A school-based cross-sectional study was conducted between September 2024 to December 2025 in the Buea subdivision, Fako Division, South West Region of Cameroon. Ten secondary schools comprising both private and state-owned institutions were selected by consecutive sampling, prioritizing schools that were accessible and located close to main roads, a security precaution necessitated by the ongoing sociopolitical crisis in the South West Region. The participating schools are identified in this manuscript by anonymized letter codes (Schools A to J).

The study was conducted among students enrolled in selected secondary schools in Cameroon. In the Cameroonian education system, secondary education comprises the first cycle (Forms 1 - 5) and second cycle (Lower Sixth and Upper Sixth). Students may vary in age within each form due to differences in school entry age, repetition, or progression. The minimum sample size was calculated using the Yaro Yamane (2007) formula, n = N/[1 + N(e)2], assuming a population of N = 250 students per school and a margin of error of e = 0.05. This yielded a minimum of 154 students per school, and a total minimum sample of 1540 students across ten schools. Students were selected within schools through stratified random sampling based on proportional class representation. The final enrolled sample was 1674 students.

Students were eligible to participate if they were aged 10 - 17 years, enrolled in a participating school, present on the day of data collection, and had the required parental/guardian consent. Parental/guardian consent forms were distributed in advance of data collection and returned as signed forms before students participated. Students also provided assent before participation. Students were excluded if they were younger than 10 years or older than 17 years, absent on the day of data collection, did not have the required parental/guardian consent, declined participation, or were enrolled in schools located in security-restricted zones.

2.3.1. Sources of Indoor Air Pollution

Sources of indoor air pollution were assessed using the validated WHO indoor environment health exposure indicator checklist [13]. The checklist comprised 22 closed-ended items (Yes/No): eight (8) items assessed PM2.5 exposure indicators, ten (10) items assessed humidity and temperature exposure indicators, and four (4) items assessed CO exposure indicators. For each affirmative response, one point was assigned. Final domain scores were scaled to 20: PM2.5 items were multiplied by 2.5, humidity and temperature items by 2, and CO items by 5. Total scores were categorized as follows: 0 - 5 (low risk), 6 - 10 (moderate risk), 11 - 15 (high risk), and 16 - 20 (very high risk).

2.3.2. Measurement of Carbon Monoxide (CO) Concentrations

Carbon monoxide (CO) concentrations were continuously monitored using Lascar EL-USB-CO data loggers (Lascar Electronics, UK), which recorded CO levels in parts per million (ppm). Devices were positioned one metre from walls and 1.2 metres above ground level, and monitored continuously for 24 hours per school. The device recorded CO at 10s intervals continuously for 24hours monitoring period, yielding approximately 8640 observations per school. Where retrieval of the logger was delayed beyond the intended 24-hour period, observations beyond the predefined monitoring period were excluded. A 24-hour mean CO concentration was calculated for each school and assigned to all participating students within that school since CO exposure was measured at school level.

CO data were collected in six of the ten schools (Schools A to F); the four remaining schools (G to J) declined participation despite thorough explanation of the non-invasive nature of the monitoring equipment. CO measurements were evaluated against the WHO 2021 global air quality guideline average of 4mg/m3 (approximately 3.5 ppm) for 24 hours, defined as normal, and readings exceeding this threshold were classified as abnormal.

2.3.3. Respiratory Symptom Assessment

Respiratory symptoms were assessed using the validated AstraZeneca Respiratory Symptom Questionnaire (RSQ) [14], a four-item instrument that measures the frequency of shortness of breath, rescue inhaler use, activity limitation, and nocturnal awakening due to respiratory symptoms. Each item was rated on a five-point Likert scale, and the composite score was categorized as: mild (1 - 5), moderate (6 - 10), severe (11 - 15), or very severe (16 - 20). The questionnaire was self-administered by students in the classroom setting.

A pilot study was conducted prior to full data collection to assess instrument validity in the local school context. Research assistants received structured training to standardize data collection procedures. Data were captured using Open Data Kit (ODK) installed on mobile devices and stored securely within Kobo Toolbox using password protection.

Data were analyzed using IBM SPSS Statistics, version 25.0 (IBM Corp., Armonk, NY, USA). Sociodemographic, medical, and environmental characteristics were described using frequencies and percentages. CO measurement data were summarized using descriptive statistics (range, minimum, maximum, mean, standard deviation). Bivariate associations between independent variables and respiratory symptom prevalence were assessed using chi-square tests, with significance set at p < 0.05. All variables with a p-value of less than 0.2 in bivariate analysis were entered into a multivariate binary logistic regression model to identify independent predictors of respiratory symptom presence. Results are reported as adjusted odds ratios (aOR) with 95% confidence intervals (CI). Model fit was evaluated using the Nagelkerke R-squared statistic.

Respiratory Symptoms were treated as binary: symptomatic and asymptomatic. Symptomatic symptoms included any symptom recorded, while asymptomatic symptoms were recorded as those with “0” or no symptoms.

Ethical clearance was obtained from the Institutional Review Board of the Faculty of Health Sciences, University of Buea, Cameroon (IRB approval number: [to be inserted]). Administrative authorizations were obtained from the Regional Delegations of Public Health, Secondary Education, and individual school administrations. Written informed consent was obtained from adult participants and from parents or legal guardians of minor participants; written assent was additionally obtained from minor participants themselves. Confidentiality was maintained throughout; participant data were anonymized and no information was shared with school administrations.

A total of 1674 students participated in the study. The majority were female (n = 1101; 65.8%) and aged 13 to 15 years (n = 786; 47.0%). Most students had spent two to four years in secondary school (n = 913; 54.5%), and the largest proportion was in Form 5 (n = 638; 38.1%). Most participants were in ground-floor classrooms (n = 920; 55.0%). The largest school contribution was from School C (n = 389; 23.2%). Full sociodemographic characteristics are presented in Table 1.

Table 1.Sociodemographic characteristics of study participants (n = 1674).

Among all participants, 142 (8.5%) reported a prior diagnosis of a respiratory disease, comprising asthma (n = 52; 3.1%), other unspecified conditions (n = 46; 2.7%), pneumonia (n = 25; 1.5%), and chronic obstructive pulmonary disease (COPD) (n = 19; 1.1%). A total of 101 participants (6.0%) reported current cigarette or tobacco smoking, and 321 (19.2%) reported having at least one allergy. Full medical characteristics are presented in Table 2.

Table 2.Medical characteristics of study participants (n = 1674).

Overall, 452 students (27.0%) reported the presence of respiratory symptoms, while 1, 222 (73.0%) reported none. Among symptomatic students, 73.0% had mild symptoms, 20.2% moderate, 5.8% severe, and 1.0% very severe. Shortness of breath was the most prevalent symptom; 23.7% of all participants reported experiencing it to varying degrees during the past four weeks. Activity limitation due to respiratory symptoms was reported by 20.7% and nocturnal awakening by 21.2%. Detailed symptom frequencies are presented in Table 3.

Table 3. Frequency of respiratory symptoms in the past four weeks (n = 1674).

OverallPrevalenceofRespiratory Symptoms

Out of the 1674 respondents, 1222 (73.0%) reported experiencing none of the respiratory symptoms, while 452 (27%) reported experiencing respiratory symptoms (Figure 1).

Figure 1. Overall prevalence of respiratory symptoms.

3.4.1. PM2.5 Checklist-Based Exposures

All 1674 participants (100%) attended schools exposed to chalk, blackboards, benches, books, and pens were in universal use. Visible dust on surfaces was present in schools attended by 1469 (87.7%) participants. Well-furnished walls were reported in schools of 1520 (90.8%) participants, and cemented floors in 1420 (84.8%). Classroom crowding was reported by 780 participants (46.5%). No school used air conditioning or mechanical air filtration. As a result, all participants faced high or very high PM2.5 source exposure risk: 1315 (78.6%) at very high risk and 359 (21.4%) at high risk (Table 4).

Table 4. PM2.5 Checklist-based exposures.

Overall checklist exposure level of students to PM2.5

Out of the 1674 participants, 1315 (78.6%) had very high risk level of exposure to PM2.5 and 359 (21.4%) for high risk level of exposure (Figure 2).

Figure 2. Checklist exposure level of students to PM2.5.

3.4.2. Humidity and Temperature Checklist-Based Exposures

Of all participants, 731 (43.7%) attended schools in hot and dry climate zones. Most schools were classified as well-ventilated (90.8%), and all schools opened windows regularly (100%). No school used air conditioning or mechanical heating (Table 5).

Table 5. Humidity and Temperature checklist-based exposures.

Overall checklist exposure of study participants to Humidity and temperature

Checklist-based exposure risk distribution for humidity and temperature was: moderate risk 46.8% (n = 784), low risk 27.6% (n = 462), high risk 17.4% (n = 291), and very high risk 8.2% (n = 137) (Figure 3).

Figure 3. Overall checklist exposure of study participants to Humidity and temperature.

3.4.3. Carbon Monoxide Sources

Fuel-powered generators or equipment were present in schools attended by 1343 (80.2%) participants, cooking appliances in 1299 (77.5%), fireplaces or wood stoves in 1017 (60.0%), and vehicles idling near school premises in 675 (40.3%) (Table 6).

Table 6. Checklist CO sources.

Overall checklist exposure of participants to carbon monoxide

Figure 4. Overall exposure level of participants to carbon monoxide.

Out of 1674 participants, 51.6% (863) had a high-risk exposure level to carbon monoxide, 42.2% (706) had Moderate risk, and 6.3% (105) had a very high-risk level of exposure to carbon monoxide (Figure 4).

Descriptive statistics for CO concentrations measured continuously over 24 hours in six schools are presented in Table 4. Mean CO levels ranged from 0.01 ppm (School C) to 20.50 ppm (School D). School D recorded a 24-hour mean CO of 20.50ppm approximately 23.5mg/m³ exceeding the WHO 2021 24-hour air quality guideline of 4mg/m³ (Table 7).

Table 7.Descriptive statistics of CO concentrations measured over 24 hours in six monitored schools.

Note: *Mean exceeds the WHO/EPA normal range of 0.5 - 5 ppm. SD = standard deviation.

3.5.1. Sociodemographic Factors

Chi-square analysis identified significant associations between respiratory symptoms and gender (p = 0.022), school attended (p < 0.001), classroom building level (p = 0.023), and educational form level (p = 0.014). Female students had a higher proportion of symptoms (28.8%) compared to male students (23.6%). School J had the highest symptom prevalence (51.0%). Age and years in secondary school were not significantly associated with symptom presence.

3.5.2. Medical Factors

A prior diagnosis of respiratory disease was strongly associated with symptom presence (p < 0.001): 64.8% of students with a prior diagnosis reported symptoms compared to 23.5% of those without. Allergy status (p < 0.001) and current smoking (p < 0.001) were also significantly associated with symptoms. Among current smokers, 52.5% reported symptoms compared to 25.3% of non-smokers.

3.5.3. Indoor Air Pollution Exposure

At the bivariate level, humidity and temperature checklist-based exposure risk level was significantly associated with respiratory symptoms (p = 0.002). PM2.5 exposure risk level (p = 0.142) and CO exposure risk category (p = 0.073) were not significantly associated with symptom presence. Full bivariate results are presented in Tables 8-9.

Table 8. Bivariate association of indoor air pollution exposure with respiratory symptoms (n = 1674).

Note: *p < 0.05. Statistically significant.

The results of the multivariate logistic regression analysis are presented in Table 7. The final model explained 15.4% of the variance in respiratory symptom presence (Nagelkerke R² = 0.154). Three variables were independently and significantly associated with respiratory symptoms.

Students with a prior diagnosis of a respiratory disease had significantly higher odds of reporting symptoms compared to students without a diagnosis (aOR 3.24, 95% CI 1.40 - 7.53; p = 0.006). Students reporting allergies had 1.66 times higher odds of reporting symptoms than those without (aOR 1.66, 95% CI 1.25 - 2.21; p < 0.001). Male students had significantly higher odds of reporting respiratory symptoms than female students (aOR 1.45, 95% CI 1.12 - 1.90; p = 0.006).

Several schools showed significantly lower adjusted odds of symptom reporting compared to School A, including School H (aOR 0.29, 95% CI 0.14 - 0.61; p = 0.001), School J (aOR 0.19, 95% CI 0.06 - 0.59; p = 0.004), and Schools B, C, D, and F. Students in second-floor classrooms had lower odds of reporting symptoms, though this did not reach statistical significance (aOR 0.46, 95% CI 0.19 - 1.11; p = 0.084). Humidity and temperature exposure risk level, PM2.5 exposure risk level, smoking behaviour, classroom level, and years in school were not independently associated with symptoms.

Table 9.Multivariate logistic regression analysis: independent predictors of respiratory symptoms.

Note: aOR = adjusted odds ratio; CI = confidence interval; ref = reference category. Bold p-values indicate statistical significance (p < 0.05).

The finding that all ten schools exhibited high or very high PM2.5 checklist-based exposure risk is consistent with emerging evidence on classroom air quality in low-income settings. A study similarly demonstrated that indoor PM2.5 concentrations in schools in comparable resource-limited contexts exceeded WHO guidelines [15]. The dominant sources identified in our study chalk and blackboards (100% of schools), visible dust accumulation (87.7%), and crowded classrooms (46.5%) are well-documented contributors to classroom particulate matter in settings where dust-generating teaching materials are universally used. This contrasts markedly with higher-income country contexts; another study in London reported that air purifiers reduced indoor particulate concentrations by up to 57% in schools where chalk had already been largely replaced by digital and whiteboard-based teaching materials [16]. The absence of any air conditioning or mechanical ventilation across all participating schools further limits the dilution or removal of airborne particles.

Regarding humidity and temperature sources, 4 (40.0%) schools were located in hot and dry climate areas. Also, 9 (90.0%) schools were well-ventilated, and all (100.0%) regularly opened their windows. However, none (0.0%) of the schools used air conditioning or heating sources. Out of the 1674 participants, 46.8% (784) had a moderate risk exposure level to Humidity and temperature, 27.6% (462) had low risk, 17.4% (291) had High Risk, and 8.2% (137) had a very high risk level of exposure to humidity and temperature. However, this was different from a study done in South Africa [17] reported high levels of classroom temperature. This could be due to the fact that the classrooms in our study were well ventilated.

The detection of a mean CO level of 20.50 ppm in one school (School D) is a finding of considerable public health significance. This value exceeds the WHO/EPA normal range ceiling of 5 ppm by a factor of four, and the recorded maximum concentration of 229.5 ppm at that location presents a risk of acute toxicity. In comparison, a study [18] reported mean CO levels of 0.8 ppm among primary school and college students in a comparable study context. The likely contributors to the elevated CO in School D include the combination of fuel-powered generators, cooking appliances, and the school’s physical positioning relative to vehicle traffic. The inability to collect CO data from four schools due to administration refusal is itself a noteworthy finding: it suggests limited awareness of, or resistance to, environmental health surveillance in some school administrations, and underscores the need for regulatory frameworks that mandate air quality monitoring in educational institutions.

The prevalence of respiratory symptoms in this study (27.0%) is consistent with the broader evidence base indicating that a significant minority of school-age children experience respiratory health impairment attributable to indoor air pollution exposure. The finding that nearly 7% of symptomatic students (representing approximately 1.9% of the total sample) experienced severe or very severe symptoms is clinically important, as these students may be experiencing meaningful educational disruption and unmet healthcare needs. Fsadni et al. (2018) reported similar patterns of respiratory health impact associated with school air quality in Mediterranean school children [19].

The finding that individual susceptibility factors prior to respiratory disease diagnosis (aOR 3.24), allergy status (aOR 1.66), and male sex (aOR 1.45) were the strongest independent predictors of symptoms, while PM2.5 and CO exposure risk levels were not independently significant in adjusted analyses, warrants careful interpretation. This pattern does not diminish the importance of the exposure findings; rather, it reflects the complex relationship between aggregate environmental exposure and symptom expression in a population with heterogeneous susceptibility.

The observed association between male sex and higher odds of respiratory symptoms (aOR 1.45) is consistent with findings from [20], who reported higher asthma rates in males in a Vietnamese school-based study. This contrasts with findings from household-based studies in Cameroon, which have reported greater female vulnerability, likely reflecting the greater domestic exposure to cooking smoke among women and girls, an exposure pathway that is not present in the school setting examined in our study.

The strong school-level variation in symptom odds, with seven schools showing significantly protective associations relative to School A, suggests that unmeasured school-specific factors including physical infrastructure, proximity to traffic, school management practices, and possibly health-seeking behaviour among students play an important moderating role. School A’s elevated symptom burden may relate to its proximity to a main road, although this hypothesis requires further investigation.

This study has several strengths. It is, to our knowledge, the first study to systematically characterize classroom air quality in Cameroonian secondary schools and to examine its association with respiratory symptoms in a large, representative sample. The use of continuous environmental monitoring (Lascar CO data loggers), validated instruments (WHO checklist and AstraZeneca RSQ), and robust multivariate analytic methods strengthens the credibility of the findings. The large sample size (n = 1674) provides adequate power to detect associations.

Important limitations must be acknowledged. Firstly, CO monitoring was not possible in four of ten schools due to lack of authorization attributed to fear of the unknown, which may have introduced selection bias in the environmental exposure analysis. Secondly, PM2.5 analyses were restricted to source checklist-based risk scoring rather than measured concentration data. Thirdly, the cross-sectional design precludes causal inference, and residual confounding by unmeasured factors cannot be excluded. Fourthly, the AstraZeneca RSQ was originally designed for adult asthma and COPD patients, and its applicability to adolescent school populations may not fully capture the spectrum of respiratory manifestations in this age group. Furthermore, school selection was limited and influenced by security conditions prevailing in the South West Region, potentially restricting the generalizability of findings to accessible urban and peri-urban schools. Lastly, students were clustered within the six schools and environmental exposures were measured at the school level. The primary logistic regression did not explicitly model within-school correlation. Given the small number of schools, school-level effects could not be reliably estimated within the available analytical framework.