# Acute Effects of SummerAir Pollution on Respiratory Health ofAsthmatic Children

MARLEEN H. GIELEN, SASKIA 

### C. van der ZEE, JOOP H. van WIJNEN, CLAUDIA 

# J. van STEEN, and BERT BRUNEKREEF Department of Epidemiology and Public Health, Agricultural University ofWageningen, the Netherlands; Department of Health Risk Analysis and Toxicology, University of Maastricht, Maastricht, The Netherlands; and Department of Environmental Medicine, Municipal Health Service Amsterdam, The Netherlands



### In the early summerof 1995, the acute respiratory effects of ambient air pollution were studied in a panel of 61 children, ages 7 to 13 yr, of whom 77% were taki!'lg asthma medication. Peak flow was measuredtwice daily with MiniWright meters athome andthe occurrence ofacute respiratory symptoms and medication use was registered daily bythe parents in a diary. Exposure to air pollution was characterized by the ambient concentrations of ozone, PM 10, 

### and black smoke. During the study period, maximall-h ozone concentrations neverexceeded 130 i-Lg/m3

, 

### and 24-h black smoke and PM 10 

### concentrations were never higher than 41 and 60 i-Lg/m3 

### respectively. Associations of air pollution and health outcomes wereevaluated using time series analysis. After adjustingfor pollen, timetrend, and day ofthe week, black smoke in particular was associated with acute respiratory symptoms and with medication use. Less strong associations were found for PM 10 

### and ozone. These results suggest that in this panel of children, most of whom had asthma, relatively low levels of particulate matter and ozone in ambient air are able to increase symptoms and medication use. Gielen MH, van der Zee SC, 

### van Wijnen JH, van Steen 

## CJ, BrunekreefB. Acute effects of summer air pollution on respiratory health ofasthmatic Children. AMJ RESPIR CRIT CARE MED 1997;155:2105-2108.



### In recent years, effects ofozone (03) 

### and airborne particulate matter (PM) have beenobserved at ever lower levels ofexposure (1). Tostudythe combinedeffects oflow levels of0 3 

### and PM on asthmatic children living in an urban area, we conducted a panel study during late spring/early summer of 1995 in Amsterdam (720,000 

### inhabitants). In Amsterdam, longrange transportis the major source of air pollution. Local air pollution is caused primarily by emissions from traffic and only to a small degree by industry.
METHODS 

### Study Population Subjectswereselectedfrom two special schoolsfor chronically ill

### children. Most children(± 80%, according to the school board) visiting these schools suffer from respiratory disease. The schools attract children from Amsterdam and from nearby communities. In April1995, parents of all 190 children older than 6 yr were invited to let their child participate. Parents of 107 children responded, 79 positively. Of these 79 children, 77% had doctor-diagnosed asthma. The 28 children who did not get permission were not different from the 79 participating children with respect to asthma status, gender, and age. We were unable to collect information about the 83 children whose parents did not respond.

(Received in original form july25, 1996 and in revised form December 18, 7 996) Correspondence and requests for reprints should be addressed to B. Brunekreef, Ph.D, Professor of Environmental Health, Department of Epidemiology and Public Health, University of Wageningen, P.O. Box 238, 6700 AE, Wageningen, The Netherlands. Am J Respir Crit Care Med Vol. 155. pp. 2105-2108, 1997 

### Lung Function and Symptoms Participants were provided with MiniWright peak expiratory flow (PEF) meters anddaily diaries andinstructed ontheirproperuse during an information evening at school. Parents who could not attend

were individually instructed. PEFwas measured twice a day between April 26 and July 4, once before breakfast and once before going to bed, before medication was taken. Each time, three blows were recordedbytheparents in a diary. Thehighest ofthe three was used for analysis. The occurrence of acute respiratory symptoms and medicationuse was notedin thediary. Medication use was reportedbybrand

name and number ofunits taken. Five different medications could be noted. We also asked about fever and the amount oftime spent outdoors or outoftown. A questionnaire was administered onthe respiratory health history ofthe child, and on characteristics ofthe home. All parents completed an informed consent form. The study was approved by the medical ethics committee ofthe Municipal Health Service ofAmsterdam.



### Pollution aild Weather Data Ambientconcentrationsof particles 

### < 10 1-Lmindiameter (PM10) 

### and 0 3 

### were obtained from acity-background site ofthe NationalAir Quality Monitoring Network, located in Amsterdam North. PM10 

### was measured with a Sierra Anderson dichotomous sampler and 0 3 

### was measured by a chemiluminescent Philips PW 9771/00 continuous monitor (2). Black smoke (BS) data, measured according to (2) were obtained from monitors operated by the City of Amsterdam. BS data from three different sites were obtained: a street site (number 1); a street/ city background site (depending on wind direction, number 2), both located downtown; and a background site in NorthAmsterdam (number3). Temperaturewas obtained from Schiphol AmsterdamAirport. Pollen data were obtained from the University ofLeiden, 30 km to the southwestofAmsterdam, as collected with a Burkard sampler (3).

### 2106 AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE VOL. 155 1997

Statistical Analysis Eighteen children were notincluded in the final/analysis, because they completed less than 60% (i.e., 

### < 35 d) ofthe study period. Final analysis included 61 children, 41 from Amsterdam, 20 from nonindustrial communities within25 km ofAmsterdam. Statistical analysis was conducted using SAS (4). The first 3dofeachchild's diary wereremoved to allow for training effects and to allow children (and parents) to get used to the diaries. Furthermore, we removed the first 11 d of the study period,becausethenumberofparticipatingchildrenwas still increasing. Foreachchild, plots ofmorning andeveningPEFversus day of study were made to detect trends or irregularities. There were no children whose datawere considered unreliable. Theassociationbetweenairpollution and PEFwas evaluatedby individual multiple linear regression and subsequent evaluation of the individual regression slopes. Morning andeveningPEFwere analyzed separately. The 8-h maximal ozone concentration of the same day, previous day (lag 1 ), and2 d before (lag 2), and thesame-day,lag 1,lag2, and 5-d mean concentration ofPM10 and black smoke were used separately as independent variables. Pollen concentration (of the previous day), time trend(numberofdays since thestartofstudy),and a dummy for day of week (schoolday versus weekend/holiday) were included in theregressionmodels as potentialconfounders. Pollencountswere lognormally distributed, and were In-transformed before analysis. Pollen counts and temperature were highly correlated (r = 0.71). To avoid problems in the analysis related to collinearity, we only adjusted for pollen counts. Temperatures were moderate and showed a small range (7.9 to 22° C), therefore it did not seembiologically plausible that temperature would have a major influence on peak flow. We only used 

### Poaceae (grass pollen) counts, the major pollen in the study period. In thequestionnaire,52% ofthechildren had reportedgrass atopy. Because of the autocorrelative structure of the PEF data, we performed time series analysis based on the Box-Jenkins approach (5). To identify the degree of autocorrelation we used population mean deviations of the peak flow as described in the Utah Valley Studies (6). We evaluated the autocorrelation and partial autocorrelation function of the residuals using PROC ARIMA to idemify the model thatdescribed autocorrelation sufficiently. A first order AR model of the residuals resulted in uncorrelatedresiduals. The time series analysis was performed with PROC AUTO-REG, using the Yule-Walkerestimation method. Daily symptomprevalence
was calculated as the fraction ofchildren reportingsymptoms ormedication use, using data only from children with nonmissing diary information for each separate day. We clustered the symptoms runny/ stuffed nose and sore throat as upper respiratory symptoms (URS), and shortness of breath, wheeze, and attacks of shortness of breath with wheeze as lower respiratory symptoms (LRS). Because of the high prevalence ofacute respiratorysymptoms in thepanel, the distribution ofthe daily prevalence data did not significantly deviate from normality according tothe Shapiro-Wilk statistic. Therefore, the association was assessed using a linear model as well (PROC AUTOREG, using the Yule-Walker estimation method). Using the partial autocorrelation function ofthe residuals, a first autoregressive model
was identified for all symptoms and bronchodilatoruse. To facilitate interpretation of the results, associations have been expressed as the changein prevalence between the day with the highestand the day with the lowest airpollutionconcentration, relative to the mean prevalence during the study period. The same confounders
as described for peak flow analysis were included in the model. Time trend was included in the analysis as a linear, a quadratic, and a cubic term in order to adjust for trends with a longer periodicity, that were unlikely to result from airpollution. Medication use was divided into bronchodilator use (atrovent, beconase, berodual, berotec, bricanyl, flixotide, salbutamol, and ventolin), preventive ormaintenancemedication (becotide, fenistil, livocab, lomudal, phenergan, prednisone, promethazine, pulmicort, rhinocort. theolair, tinset, triludan, andzaditen), and other. Bronchodilator use was analyzed with both the prevalence and the daily mean deviation from the overall mean ofnumber ofpuffs as the dependent variable. To remove the effect of different groups of children contributing to the
mean on different days, the number ofpuffs taken by child ion a certain day was subtracted from themean numberofpuffs taken by child i (number of puffs

- mean number of puffs = deviation). For each dayofthe study, the mean ofall children was calculated (sum all deviations/number of children). In addition, analyses were performed excluding the children who had, during the study period, ever reported using bronchodilators ormaintenance/preventive medicatior;.


### RESULTS The prevalence of acute respiratory symptoms during the study period is shown in Table 1. 

### In Figure 1, the daily variation of the mean PEF and the prevalence of LRS is plotted. The day-to-day variation of the maximal 8-h ozone, and the 24-h average PM10 

### and BS (site 2) are shownfirst. A peakwas seen around Day 28 (May 23). A decrease of PEF was seen around Day 32 (May 27). Airpollutionlevels duringthe study period are shown in Table 2. The correlationbetween air pollutants was low to moderate, generally less than 0.3 for correlations between ozone and particles, and up to 0.65 for correlations between BS and PM10. 

### Theresults ofthe regression analyses are showninTable 3. Morning PEF had the strongest associations with the 2-d lag of ozone, and the 2-d lag and the 5-d mean of BS, site 1. 

### Evening PEF was related to the 2-d lag of ozone and the 1-d lag and the 5-d mean of BS (all sites). For PM10, 

### none of the associations with PEF reached statistical significance, although most coefficients were negative. Current day ozone was positively related to upper respiratory symptoms but not to other symptoms. The association of symptom prevalence with PM 10 

### was mostly positive, but only statistically significant for LRS and the 5-d mean PM10• 

### The 5-d mean BS was positively related to LRS, URS, eye irritation, and nocturnal awakenings. The 5-d mean BS was also strongly related to bronchodilator use (Table 4). The associations with BS were somewhat stronger among bronchodilator
users and children living in Amsterdam (results not shown).


### DISCUSSION The acute health effects of exposure to low concentrations of ozone, PM10, 

### and BS were investigated in a panel of 61 children. Significant decreases in peak flow were found, in the morning up to 4.2%, and in the evening up to 2.9%, over the range ofpollutant concentrations that occurred. Acute respiratory symptoms and bronchodilator use were also significantly increased when pollutant concentrations (especially BS) were elevated. For ozone, the effects estimated in this study were of the same order of magnitude as the effects found in earlier studies on lung function changes related to
ozone (1). However, in ourstudyeffects onlungfunction were most clearly seen at lag 2, suggesting a delayed effect possibly mediated by inflammatory changes that may have occurred in the airways. Removing all ozone concentrations ofmore than

TAE!LE 1 DESCRIPTION OF MEAN DAILY PREVALENCE (%) OF SYMPTOMS AND MEDICATION USE

Cough Woken up with breathing problems Eye irritation Upper respiratory symptoms

(runny/stuffed nose, sore throat) Lower respiratory symptoms

(shortness of breath, wheeze, asthma attacks) Bronchodilator use Maintenance/preventive medication use Mean (min-max)
34.8 (19.0-53.4) 13.7 (4.7-25.4) 11.2 (3.9-20.3) 45.8 (28.3-60.3)

20.7 (7.9-35.6)

38.7 (25.9-50.0) 56.6 (49.2-63.5)Brief,Communication

f.1g/m 370,--------r=================r--------. 60 50 40 30 20 10

**QL---------------------------~----~** 120

**.-----------~=========~-----------,** J-max.

**S~hour** ozone 100
80 60 40 20

0

**L---------------------------------~** % 40 

## ,----------------,==========,--------------, j-daily mean LRS 30

20

10

15 20 25 30 35 40 45 50 55 60 65 70 daynumber 

### Figure 1. Air pollution levels and respiratory symptoms during the study period (Amsterdam, May 9 through July 4, 1995).



### 100 Jl.g/m3 

### did not change the results, indicating that the associations apparentlypersist atlow concentrations. The results ofour study with respect to particulate matter
are comparable to those we found in an earlier study (7). In that study, an increase in same-day PM10 

### levels with 10 Jl.g/m3 

### corresponded with a decrease of0.14% of morning PEF and
an increase of the 7-d mean with 10 fl.g/m3 

### decreased the eveningPEFby 0.26%. We found a nonsignificant decrease of 0.52% per 10 Jl.g/m3 

### ofthe 5-d mean PM10• 

### For black smoke,
our previous study suggested a decrease of PEF associated

TABLE 2 AIR POLLUTION CONCENTRATIONS (f.l.g/m3) AND WEATHER DATA, AMSTERDAM, MAY 7-JULY 4, 1995*

Ozone, max 8-h value Ozone, max 1-h value PM 10, 24-h average N02, 24-h avereage Black smoke (site 1), 24-h average Black smoke (site 2), 24-h average Black smoke (site 3), 24-h average Grass pollen, no. ofgrains/m3 Temperature, 

### oc

• Number ofdays is 57 for all variables. Mean:±: SD
67.0 ::':: 14.9 77.3 ::':: 15.7 30.5 ::':: 10.1 29.8 ::':: 15.6 13.7 ::':: 7.0 13.6 ::':: 7.8 8.0 ::':: 3.9 58.1 ::':: 65.8 13.4 ::':: 3.5 Range
27.6-110.8

33-130 16.0-60.3 7.0-74.0

4-41 3-37 3-23 3-325 7.9-22.0 

### 2107

TABLE 3 ADJUSTED ASSOCIATION OF PEAK FLOW WITH OZONE AND PARTICULATE MATTER (POOLED INDIVIDUAL REGRESSION COEFFICIENTS AND 95% CONFIDENCE

INTERVALS IN % OF MEAN PEF OVER RANGE OF AIR POLLUTION CONCENTRATIONS)
Peak Expiratory Flow
Air Pollution Morning Evening
0 3, max 8 h, lag 0 1.85 (-0.35, 4.05)*

lag 1 -0.82 (-2.68, 1.04) -0.57 (-3.39, 2.67) lag 2 -1 .86 (- 3.58, -0.14)t -1.88 (-3.94, 0.18);
PM 10, lag 0 -0.07 (-2.21, 2.14)

lag 1 1.23 (-0.51' 2.97) -0.27 (-1.99, 1.45) lag 2 0.30 (-1.58, 2.18) -1.29 (-2.86, 0.28) 5-d mean -0.57 (- 3.10, 1.96) -2.06 (-4.75, 0.63) Black smoke, site '1

lag 0 0.70 (-1.48, 2.88) lag 1 -0.25 (-2.46, 1.96) -2.24 (-4.49, 0.01)* lag 2 -2.60 (-4.68, -0.52)§ -1.16 (-2.81, 0.49) 5-d mean -4.21 (-6.54, -1.88)§ -2.93 (-5.60, -0.26)1 Black smoke, site 2

lag 0 1.35 (-1.39, 4.09) lag 1 -0.69 (-3.14, 1.76) -2.24 (-4.47, -0.01)t lag 2 -2.10 (-4.15, 0.15)1 -1.30 (- 3.57, 0.97) 5-d mean -1.93 (-4.34, 0.48) -2.07 (-4.76, 0.62) Black smoke, site 3

lag 0 -0.07 (-1.91' 1.77) lag 1 -0.80 (-2.51, 0.91) -1 .94 (- 3.88, 0.00); lag 2 -0.24 (-2.20, 1.72) -1.00 (-2.86, 0.86) 5-d mean -1.36 (- 3.50, 0.78) -2.04 (-4.73, 0.65)

• Results would have no meaning because these data reflect PEF measured before measuring ofexposure started.

t p< 0.05. I p < 0.10.
§ p < O.Ql.


### with the same-day BS concentration of 0.13% per 10 fl.g/m3 

### end of0.31% per 10 Jl.g/m3 

### ofthe 7-d mean BS concentration. In the present study, the 5-d mean BS was associated with a maximal decrease of the morning peak flow of 1.13% per 10 fl.g/m3• 

### In a meta-analysis, Dockery and Pope (8) calculated a meandecreaseinPEFof0.08% per 10 Jl.g/m3

### in studiesofparticulatematter. We observed consistent effects ofparticulate air pollution
on respiratory health in this panel of mainly asthmatic children, despite the lowlevels ofair pollution. The asthma severity in our panel is reflected in the high percentage ofchildren recently treated for asthma (67%) and currentairway medication users (77%). Other studies that investigated particulate effectsonsymptomaticchildren have selectedrelativelyhealthy children with a low percentage ofmedication users (6, 7). The study indicates that more severely asthmatic children may be
more sensitive to particulate air pollution. Particulate air pollution may directly trigger asthma attacks in susceptible children, or may interact in a more complex way to increase airway responsiveness to virus orallergy triggers. Our study suggests that in an urban environment in the summer, BS was the most important air pollution indicator associated with acute changes in lung function, respiratory symptoms, and medication use in asthmatic children. BS monitored with the British smoke filter technique measures the blackness ofparticles with aerodynamic diameters ofless than about4.5 f.Lm (2). Underpresent-day conditions in theNetherlands with very little coal burning, BS can beconsidered as an indicator of fine black particles emitted by diesel engines. A number of recent studies suggest that PM2

_ 5 

### is more strongly associated with healthoutcome than PM 10 

### (9). Anotherexpla-2108 AMERICAN jOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE VOL 155 1997

TABLE 4 ADjUSTED ASSOCIATION OF ACUTE RESPIRATORY SYMPTOMS AND MEDICATION USE WITH OZONE AND PARTICULATE MATTER*
Prevalence Ratios (95% confidence interval)
Lower Respiratory Upper Respiratory Air Pollution Cough Woken Up Symptoms Symptoms Bronchodilators1

8-h 0 3 1ag 0 1.21 (0.94-1.48) 1.06 (0.67-1.45) 1.23 (1.03-1.43)§ 1.14(0.71-1.57)

lag 1 1.12 (0.83-1.41) 0.83 (0.30-1.36) 0.86 (0.45-1.27) 1.05 (0.83-1.27) 1.05 (0.58-1.52) lag 2 1.07 (0.87-1.27) 0.71 (0.40-1.02)* 1.1 3 (0.88-1 .38) 0.98 (0.84-1.12) 1.16 (0.87-1 .45) PM 10, lag 0 0.97 (0.73-1.21) 1.23 (0.92-1.54) 0.97 (0.79-1.15) 1.00 (0.65-1.35)

lag 1 1.17 (0.93-1.41) 0.84 (0.43-1 .25) 1.02 (0.69-1.35) 0.95 (0.77-1.13) 0.95 (0.58-1.32) lag 2 1.02 (0.80-1 .24) 1.07 (0.70-1.44) 1.04 (0.73-1.35) 1.04 (0.88-1.20) 1.45 (1.14-1.76)11 5-d mean 1.1 7 (0.82-1.52) 1.22 (0.65-1.79) 1.47 (1.00-1.94)* 1.12 (0.87-1.37) 1.61 (1.16-2.06)!1 Black smoke

site 2, lag 0 0.99 (0.68-1.30) 1.02(0.58-1.47) 1.06 (0.82-1 .30) 1.05 (0.56-1.54) lag 1 0.97 (0.68-1.26) 0.92 (0.41-1.43) 1.07 (0.66--"1.48) 0.98 (0.76-1.20) 1.11 (0.66-1.56) lag 2 1.14 (0.85-1.43) 0.97 (0.46-1.48) 1.01 (0.61-1.42) 1.16 (0.94-1.38) 1.69 (1.28-2.1

### Ol 5-d mean 1.58 (0.78-2.38) 2.27 (1.05-3.49)§ 2.30 (1.22-3.38)§ 1.66 (1.09-2.23)§ 2.57 (1.55-3.59)11

1 Change in mean population deviation ofbronchodilatoruse. • Prevalence ratios reflect difference overthe range ofair pollution, relative tothe mean prevalence.
t Results would have no meaning because these data reflect the awakening before measuring ofexposure started. *p<O.lO.
§ p <0.05. II p < 0.01.



### nation might be that BS is a better indicator of the chemical compounds causing health effects. The stronger associations found for BS in comparison to thosefound for PM 10 

### are in line with theresultsofa studyconductedonairpollution and daily mortality in the city of Amsterdam during 1986--1992 (10). Bothfor PM10

### and black smoke, the results for bothpeak flow and symptoms suggest that stronger associations are found when the 5-d lagged mean concentration is included in the model, compared with the same-day concentration. The finding of stronger associations with 5-d mean concentrations of particulates is in line with other studies (6) and suggests that changes in respiratory status may reflect cumulative exposure of several days prior to the measurement. In addition, 5-d
meanconcentrations might beless affectedby nonaccurate estimates in personal exposure than 24-h mean concentrations, since deviations are averaged out. Subjects using bronchodilators during the study period showed stronger associations between black smoke and acute changes in respiratory health indicators than subjects not using bronchodilators. This suggests that this subpopulationmay be more responsive to particulatematterairpollution thanthe subjects not using bronchodilators during the study period. Otherinvestigators (11) have found that the use ofbronchodilators increased, and (possibly as a result) lung function improved also with increasing air pollution concentrations. Such interactions do not seem to have occurred in ourstudy. In conclusion, our study suggests that respiratory health in asthmatic children attending school in the urban area ofAmsterdam was adversely affected by relatively low concentrations ofair pollutants, especially black smoke, during the summer of 1995. Acknowledgment The writers thank the schoolchildren, their parents and teachers and medical staff for their cooperation and support during a busy school term and Professor F. Spieksma for his help with the pollen data.

References

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