweenair

### pollutionand 

### childhood ED visits 

### for asthma 

### from the inner

### cityareawith

### high asthma

### hospitalization rates

were 

### comparedwiththose from lower

### hospital utlizationareas. 

### DailyED counts were

### regressed against fine

### particulate matter 

### (PM), carbonmonoxide 

### (CO), sulfur

### dioxide, andnitrogen dioxide using a 

### semiparametric 

### Poisson7regression 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
PMwasassociatedwitharelative rate of 

### 1.15 [95% confidence 

### interval (CI), 1.08-1.23]. There

wasno strongerassociationbetweenEDvisits 

### forasthmaandair

### poilutionin

### thehigherhospital utilization area 

### than in the 

### l6wer utilization area. These 

### findings were seenwhen estimated 

### PM25 concentrations were 

### below the 

### newly adopted annual National Ambient Air 

### Quality 

### Standard of 

### 15 

### pg/m3. Keywords. air

### pollution, asthma, carbon

### monoxide, 

### children, emergency 

### departments, nitrogendioxide, particulatematter. EnvironHealth

### 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 

### ofage 

### 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 

### ofasthma 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 stan

### dards. In 

### Seattle, three studies have been 

### conducted that found associations 

### between the exacerbation 

### of asthma 

### and airborne 

### particulate matter 

### (PM). A 

### study conduct

### ed 

### 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 

### offine PM 

### (8). Another 

### study car

### ried out in 

### Seattle from 1989 to 

### 1990 

### found 

### significant associations 

### between 

### both
PM < 10 

### pm in 

### aerodynamic diameter 

## (PM0O) and 

### light-scatteringvalues 

### (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, resus

### pended road 

### dust, and 

### industry (23). A

source 

### apportionment 

### study of

## PMIO in 

### Seattle in 

### 1991-1992 

### found that 

### during the

### heatingseason, 80% 

### ofPM in

### residential

areas 

### originated from

### wood-burningdevices (24). Concentrations 

### ofallmeasuredair

### pol

### lutants have been decreasing in 

### Seattle over 

### the lastdecade 

### (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 sur

### rounding 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 

### otherdistricts 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 

### foundthat therewas not. 

## Methods
Healthdata 

### DailyEDvisits 

### forasthmawere 

### 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 com

### piledbased on 

### the 

### International

### Classification 

### ofDisease, 

### 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 ofWashington, 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 regardingthemanuscript. Funding for this research was from the Puget Sound Air Pollution Control Agency, Seattle, Washington, and inpart fromNIEHS Centergrant 1 P30 ES 07033. Received 16 November 1998; accepted 26 February 1999.

Environmental Health Perspectives * Volume 107, Number 6, June 1999 

### 489Articles * 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 drylight 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 

### densitybased on 

### the 

### childhood ED visits 

### for asthma. The top 

### 20th 

### percentile zip 

### codes

were 

### designated as 

### the 

### high ED 

### asthma uti

### lization 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 

### neph

### elometer most 

### efficiently measures 

### particles between 0.2 

### and 0.9

## pim (27), which makes it ameasure 

### ofthe concentration 

### ofPM < 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 

### studyresided. For

### pollutants measured at

### multiple sites,

a 

### dailyarithmetic 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 

### averagewas used formeasures of PM. A 1-hr average was used for 

### S02 to 

### reflect theWDOE 1-hr standard of400 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 gen

### eral 

### indicator ofthe 

### build up 

### ofair

### pollution 

### andwe 

### used an averaging time 

### that matched 

### the PM measurements 

### (24 hr). Dew point temperature 

### and average 

### daily temperature 

### datawere 

### 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 meteoro

### logicvariables. Statistical 

### analysis. The ED visits for 

### asthma were 

### regressed on 

### predictor and 

### confoundingvariables 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 gen

### eralized 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 dewpoint temperature. 

### The smooth 

### function for time 

### trends used a 

### smoothing spline (31) 

### that

was 

### approximately equivalent to a 

### 2-month moving average. 

### The degrees 

### offreedom 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 

### ofthe three utilization areas 

### (high, low, and entire area), 

### the air 

### pollution exposure 

### variables were 

### evalu

### ated 

### 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.

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



### The ED visits 

### for asthmawere 

### 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

### ofpollutant 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-scatteringmeasurements 

### from 

### the inner 

### citymonitorwere

### highlycorrelatedwith 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 

### ofage 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 

### ofage 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% ofthe 

### asthmavisits 

### forthe

### highandlow

### utilization regions, 

### respectively. When comparing

### asthma 

### ED visits 

### between the two 

### study areas, 

### the 

### high utilization area 

### accounted for 

### 41% ofall theasthma ED visits 

### (sevenzip

### codes). The association 

### between air 

### pollution and 

### increased EDvisits 

### forasthmawas 

### 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 per

### centile) increase in

### pollutant concentration. Fine 

### particles measured as 

### light-scattering coefficient 

### (a) were

### significantlyassociated with increase ED 

### asthmavisits in 

### children 

### from all three zip 

### code areas 

### with 

### relative
rate increases 

### ranging 

### from 1.13 to 

### 1.16

across 

### the 

### studyregions. 

## PMIo and CO 

### had similar relative rates over 

### the 

### study regions andwere

### significandyassociatedwith EDvisits 

### for asthma in 

### the lowutilization area 

### and in 

### the 

### total 

### study area. 

### The 

### daily 1-hr maxi

### mum SO2 and NO2 were not 

### significantly Table 1. Summaryofairpollutants and health end points (1 September 1995to 31 December 1996).

Variable Mean SD Minimum Maximum Missing (%) Meteorology Temperature (°F), dailyaverage 52.0 10.6 1.0 80.0 0.0 Dewpoint (°F), dailyaverage 43.5 9.2 7.5 62.0 0.0 Airpollutants PM1 (pg/M3), three sites, dailyaverage 21.7 10.0 8.0 69.3 0.0

a 

### m-1), threesites, dailyaverage 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), dailyaverage 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), dailyaverageb 20.2 7.1 5.0 47.0 2.0 Average CO (ppm), dailyaverage 1.6 0.5 0.6 4.1 0.0 03(ppb), dailymaximum8-hr 30.4 14.9 2.5 83.1 45.0 Health endpoints (dailyEDvisits) Highutilization 0.8 0.8 0.0 5.0 0.0 Lowutilization 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 < 10pm in aerodynamic diameter; SD, standarddeviation; SO2, sulfurdioxide;

### a,pI drylightscattering. "Approximately 12pg/m3fine PM

### (<2.5pm). bJanuary 1995to December 1996.

Table2. Bivariate correlation among exposurevariables (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 

### N82C c1.00 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, sulfurdioxide;

### a,,, drylightscattering. 'Dailyaverage. bLightscattering. cDailymaximum 1-hr. Table 3. Emergency departmentvisits (%) for asth

### associated with an 

### increase in ED visits 

### for asthma forany

### ofthe 

### studyareas. 

### 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% 

### ofthe 

### days). Ozonewas 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

### of4.6 

### ppb in 

### themaximum

### daily

### running 

### 8-hraverage. 

### Multipollutantmodels containing

### either
PM measurement 

### (light scattering or 

### PM10), SO2, and NO2 were 

### also 

### analyzed forthe entire 

### studyregion usinga 

### 1-daylag. CO was 

### excluded from the 

### multipollutant model because itwas 

### assumed to

### be a surro

### gate 

### 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
EDvisits 

### forasthma 

### (datanot 

### shown). The 

### highest PM concentrations were 

### observed

### duringthewinter

### 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 

### influentialdatapoints in 

### the

### regressionbecause

### 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) Dailyvisits 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

### analyseswith

### andwithout these 2 

### days to 

### evaluate the 

### effect of these 

### atypically high
PM 

### concentrations 

### (Figure 2). The 

### relative
rates 

### forincreasedEDvisits

### forasthma in

### the entire 

### studyarea 

### indudingand

### excluding 24 

### and25 

### Decemberwere 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 491Articles * 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) 

## piOa
a 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, sulfurdioxide;

### osp dry lightscattering. All associations are reported forthe 1-daylag except those stated otherwise. &Daily average. bApproximately 9.5pg/M3 of fine PM 1< 2.5pm). cDaily maximum 1-hr. dZero-day lag. "Two-day lag.

70

60

so

.fi 40

0 U0 'p20

10



### 0lp -PP M 

### 45,

0, 

### 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 

### dayswere 

### hol

### idays, 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 isameasure

### offine PM 

### primarily < 1.0 

### pm in 

### diameter, was 

### signifi

### candy associated with ED visits 

### for asthma in 

### all the 

### analyses. Additionally, 

## PM1O and CO

were 

### significant predictors ofED asthmavisits 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 theage 

### of 18, amore suscep

### tiblegroup 

### than 

### thepopulation 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 spe

### cializing in

### diagnoses for

### childhooddiseases. The number 

### ofvisits 

### for the

### high utilization 

### region 

### (371) was 

### less 

### than the low uti

### lization area 

### (529) and 

### likelydid not 

### achieve 

### statistical 

### significance for 

### PM1O and CO 

### because of the 

### reduced number 

### of events. 

### Otherwise, the

### high and lowutilization areas 

### did not appear to 

### have different associations 

### between air

### pollutionand increased EDvisits 

### for asthma. The 

### estimated numbers of chil

### dren under 18 years 

### of age in 

### the 

### high uti

### lization 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 EDvisits 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 monitor

### ing stations. In the 

### present 

### study, we used a 24-hr

### average offour sites in our 

### studyregion for the entire 15 months. Because CO has no biologicallyplausiblemechanism for the exacerbation of asthma 

### (3/) we interpret it as a 

### general indicator ofair

### 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 

### ofair

### pollution in

### Seattlewas 

### 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 thevarious airpollutantvariables and to iden

### tify important features of the variability in 

### thesepollutants (Table 

### 5) (39,40). Main etal. 

### (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 

### datawe 

### derived three factors which explained 95% of the variance and show that the vari

### ability in

## PM2.5 composition is 

### influenced

### by three 

### factors: a) 

### incomplete combustion 

### products consisting

### ofCO, 

### 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

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

### particles

Table 5. Loadings from a factor analysis ofthe air pollution concentrations.a

Factor 1 Factor 2 Factor 3

Percent ofvariance 55.2 25.2 15.2 Carbonmonoxide 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 ele

### mental carbon. 

### This 

### studydidnot 

### finda

### significantassociation 

### between NO2, SO2, or ozone 

### and increased ED 

### asthmavisits. The 

### NO2, 

### SO2, and ozone measurements are 

### only taken at

one 

### site in 

### the 

### network and may

### be subject
to a 

### greater 

### degree ofexposure 

### misclassification 

### than the CO 

### and PM 

### pollutant measures 

### that represent averages 

### ofthree 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

### hourlyNO2 concentration was 

### significantly

### associatedwith 

### 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 

### associatedwith 

### increased ED visits 

### for asthma 

### during both the winter 

### and summer 

### months in 

### Barcelona, Spain 

### (14). The aver

### age 

### 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 EDvisits 

### forasthma

### and

ozone in 

### this 

### studymay be due to 

### the 

### large number

### ofmissingmeasurements. In

### summary, this 

### studyfounda

### 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 EDvisits 

### forasthmaand

### represent the

### dailyvariation in 

### incomplete combustion products including 

### elemental carbon and 

### organic 

### carbon. Results 

### from this 

### study ofa 

### susceptible 

### subpopulation show significant increases ofED asthma

### visits for childrenwith daily

# PM2.5 concentrations substantiallybelow the 

### newly adopted National Ambient Air 

### QualityStandardof15 

### 1Ig/mannually 

### (42).

REFERENCES AND 

### NOTES

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