Introduction

Particulate matter (PM) are pollutants of environmental concern that consist of tiny particles suspended in the air1. They originate from human activities such as industrial processes, construction activities, and vehicle exhaust1,2,3. They also arise from natural activities such as wildfires and volcanic eruptions1. These particles are classified according to their size, with PM2.5 and PM10 being two common categories4,5. PM2.5 refers to particles of 2.5 micrometres or smaller in diameter, and PM10 refers to particles of 10 micrometres or smaller in diameter1,3. Scholars note that the size of PM influences their transportation into the air, and that particles of an aerodynamic size, such as PM2.5 and PM10, are more likely to be transported over long distances1,6 and contribute to the increased concentration of PM in areas far from the pollution source (Fig. 1).

The outcome of previous investigations reveal that exposure to PM can lead to many poor health outcomes, including morbidity and mortality7. The WHO and scholars reveal that a significant number of people globally in different settings, including educational institutions, are exposed to air quality that does not meet the minimum standards stipulated by the WHO8,9,10,11. Studies indicate that exposure to high levels of PM has become a global public health concern because of its association with many health risks, including respiratory and cardiovascular diseases9,12,13. These health issues are heightened among vulnerable groups, such as the elderly, the sick, and school children9,14,15. Scholars illustrate that children spend significant time in school environments with high concentrations of air pollutants, including PM16,17,18,19. This exposure leads to short- and long-term respiratory issues among school children.

Previous studies also reveal that exposure to both PM2.5 and PM10 is associated with decreased lung function in school children14,20, and that school children’s increased vulnerability to high concentrations of PM is because their bodies, including their lungs, are still developing, making them more susceptible to the adverse effects of pollutants than adults14. Research also demonstrate that children’s vulnerability is because of their prolonged and regular exposure to poor quality air in school environments16,21. Therefore, reducing the risk associated with exposure to air pollution in school settings is essential to minimise the effects of air pollution on children’s quality of life and to increase their chances of survival from respiratory diseases. Achieving this requires comprehensive global data on air pollution, including PM exposure and its health effects.

The health effects of exposure to PM in school environments in the developing world is considered very precarious. Studies on air pollution and associated health effects have been conducted worldwide16,17,22,23. However, evidence from the WHO and other scientific literature reveal limited research on air pollution and its health effects in low- and middle-income countries, especially in Africa, including Rwanda24,25. The limited available information indicates that air pollution in Rwanda often exceeds acceptable guidelines established by the WHO26,27,28. The elevated pollution could be related to rapid urbanisation, industrialisation, a significant increase in road traffic and construction activities, as well as agricultural activities, mining, brick making, and quarrying29,30.

Kigali is characterised by many traffic jams, like other cities in the region, such as Nairobi in Kenya and Kampala in Uganda29. A recent study shows that the traffic flow on one road in Kigali is 3 346.6 vehicles/h29.Moreover, Kigali is a rapidly developing city with an increase in industries, mainly due to policies that facilitate investors31. The combination of this rapid industrialisation, high traffic volumes, and widespread construction activities have significantly contributed to air pollution in Kigali. In Kigali, the concentration of PM2.5 between 2022 and 2024 ranged from 25 µg/m³ to 175 µg/m³, and the levels remain high32, which is a public health concern. Several reports have shown that air pollution levels in Kigali exceeds WHO guidelines26,28,32.

There is a need to explore the health effects of PM on lung function among school children in Rwanda. Although efforts have been made to research environmental pollution in Rwanda, only two studies on air quality in Rwandan schools have been conducted. These two studies report high concentrations of PM2.5, PM10, and black carbon in school environments4,16. Neither studies assessed the associated health effects, leaving a significant research gap. Understanding the effects of PM on children’s health, specifically their lung function, is crucial to design intervention programmes to improve air quality and safeguard children’s health in learning environments. Therefore, this research aimed to assess the effects of exposure to PM on children’s lung function in schools and to provide empirical evidence for future interventions.

Methods and materials

The study area

This study was conducted in the city of Kigali, the capital of Rwanda. Kigali is an urban area characterised by rapid industrial development, high traffic, and many construction activities. Such human development activities typically lead to more significant environmental pollution, including air pollution. Such air pollutants can penetrate any environment, including learning spaces. Studies show that schools in urban environments are more likely to experience elevated air pollutants, such as PM, than schools in rural areas33.

Study design

The panel study design was used to measure PM2.5 and PM10 indoor concentrations in two phases in the same schools/classrooms. The same design was employed to conduct repeated lung function evaluations among the same school children. A panel study design allows the researchers to observe or measure the same individual or units multiple times at different times34. Therefore, this design was used to repeatedly measure PM concentrations and lung function parameters in the same schools and same individuals.

Sampling methods

The Directorate of Education at the city level provided the list of schools, and thereafter, the schools were grouped into two strata. The first stratum consisted of schools located in highly exposed areas, defined as being within 0.5–1 km of significant pollution sources. These sources included roads with heavy vehicular movements or high traffic volumes characterised by traffic congestion, as well as industrial activities such as flour processing facilities, bus stations, garages, and other noticeable sources of pollution, including commercial buildings. Another stratum comprised schools located in predominantly peri-urban areas, characterised by the absence of busy roads, industries, or significant pollution sources.

Six study sites (public primary schools) were randomly selected from the two strata. Three of the selected schools were located in highly exposed areas (more urban), and three were in moderately exposed areas, predominantly peri-urban. The selection of these schools followed the procedures described in other studies evaluating air quality in schools and their effects on lung function in primary school children4,35.

For the school children to be eligible for participation, they had to be enrolled in primary school Year 3, refereed as school children of Grade III; In Rwanda, children in Grade III are typically between 8 and 12 years old. However, due to government strategies aimed at reintegrating school dropouts, some schoolchildren in this grade may be as old as 15 years36. Researchers extended the age range to account for these older children.The children also must not have had any respiratory disease in the two weeks before the data collection or any other health problem that can affect the lung function test. A proportionate stratified sampling method was used to determine the number of participants from each selected school. Subsequently, simple random sampling was used at the school level to select Grade III classrooms. The same simple random sampling method was applied in the chosen classrooms to select individual children for the study.

The age of Grade III students are critical for lung growth and development, making children more vulnerable to environmental pollutants such as PM37. Children in this category have sufficient cognitive maturity to follow the instructions of lung function tests38.

Sample size calculation

In the six selected schools, the researcher determined the number of children in each school, and determined that the total population was 278 children. Based on this population, the sample size was calculated using Krejcie and Morgan’s formula39:

$$\:n=\frac{N\times\:X\times\:p\times\:q}{{d}^{2}(N-1)+{X}^{2}\times\:p\times\:q}$$

Where

n = Required sample size;

N = Population size (278);

χ² = Chi-square values for 95% confidence level (3.841);

p = Estimated population proportion (0.5);

q = 1-p;

d = Margin of error (0.05);

n = \(\:278\times\:3.841\times\:0.5\times\:0.5/\left(0.05\right)2\times\:(278-1)+3.841\times\:0.5\times\:0.5\:=\:162\)

The researchers estimated a 10% non-response rate, and therefore the adjusted sample size was \(\:\left(n\right)\:=\:162/1-10\%\:=\:180\). The researchers applied a proportionate stratified sampling technique to ensure all classrooms sampled were represented proportionally.

Fig. 1
figure 1

A flow diagram of the stage of selection.

Full size image

The first stage went through two phases. In the first phase of selection, the researchers used a list of schools based on their location and created two groups: One for schools in moderately exposed areas, and the other for schools in highly exposed areas. Afterwards, the researchers applied simple random sampling to select three schools from each group, resulting in a total of six schools. In the second phase, simple random sampling was used again to select Grade III classrooms in each of the six schools; six classrooms were selected for the study.

In the second stage of selection, the researchers selected 180 children and ensured a proportionate representation in each school. Research assistants visited the homes of the children to seek consent from the parents or legal guardians to allow the children to participate and to ask questions to determine the eligibility of the children. At this stage, 139 children were eligible because 41 were excluded for lack of parental consent or non-satisfaction with the criteria for inclusion. On the day of the pulmonary function test in Phase I, nine children were absent, eight failed three times with spirometers, and only 122 were tested. In the second phase of the test, the researchers tested only 107 children because eight were missing, two had failed the spirometer test three times, and five had changed schools.

Assessment of children’s health history

The school administration facilitated identifying the selected children’s parents or legal guardians by providing their names and contact details. The research assistants contacted the parents or guardians and organised house visits to collect data on medical history of children, their social demographic information, and information about the energy sources used at home. A total of 139 parents and guardians completed the questionnaire and signed consent forms for their children to participate in the study. Based on the completed questionnaires, the researchers identified 139 eligible schoolchildren to participate in the lung function test. A questionnaire adapted from other studies40,41 was used to capture data on the children’s medical history, age, sex, types of cooking energy used at home, and smoking-related practices.

Determination of PM levels

Air quality data were collected in two phases, Phases I and II respectively. During the dry season, Phase I spanned 14 consecutive days from May to June 2022. Phase II was conducted during the rainy season from November to December 2022 and also lasted 14 consecutive days. During both phases, the indoor PM2.5 and PM10 concentrations were determined in the selected classrooms at each school. Mobile air sensors (PurpleAir PA-II) were used to collect PM2.5 and PM10 concentrations. The monitoring of PM was carried out simultaneously in schools located in highly exposed and moderately exposed areas. Before starting the data collection exercise, air sensors were calibrated in the laboratory of the School of Science of the College of Science and Technology at the University of Rwanda.

To ensure the accuracy of the air sensors (PurpleAir), technicians concurrently installed high-accuracy air quality monitors (AQMesh) with air sensors (PurpleAir) at the same location with the same background for reference. The air sensor calibration followed the procedure described in a study that evaluated the air quality in schools in Kigali [20]. Before starting the air quality testing, the teachers were informed about the purpose of the study and they were asked to continue working as usual without modifying any classroom condition, which they readily obliged.

The samples were collected during regular teaching and learning hours, from 8:00 AM to 4:00 PM. Data were collected in real-time and reported at intervals of 2 min, and hourly values were calculated. Indoor air samplers were placed on stationary walls within the breathing zones of the children at heights ranging from 0.8 m to 1.5 m near the wall opposite the blackboard, 1 m from the window. The position of the air sampling equipment followed the procedure described in another study that assessed the patterns and disparities in indoor PM levels in selected primary schools in Kigali, Rwanda4. The values obtained for PM2.5 and PM10 were compared with the WHO air quality guidelines42.

Determination of the lung function status of children

The evaluation of lung function among school children was also carried out during Phase I (during the dry season) and Phase II (during the rainy season) after the assessment of the PM concentrations in the schools. Before starting the lung function exercises, the principal investigator verified the questionnaires returned by the research assistants. During Phase I, lung function tests were performed on 122 schoolchildren. However, some participants were excluded from the study due to absenteeism on the day of testing; some changed schools, and others failed to complete the spirometer test after three attempts. Therefore, 107 schoolchildren were tested in both phases since each school was visited once. In schools located in highly polluted areas, 58 children were tested, and 49 children were tested in suburban schools.

Before children participate in the study, informed consents have been obtained from the parents and/or legal guardians. Prior to starting the lung function test, schoolchildren were educated about lung function test procedures until they were familiar with it. The researchers explained the risks associated with spirometry tests, such as discomfort, coughing, anxiety, and risk of infection. The lung function tests were performed during the morning hours in a prepared room. Trained clinicians from the Department of Clinical Medicine of the University of Rwanda carried out the tests. The assessment of the lung function followed the procedures described by the American Thoracic Society43.

Three indicators of lung function were determined by spirometer (Spirobank II), namely forced expiratory volume in the first second (FEV1), forced vital capacity (FVC), and peak expiratory flow (PEF); these parameters were recorded directly on the computer. Disposable turbines with cardboard mouthpieces were used to avoid the risk of infection. The children’s height and weight were also measured before the lung function tests.

The spirometers were connected to computer software to ensure data accuracy and quality control of data. In device configuration on the computer, the option of using disposable turbines (FlowMIR) manufactured with factory calibrations and designed to be used without additional calibrations was selected44. Data were kept on the computer, protected by a password, and all spirometers were handed over to the project’s principal investigator.

Statistical analysis

Data were analysed using the Statistical Package (SPSS) software version 29 (https://www.ibm.com/support/pages/downloading-ibm-spss-statistics-29). Descriptive statistics, including the mean and standard deviation (SD), were used to describe the concentration of PM2.5 and PM10 in schools, as well as anthropometric measurements and age. Percentages were used to describe the gender and types of energy sources used at home. Furthermore, inferential statistical methods were used to examine the relationship between air quality and lung function. A Mann-Whitney U test was applied to assess differences in lung function between groups characterized by two categories.

Wilcoxon signed-rank test was used to compare lung function among children and air quality in schools in different seasons. A correlation coefficient test was also applied to test the correlation between air quality and lung function. Finally, a linear regression analysis was performed to examine air quality’s effect on the school children’s lung function. All data were considered statistically significant at p = 0.05. Ethical approval was obtained prior to data collection (Notice No. 108/CMHS 110/2021).

Results

Table 1 provides a summary of the various characteristics of the respondents. The mean age of the respondents was 11 years (SD = 1.15), the mean weight was 30.25 kg (SD = 5.68), and the mean height was 136.03 cm (SD = 8.01). Regarding sex, the participants were predominantly female (62.6%), and 37.4% were male. Traditional cooking energy sources are the predominant cooking energy used in the households where these children live. In this study, traditional cooking energy sources primarily include wood, coal, and agricultural waste, and modern cooking technology includes the use of liquefied petroleum gases, electricity, and biogases.

Table 1 Demographic and household energy characteristics of study participants.
Full size table

As shown in Fig. 2, data on the concentrations of PM2.5 and PM10 during the rainy and dry seasons indicate that during the dry season, the daily average concentration of PM2.5 ranged from 38.8 µg/m3 to 70.3 µg/m3 in moderately exposed schools, and from 37.0 µg/m3 to 84.0 µg/m3 in highly exposed schools. During the rainy season, PM2.5 levels ranged from 32.9 µg/m3 to 73.8 µg/m3 in moderately exposed schools, and from 43.3 µg/m3 to 61.8 µg/m³ in highly exposed areas.

For PM10, during the dry season, the levels ranged from 43.8 µg/m³ to 75.2 µg/m3 in moderately exposed schools and from 41.8 µg/m3 to 90.3 µg/m3 in highly exposed schools. During the rainy season, the level of PM10 ranged from 37.6 µg/m3 to 80.8 µg/m3 in moderately exposed schools and from 42.9 µg/m3 to 70.3 µg/m3 in highly exposed schools. PM levels were significantly higher in the dry season for both areas, with highly exposed areas showing palpable elevations. (Fig. 2).

Fig. 2
figure 2

PM2.5 and PM10 concentrations (µg/m3) measured in schools in the dry season versus the rainy season

Full size image

Data on the disparities in lung function between schools in two different settings and the variations between seasons indicate that there was a significant difference in the median scores of the measured lung function parameters, including FVC (highly exposed schools = 1.58 L; moderately exposed schools = 1.80 L; U = 922.5; p-value = 0.003), FEV1 (highly exposed schools = 1.28 L; moderately exposed schools = 1.61 L; U = 680.5; p-value < 0.001), and PEF (highly exposed schools = 1.79 L/s; moderately exposed schools = 2.78 L/s; U = 548.5; p-value < 0.001).

Significant differences were observed in the median PM10 scores between highly exposed schools (68.35 µg/m3) and moderately exposed schools (51.85 µg/m3) during the dry season (U = 480.0; p < 0.001). Similarly, during the rainy season, significant differences were found in PM2.5 scores (highly exposed schools = 47.45 µg/m3; moderately exposed schools = 55.70 µg/m3; U = 754.0; p < 0.001) and PM10 scores (highly exposed schools = 51.85 µg/m3; moderately exposed schools = 62.74; U = 754.0; p < 0.001). However, there was no significant difference in the median lung function scores between children in highly exposed and moderately exposed schools during the rainy season. These results are shown in Table 2.

Table 2 Disparities in the levels of PM and lung function parameters between schools in the two different settings, across the dry and rainy seasons.
Full size table

Table 3 presents a comparison of air quality in schools and lung function among school children between the dry and rainy seasons. A Wilcoxon signed-rank test revealed a significant difference in PM2.5 (z = -9.01; p < 0.001) and PM10 (z = -5.37; p < 0.001) levels between the two seasons. The median of PM2.5 concentration was higher during the rainy season (54.5 µg/m2) compared to the dry season (51.7 µg/m2). Conversely, the median PM10 concentration was higher in the dry season (63.4 µg/m2) than in the rainy season (60.6 µg/m2). However, no significant difference was observed in lung function between the two seasons.

Table 3 Variations in PM2.5 and PM10 and lung function parameters between the dry and rainy seasons.
Full size table

Table 4 shows the correlations between PM2.5 and PM10 levels in schools and lung function during the dry and rainy seasons. Our results revealed a significant correlation between PM10 and FEV1 and PEF (P < 0.001). No significant correlation was found between PM10 and FVC. Furthermore, no significant correlation was found between PM2.5 concentration and FEV1 or FVC. This finding also indicates that there was a significant association between PM2.5, PM10, and PEF during the rainy season (p < 0.001). However, PM2.5 and PM10 concentrations were not significantly associated with FVC or FEV1 (P > 0.001).

Table 4 Correlations between PM concentrations and lung function parameters of school children during the dry and rainy seasons.
Full size table

Table 5 shows the associations between PM and lung function parameters, controlling for age, weight, height, and cooking energy use. PM10 during the dry season was found to predict FEV1 (β = -0.011; t = -3.180; P = 0.002), PEF (β = -0.040; t = -4.108; P < 0.001), and FVC (β = -0.009; t = -2.284; P = 0.025). Additionally, PM2.5 predicted FVC (β = -0.008; t = -2.017; P = 0.046), FEV1 (β = -0.011; t = -3.180; P = 0.002), and PEF (β = -0.042; t = -4.315; P < 0.001). Furthermore, our findings indicate that during the rainy season PM10 (β = -0.050; t = -6.340; P < 0.001) and PM2.5 (β = -0.021; t = -2.240; P = 0.027) predicted PEF, and PM10 predicted FEV1 (β = -0.020; t = -5.450; P < 0.001). Neither PM10 nor PM2.5 predicted FVC or FEV1 during the rainy season.

Table 5 Association between air quality and lung function of children in schools across the dry and rainy seasons.
Full size table

Discussion

The study aimed to assess the effect of exposure to indoor PM concentrations on the lung function of children in primary schools in Kigali, Rwanda. The results showed that indoor PM2.5 and PM10 in the sampled schools exceeded the WHO air quality guidelines and were associated with a decline in lung function among schoolchildren. According to the WHO air quality guidelines, the annual mean for PM10 should not exceed 15 µg/m3, and the 24-hour mean should not exceed 45 µg/m3, and for PM2.5, the annual mean limit is 5 µg/m3, and the 24-hour mean limit is 15 µg/m3 46. Previous studies conducted in schools indicate that in many schools, the PM concentrations are higher than the WHO air quality guidelines4,10,45,46. Exposure to such high concentrations of air pollutants, particularly PM2.5 and PM10, poses a serious risk to public health, which makes interventions necessary to improve indoor air quality in schools.

Findings from the present showed that the concentration of PM2.5 and PM10 in schools varied significantly across dry and rainy seasons respectively. Our results align with the findings of other studies reporting seasonal variations in PM concentrations47,48. The current study revealed that during the rainy season, PM2.5 and PM10 concentrations were higher in schools located in moderately exposed areas compared to highly exposed areas. The findings contrast with previous research, which report that PM concentrations are typically higher in urban schools than in rural schools49,50. However, the literature also explains the factors that contribute to higher PM levels in rural areas compared to urban settings. It is reported that rural areas are often dependent on biomass fuels (wood, crop residues) for cooking and heating51,52. Scholars report the contribution of biomass burning in the increase of PM concentration in different settings, including learning environments53,54,—55.

Furthermore, agricultural activities, which are prevalent in rural areas, have been identified in the literature as a significant contributor to increasing PM concentrations in various environments, including schools53. In Rwandan context, high concentrations of the PM in schools located in moderately exposed areas which are predominantly peri-urban, may be attributed to several factors, such as the presence of unpaved roads, which results in increased dust resuspension if the wind disturbs the surface. Emissions from the burning of crop residues and a lower density of buildings and larger open spaces in these areas facilitate wind-driven dust from unpaved surfaces and increase the level of PM in school buildings. The findings of this study raise concerns about potential negative health effects on exposed children and emphasise the need for targeted interventions to improve air quality in school environments.

The results of this study indicate that during the dry season the levels of PM10 were higher in schools with high exposure, and the concentrations of PM2.5 tended to also be higher in these schools. This is supported by the findings of other studies which revealed that the concentration of PM2.5 and PM10 tends to be higher in dry season because of increased dust and increased use of firewood for burning, which are the primary sources of PM1052,56,57,58. Scholars report that large particles, such as PM10, are more likely to accumulate near the surface of the ground than small particles, such as PM2.558,59.This finding contrast with the findings of the study conducted in Kenyan schools which indicated that PM2.5 concentrations were higher during the wet season than during the dry season60.

Literature indicates that increased indoor activities and limited outdoor movements during the rainy seasons contribute significantly to the resuspension of settled dust particles, leading to higher indoor PM concentrations61. Several factors also contribute to high levels of PM in environments such as schools in the rainy season62. Studies have shown that the reduction in ventilation, due to the closure of windows and doors during rainy season, may lead to higher indoor PM concentrations63,64. This decreases natural ventilation and can result in higher concentrations of indoor pollutants, including PM2.5 64,65,65. The findings of the current study accentuate the influence of seasonal variations on the level of air pollution in school environments, particularly the differences in PM2.5 and PM10 concentrations. Understanding these seasonal patterns is essential for designing effective interventions to improve indoor air quality in schools regardless of season, and promote respiratory health.

The findings of this study illustrated that an increase in PM2.5 and PM10 concentration is associated with a decline in lung function specifically FVC, FEV1 and PEF among school children. Previous studies corroborate the findings from this research work. Tasmin et al.2019 reports that exposure to PM10 influences FEV1 and PEF only in the dry season66. In previous investigations, it was reported that exposure to PM2.5 leads to a reduction of FVC, FEV1 and PEF22,67. The current findings indicated that increases in PM2.5 and PM10 concentrations in both seasons are associated with a decrease of PEF, FVC, and FEV1 in school children. These findings are consistent with previous studies’ results that indicate the effects of PM exposure on schoolchildren’s lung function23,68. Specifically, in low- and middle-income countries, including those in Africa, exposure to PM significantly affects the respiratory health of children in schools69.

The findings of the current study are consistent with previous studies demonstrating the impact of PM exposure on children’s lung function in Africa. A study conducted in South Africa indicated that exposure to PM2.5, PM10 and PM1 was associated with lower lung function of children, specifically FEV1,FVC, and PEF69. Shumani Phaswana et al. (2022) indicated that increased level of PM2.5 concentration was associated with the peak expiratory flow rate(PEFR)70. Another study conducted in Kenya indicated that the link between exposure to high level of PM2.5 and PM10 concentrations and reduced lung function among schoolchildren60.Moreover, a study conducted across six cities in sub-Saharan Africa demonstrated that many schoolchildren are exposed to high concentrations of air pollutant, including PM2.5, and that such exposed is associated with reduced lung function5. Therefore, the findings of the current study emphasize the need for an effective air quality management strategy in school environments.

In conclusion, our study provides empirical evidence that high levels of PM in school environments have negative effects on children’s lung function.These findings highlight the urgent need for a multidisciplinary approach to develop and implement effective strategies to reduce exposure to PM in school environments and to protect children’s health. To achieve this, effective strategies may include environmental education for children, school staff, and local authorities on maintaining good air quality and dust control measures. Additionally, awareness campaigns should be implemented to educate school children, school staff, parents, and local authorities on the health effects of PM and how to minimise exposure, thereby fostering healthier school environments.

Limitations

Despite the milestones in the current investigation, the study had a few limitations. The study was conducted only in urban settings and included only six schools. Data were collected over an 8-hour sampling period, making direct comparison to the WHO’s 24-hour mean PM values challenging.

The study selected six schools and 180 school children, which limits its generalization. Additionally, the study did not analyse the elemental composition of PM. The findings on children’s exposure are based solely on the time they spend in schools and data on the health status of the children prior to lung function testing were collected from information provided by parents or guardians.

Conclusion

This study assessed the effects of exposure to indoor PM on the lung function of school children in Kigali, Rwanda. The findings indicated that the PM concentration in selected schools exceeds WHO-acceptable air quality guidelines, and exposure to these particles was associated with reduced lung function among the selected children. This research underscores the need for strategies to improve air quality and promote children’s respiratory health in schools. These initiatives should focus on the implementation of air quality awareness programs that educate school communities about practical measures to reduce exposure. Furthermore, future studies should consider longitudinal 24-hour real time monitoring to obtain a more comprehensive exposure profile, extend similar research to schools in rural settings, increase the sample size of both schools and children, and explore the children’s exposure to air pollution in their homes.