Acute Effects of Summer Air Pollution on Respiratory 

Health of Asthmatic 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 of Wageningen, 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 summer of 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 

measured twice daily with MiniWright meters at home and the occurrence of acute respiratory symptoms and medication use was registered daily by the parents in a diary. Exposure to air pollution was 

characterized by the ambient concentrations of ozone, PM10, and black smoke. During the study period, maximall-h ozone concentrations never exceeded 130 i-Lg/m3

, and 24-h black smoke and PM10 

concentrations were never higher than 41 and 60 i-Lg/m3 respectively. Associations of air pollution 

and health outcomes were evaluated using time series analysis. After adjusting for pollen, time trend, 

and day of the week, black smoke in particular was associated with acute respiratory symptoms and 

with medication use. Less strong associations were found for PM10 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, Brunekreef B. Acute effects of summer air pollution on 

respiratory health of asthmatic Children. AM J RESPIR CRIT CARE MED 1997;155:2105-2108. 

In recent years, effects of ozone (03) and airborne particulate 

matter (PM) have been observed at ever lower levels of exposure (1). To study the combined effects of low levels of 0 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 transport is 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 

Subjects were selected from two special schools for 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 july 25, 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 and daily diaries and instructed on their proper use during an information evening at school. Parents who could not attend 

were individually instructed. PEF was 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 recorded by the parents in a diary. The highest of the three was used for 

analysis. The occurrence of acute respiratory symptoms and medication use was noted in the diary. Medication use was reported by brand 

name and number of units taken. Five different medications could be 

noted. We also asked about fever and the amount of time spent outdoors or out of town. A questionnaire was administered on the respiratory health history of the child, and on characteristics of the home. 

All parents completed an informed consent form. The study was approved by the medical ethics committee of the Municipal Health Service of Amsterdam. 

Pollution aild Weather Data 

Ambient concentrations of particles < 10 1-Lm in diameter (PM10) and 0 3 

were obtained from a city-background site of the National Air 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 North Amsterdam (number 3). Temperature was obtained from Schiphol Amsterdam Airport. 

Pollen data were obtained from the University of Leiden, 30 km to the 

southwest of Amsterdam, as collected with a Burkard sampler (3). 
2106 AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE VOL. 155 1997 

Statistical Analysis 

Eighteen children were not included in the final/analysis, because they 

completed less than 60% (i.e., < 35 d) of the study period. Final analysis included 61 children, 41 from Amsterdam, 20 from nonindustrial 

communities within 25 km of Amsterdam. Statistical analysis was conducted using SAS (4). The first 3d of each child's diary were removed 

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, because the number of participating children was still increasing. For each child, plots of morning and evening PEF versus day 

of study were made to detect trends or irregularities. There were no 

children whose data were considered unreliable. 

The association between air pollution and PEF was evaluated by individual multiple linear regression and subsequent evaluation of the 

individual regression slopes. Morning and evening PEF were analyzed 

separately. The 8-h maximal ozone concentration of the same day, 

previous day (lag 1 ), and 2 d before (lag 2), and the same-day, lag 1, lag 2, 

and 5-d mean concentration of PM10 and black smoke were used separately as independent variables. Pollen concentration (of the previous 

day), time trend (number of days since the start of study), and a dummy 

for day of week (schoolday versus weekend/holiday) were included in 

the regression models as potential confounders. Pollen counts were 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 seem biologically 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 

the questionnaire, 52% of the children had reported grass 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 

that described autocorrelation sufficiently. A first order AR model of 

the residuals resulted in uncorrelated residuals. 

The time series analysis was performed with PROC AUTO-REG, 

using the Yule-Walker estimation method. Daily symptom prevalence 

was calculated as the fraction of children reporting symptoms or medication 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 of acute respiratory symptoms in the panel, the distribution of the daily prevalence data did not significantly deviate from 

normality according to the 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 of the residuals, a first autoregressive model 

was identified for all symptoms and bronchodilator use. 

To facilitate interpretation of the results, associations have been 

expressed as the change in prevalence between the day with the highest and the day with the lowest air pollution concentration, 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 air pollution. 

Medication use was divided into bronchodilator use (atrovent, beconase, berodual, berotec, bricanyl, flixotide, salbutamol, and ventolin), preventive or maintenance medication (becotide, fenistil, livocab, 

lomudal, phenergan, prednisone, promethazine, pulmicort, rhinocort. 

theolair, tinset, triludan, and zaditen), and other. Bronchodilator use was 

analyzed with both the prevalence and the daily mean deviation from 

the overall mean of number of puffs as the dependent variable. To remove the effect of different groups of children contributing to the 

mean on different days, the number of puffs taken by child ion a certain day was subtracted from the mean number of puffs taken by child 

i (number of puffs - mean number of puffs = deviation). For each 

day of the study, the mean of all 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 or maintenance/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 shown first. A peak was 

seen around Day 28 (May 23). A decrease of PEF was seen 

around Day 32 (May 27). Air pollution levels during the study 

period are shown in Table 2. The correlation between 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. 

The results of the regression analyses are shown in Table 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 PM10 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 of pollutant 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 our study effects on lung function 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 of more 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 3 

70,--------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 apparently persist at low concentrations. 

The results of our 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 of 0.14% of morning PEF and 

an increase of the 7-d mean with 10 fl.g/m3 decreased the 

evening PEF by 0.26%. We found a nonsignificant decrease of 

0.52% per 10 Jl.g/m3 of the 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 

PM10, 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. of grains/m3 

Temperature, oc 

• Number of days 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); 

PM10, 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 (-3.1 0, 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 of exposure 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 of 0.31% per 10 Jl.g/m3 of the 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 

mean decrease in PEF of 0.08% per 10 Jl.g/m3 in studies of particulate matter. 

We observed consistent effects of particulate air pollution 

on respiratory health in this panel of mainly asthmatic children, despite the low levels of air pollution. The asthma severity in our panel is reflected in the high percentage of children 

recently treated for asthma (67%) and current airway medication users (77% ). Other studies that investigated particulate 

effects on symptomatic children have selected relatively healthy 

children with a low percentage of medication 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 or allergy 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 of particles with aerodynamic diameters of less than 

about 4.5 f.Lm (2). Under present-day conditions in the Netherlands with very little coal burning, BS can be considered 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 health outcome than PM10 (9). Another expla-
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 Bronchodilators 1 

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) 

PM10, 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 of bronchodilator use. 

• Prevalence ratios reflect difference over the range of air pollution, relative to the mean prevalence. 

t Results would have no meaning because these data reflect the awakening before measuring of exposure 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 those found for PM10 are in line 

with the results of a study conducted on air pollution and daily 

mortality in the city of Amsterdam during 1986--1992 (10). 

Both for PM10 and black smoke, the results for both peak 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 

mean concentrations might be less affected by 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 subpopulation may 

be more responsive to particulate matter air pollution than the 

subjects not using bronchodilators during the study period. 

Other investigators (11) have found that the use of bronchodilators increased, and (possibly as a result) lung function improved also with increasing air pollution concentrations. Such 

interactions do not seem to have occurred in our study. 

In conclusion, our study suggests that respiratory health in 

asthmatic children attending school in the urban area of Amsterdam was adversely affected by relatively low concentrations of air 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. 

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