Indoor air quality (IAQ) has been the object of several studies due to its adverse health effects on children.
Indoor air quality (IAQ) has been the object of several studies due to an increasing concern within the scientific community on the effects of IAQ upon health, especially when people tend to spend more time indoors than outdoors [
The aim of this study is to determine the IAQ and its association with respiratory health among Malay preschool children in Selangor. Preschool children are particularly advantageous when studying respiratory symptoms and air pollution because they are unlikely to smoke cigarettes regularly, have no serious exposure to occupational pollutants, and tend to have a stable residential history, and their respiratory system seems to be more sensitive to air pollution. There were very limited numbers of studies that relate IAQ to the health impacts in Malaysia. This research will help to increase awareness to the community, especially the parents and teachers on the risk of poor IAQ.
This cross-sectional study was carried out among male and female Malay children who attended preschools in Balakong area (studied group), while the population for comparative group was selected among male and female Malay children who attended preschools in Bangi area.
A total of 111 preschool children aged 5-6 years were recruited from 4 preschools located in Balakong and Bangi, Selangor. Studied populations were selected among male and female Malay children who attended preschool in Balakong area, while the population for comparative group was selected among male and female Malay children who attended preschools in Bangi area. Random sampling method was used to select the respondents with several inclusion criteria; only preschool children ranging from 5 to 6 years old, healthy, Malays, and free from any respiratory illness were selected. The name list of the children was obtained from the preschool teachers. Screening of respiratory illnesses was done by using a questionnaire and those who reported respiratory illnesses were excluded from the study. Children’s parents were responsible for answering the questionnaires and for passing back to the school teacher, which was then being collected by the researcher.
The questionnaires used were adopted from The American Thoracic Society, “Questionnaire ATS-DLD-C WHO (1982).” The questionnaire was pretested and the total respondents for the pretest were 10% of the sample size. Indoor air quality assessments were conducted in each preschool using several indoor air monitoring instruments. The monitoring phase included air sampling for at least a 3- to 4-hour period during preschool normal activities. The IAQ monitoring instruments used in this study included TSI 8520 DustTrak Airborne Particle Monitor for PM2.5 and PM10; PbbRAE Portable VOC Monitor (pbbRAE 3000) for VOCs; Q-Trak Plus Model 8554 Monitor for CO2, CO, relative humidity, and temperature; and TSI Velocicalc Plus Model 8386 for air velocity. Instruments for PM2.5, PM10, and VOCs were placed at a height of about 0.6–1.5 meters above the floor, approximately at the level of children’s breathing zone. The selected place was not closer than 1 meter to a wall, a door, or an active heating system. Whenever possible, all the instruments were placed at the back of the classroom to avoid any disruption of sound from the instruments during a learning session and to avoid attraction for the children. Meanwhile, the measurements of CO2, CO, temperature, relative humidity, and air velocity were taken periodically and spread throughout many areas in the building to be sure that they were distributed evenly. MM-SP004 Tabletop Portable Spirometer was used to conduct a lung function test among the study respondents. In this study, evaluation of lung function test was performed by comparing the obtained value with normal values (standard value). Based on a study by Azizi and Henry [
Statistical analysis was carried out using SPSS version 20.0. To study the association and differences between indoor air pollutant concentrations and the respiratory health of children,
According to the results obtained, the majority of fathers in the studied group have an education level of degree or Ph.D. degree, while the majority of fathers in the comparison groups only studied until SPM level. As for mother’s education level, the majority of mothers in both groups studied until end of Form 5 or STPM level. The Chi-square test showed that there were significant differences between the parental education level between studied and comparative groups. As for the total household income, the mean income for the studied group was RM (
Data samples from these 4 preschools have been compared. The preliminary site survey showed that more than all of the preschool buildings were aged below 10 years. Most of the preschool building walls were made up of the concrete and cemented floor. All the preschools were naturally ventilated where they used fan as their mechanical devices for ventilation purpose. All the preschools have more than 4 windows in the preschools’ building. Preschools from studied area cleaned their preschool twice daily. All 2 studied group preschools have cooking activities in their premises. All preschools involved in this study were using insulation board for the preschools’ building ceiling. Preschools from the comparative group were using book shelf made up of pressed wood. All these preschools cleaned their floor once a day. Preschools from studied group reported the highest number of heavy traffic density as compared to comparative group preschools. All the preschools reported most of the outdoor pollution was originated from vehicles.
Table
Comparisons of indoor air quality between study areas.
Variables | Studied area |
Comparative area |
|
|
---|---|---|---|---|
Median (IQR) | ||||
PM2.5 ( |
80.0 (17.0) | 72.0 (19.0) | −5.494 |
|
PM10 ( |
118.0 (46.5) | 54.0 (42.0) | −6.445 |
|
VOCs (ppm) | 0.08 (0.03) | 0.11 (0.05) | −2.214 |
|
CO2 (ppm) | 579.0 (340.0) | 784.0 (526) | −0.287 | 0.774 |
CO (ppm) | 1.0 (0.4) | 0.8 (1.0) | −8.076 |
|
Temperature (°C) | 29.7 (3.5) | 26.8 (2.3) | −7.143 |
|
Rh (%) | 78.1 (13.5) | 54.7 (31.7) | −2.821 |
|
Air velocity (m/s) | 1.59 (2.34) | 0.97 (0.24) | −1.046 | 0.295 |
Mann-Whitney
**Significant at
*Significance at
Parametric test revealed that the distribution of CO2 in classrooms for studied and comparative areas was not normal, with the mean of CO2 in the comparative area being higher than the studied area. The distribution of CO2 between studied and comparative areas indicates no significant difference, whereas, for CO, the mean was higher in the studied area as compared to comparative area. There was a statistical significant difference between CO levels in classrooms in studying and comparative areas (
Results from statistical analysis showed that the lung function among comparative group was significantly higher as compared to studied group. Findings from the analysis show that all lung function parameters (FVC, FEV1, FVC%, and FEV1%) were significantly higher for the comparative group as compared to the studied group except for FEV1/FVC% (Table
Comparisons of lung functions among preschool children.
Lung function | Studied group ( |
Comparative group ( |
|
|
---|---|---|---|---|
Mean ± S.D/median ± IQR | ||||
FVC (Liter)a | 0.63 ± 0.18 | 0.79 ± 0.21 | −4.160 |
|
FEV1 (Liter)a | 0.60 ± 0.17 | 0.76 ± 0.19 | −4.484 |
|
FVC%a | 69.93 ± 17.03 | 85.94 ± 19.81 | −4.577 |
|
FEV1%a | 72.02 ± 18.17 | 88.69 ± 18.16 | −4.811 |
|
FEV1/FVC%b | 103.69 ± 05.67 | 105.62 ± 6.81 | −1.671 | 0.095 |
a
bMann-Whitney
Four parameters of respiratory symptoms were assessed in this study, where the symptoms were identified using the standardized and validated questionnaires, adopted from American Thoracic Society “Questionnaire ATS-DLD-C WHO” (1982). Questions asked included cough, phlegm, wheezing, and chest tightness experienced by children. Table
Prevalence of respiratory symptoms among studied and comparative preschool children.
Variables | Studied |
Comparative |
|
|
ORb | 95% CI |
---|---|---|---|---|---|---|
Total (%) | ||||||
Cough | ||||||
Yes | 21 (34.4) | 7 (14.0) |
|
|
|
|
No | 40 (65.6) | 43 (86.0) | ||||
Phlegm | ||||||
Yes | 3 (4.9) | 1 (2.0) | 0.674 | 0.412 | 2.53 | 0.26–25.15 |
No | 58 (95.1) | 49 (98.0) | ||||
Wheezing | ||||||
Yes | 20 (32.8) | 8 (16.0) |
|
|
|
|
No | 41 (67.2) | 42 (84.0) | ||||
Chest tightness | ||||||
Yes | 1 (1.6) | 0 (0.0) | 0.827 | 0.363 | 1.83 | 1.55–2.17 |
No | 60 (98.4) | 50 (100.0) |
*Significant at
95% CI = 95% confidence interval.
The indoor air pollutant concentrations were categorized based on median value. A value that was higher than median was categorized as high while the value that was lower than median was categorized as low. The results showed there was a significant association between indoor concentration of PM2.5 and abnormality of FVC% among study respondents (
The associations between indoor PM2.5, PM10, VOCs, CO2, and CO inside different classrooms in preschools with the prevalence of respiratory symptoms were established using the median value. Results from statistical analysis showed a significant association between wheezing and indoor PM2.5 concentrations in preschools (
Indoor VOCs concentration was categorized into high (≥0.103 ppm) and low (<0.103 ppm) values. From the results obtained, there were no significant associations found between phlegm and chest tightness with indoor VOCs concentrations. However, there were significant associations found between cough and wheezing with indoor VOCs concentration (
Logistic regression was conducted to determine the main factor that influenced the abnormality of FVC% and FEV1% among study respondents after controlling all the confounders in this study. Table
Factors influenced the abnormality of FVC% among study respondents after controlling all the confounders.
Independent variables |
|
S.E |
|
ORb | 95% CI |
---|---|---|---|---|---|
Constant | 1.089 | 0.595 | 0.067 | ||
PM2.5 | 1.403 | 0.566 |
|
|
|
PM10 | −0.330 | 0.553 | 0.550 | 0.72 | 0.24–2.12 |
VOCs | −0.056 | 0.517 | 0.913 | 0.95 | 0.34–2.60 |
CO2 | −0.600 | 0.556 | 0.280 | 0.55 | 0.19–1.63 |
CO | 2.000 | 0.624 |
|
|
|
Indoor smoking | −0.702 | 0.554 | 0.205 | 0.50 | 0.17–1.47 |
Duration living in the vicinity (year) | −3.927 | 0.760 | <0.001** | 0.02 | 0.00–0.09 |
Nagelkerke
**Significant at
Factors influenced the abnormality of FEV1% among study respondents after controlling all the confounders.
Independent variables |
|
S.E |
|
ORb | 95% CI |
---|---|---|---|---|---|
Constant | 0.501 | 0.529 | 0.343 | ||
PM2.5 | −0.774 | 0.481 | 0.108 | 0.46 | 0.18–1.18 |
PM10 | −0.233 | 0.505 | 0.645 | 0.80 | 0.30–2.13 |
VOCs | −0.042 | 0.473 | 0.929 | 0.96 | 0.38–2.43 |
CO2 | −0.512 | 0.511 | 0.317 | 0.60 | 0.22–1.63 |
CO | 1.648 | 0.545 |
|
|
|
Indoor smoking | −0.099 | 0.497 | 0.842 | 0.91 | 0.34–2.40 |
Years living in the vicinity | −3.217 | 0.681 | <0.001** | 0.04 | 0.01–0.15 |
Nagelkerke
*Significant at
Logistic regression was carried out in order to determine the factors that influenced wheezing symptoms among study respondents after controlling all the confounders such as income, parental education level, indoor smoking, and duration living in the vicinity. Table
Factors influenced wheezing among study respondents after controlling all the confounders.
Independent variables |
|
S.E |
|
ORb | 95% CI |
---|---|---|---|---|---|
Constant | −0.654 | 0.376 | 0.082 | ||
PM2.5 | 1.060 | 0.500 |
|
|
|
PM10 | 1.704 | 0.603 |
|
|
|
VOCs | −1.469 | 0.495 | 0.003 | 0.23 | 0.09–0.61 |
CO2 | −0.584 | 0.455 | 0.200 | 0.56 | 0.23–1.36 |
CO | 1.796 | 0.661 |
|
|
|
Indoor smoking | −0.102 | 0.479 | 0.831 | 0.90 | 0.35–2.31 |
Duration living in vicinity (year) | −0.113 | 0.526 | 0.830 | 0.89 | 0.32–2.51 |
Nagelkerke
*Significant at
111 Malay preschool children between 5 and 6 years old participated in this study. This study was carried out at 4 different preschools; 2 preschools from Balakong (studied group) and 2 preschools from Bangi (comparative group) were selected. Both studied areas consist of 8 classrooms, with 4 classrooms for each area. Classrooms A, B, C, and D were located in the studied area; Classrooms E, F, G, and H were located in the comparative area. Respondents were categorized into two sample units, studied and comparative groups. Sociodemographic factors were successfully matched as obtained in the statistical analysis where it discovered no significance differences in terms of total dwellers in both studied and comparative areas. The years living in the vicinity among study groups were also assessed as to consider their residential exposures. The majority of studied children live in the urban area while the majority of the comparative children live in a suburban area. Most children who live in urban area live in close proximity to the main road compared to those who live in a suburban area.
Indoor air quality parameters were measured from 8 different classrooms in this study. The highest concentrations of PM2.5 (94
Statistical analysis indicated that there was a significant difference in the concentration levels of PM2.5 and PM10 between studied and comparative areas and suggested that the preschool locations might have contributed to the concentration of the particulates. The location of preschools as well as outdoor and indoor combustion activities is the major contributor to the high level of indoor PM2.5 and PM10 in the studied area. Both preschools in the studied area had a kitchen in the building. Cooking activity inside the building might have contributed to higher PM2.5, compared to preschools in the comparative area. Cooking indoor can also generate particles <0.1
Outdoor sources of particulate matters such as generation of dust from paved or unpaved roads might also contribute to the high level of indoor particulate matter. Differing from preschools in comparative area, both preschools in the studied area were located nearby busy roads. Thus, heavy traffic and vehicle fossil fuel combustion might have contributed to high levels of particulate matters in studied areas. Rom [
Statistical analysis showed that there was a significant difference between indoor VOCs levels between the studied and comparative areas. The indoor concentrations of VOCs usually exceeded outdoor levels [
A significant difference was also found in the mean concentrations of CO2 and CO in the studied area. The highest concentration of CO2 was found in Classroom C, which was located in the studied area. The doors and windows were closed during each lesson, which contributed to less air exchange. The total number of persons in this classroom can be up to more than 20 persons each time, causing overcrowding. A study in Portugal reveals a strong correlation between the CO2 levels with occupancy [
A study conducted in Klang Valley by Fariza et al. [
Results from Chi-square test reveal that the prevalence of respiratory symptoms was higher among studied children for cough (
Significant associations were found between indoor concentrations of PM2.5 and CO with the abnormality of FVC%, while only concentrations of CO were found significant with the abnormality of FEV1%. This finding was supported by a study conducted in Klang Valley, where significant associations were found between indoor concentration level PM2.5 and the decrements of lung function among children in urban area [
Statistical analysis was carried out to assess the association between indoor air pollutant concentrations inside classrooms with respiratory symptoms among children. It is reported that there were significant associations found between PM2.5, PM10, VOCs, and CO with respiratory symptoms but not for CO2. A significant association between wheezing with indoor PM2.5 and indoor PM10 concentrations in preschools was found in this study. The long-term effects of PM2.5 particles on children include both lung function changes and the development of chronic respiratory disease, while short-term exposures to PM10 can result in increased respiratory symptoms. The concentrations of PM10 were associated with wheezing among asthmatic school children in a study conducted by Zakaria et al. [
Similarly, study by Sonnenschein-van der Voort et al. [
Logistic regression was conducted to determine the main factor that influenced the abnormality of FVC% and FEV1% among study respondents after controlling all the confounders in this study. Statistical analysis results reveal that the abnormality of FVC% among children was significantly associated with the concentrations of PM2.5 and CO. Short-term PM2.5 exposures are linked to reduced lung function, especially in children. An increase of 10
A study in southern California by Gauderman et al. [
Logistic regression was carried out in order to determine the factors that influenced wheezing symptoms among study respondents after controlling all the confounders such as income, parental education level, indoor smoking, and duration living in the vicinity. Statistical analysis results reveal that wheezing had significant association with the concentrations of PM2.5, PM10, and CO. Wheezing is one of the classic symptoms associated with asthma in children. Short-term PM2.5 exposures were linked to increased hospital admissions and emergency department visits for respiratory effects, such as asthma attacks, as well as increased respiratory symptoms, such as wheezing. Research based on parental reports of symptoms also showed elevated rates of wheeze in association with PM2.5 and PM10 among preschoolers [
Findings from this study indicated that the exposures to poor indoor air quality and increasing levels of indoor pollutant concentrations were the risk factors that had caused a reduction in lung function and increasing reports of respiratory symptoms among the study respondents. This study suggests that knowledge should be given to the public, preschool managements, and parents, specifically about the risk of getting respiratory problems due to poor indoor air quality. Further studies are needed to confirm the observed association between indoor air pollutant concentrations and respiratory health among preschool children in urban, suburban, and rural areas.
The authors declare that there is no conflict of interests.
This study was funded by the MOSTI Science Fund, Vote no. 06-01-04-SF1582. The authors thank the Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, for their valuable assistance in completing this research. They would also like to record their heartfelt appreciation to the management of the schools in the study as well as parents of the respondents for their cooperation.