This study was conducted in Benin City, the administrative capital of Edo State, which is situated within the South‒South geopolitical zone of Nigeria. The city occupies a strategic geographical position, roughly defined by coordinates of 6°20′N latitude and 5°37′E longitude, with a mean elevation of approximately 80 m above sea level. As a primary urban hub, Benin City has experienced rapid urbanization and population growth, with current estimates exceeding 1.7 million residents [20]. This high population density necessitates a robust healthcare infrastructure; however, the city’s medical institutions face unique environmental challenges.

The degradation of IAQ within these healthcare settings is driven primarily by a combination of infrastructural, operational, and energy-related factors. The sampling environment is defined by specific architectural and operational constraints, including restricted ventilation systems and a heavy reliance on diesel-powered energy sources. These factors, in addition to high patient turnover and overcrowding, were identified as the primary IAQ stressors during the site characterization phase of this study.

To ensure a representative assessment of the city’s healthcare environment, sampling was conducted at two major facilities representing different administrative and operational structures: a public tertiary institution and a private multispecialty hospital. The public health care institution was established in 1973. It is located along the Ugbowo-Lagos Road in the Uselu district. As one of the largest teaching hospitals in Nigeria, it maintains a capacity of approximately 900 inpatient beds and houses numerous specialized clinical departments. Its sprawling complex provides a diverse environment for assessing pollutant variations in a high-volume, public-sector setting. The private health care institution is a multispecialty healthcare facility founded in 1989. It is situated at Giwa-Amu, off Airport Road. The hospital provides a wide array of specialized services, including Paediatrics, Obstetrics and Gynaecology, and General Surgery. Its inclusion in the study allows for a comparative analysis of IAQ between public-access and private-sector healthcare management models.

The sampling points were selected on the basis of high occupancy levels, exposure duration, and the presence of localized emission sources. The precise locations of these points were mapped using GPS for spatial accuracy. Sampling points were selected across the following functional areas: emergency wards (EM), antenatal wards (AT), pediatric wards (PE), reception areas (RE), outpatient departments (CO), central laboratories (LB), and car parks (CP) (Figure 1). The selection utilized a randomized approach within these high-occupancy zones to ensure that the data captured reflected the diverse air pollutant profiles typical of a tropical urban clinical environment.

To capture the most representative data regarding human exposure, the sampling campaign was conducted over an eight-week period, spanning from July to September 2025. This timeframe coincides with the peak of the rainy season in Benin City. Sampling occurred on a weekly basis, specifically during peak operational hours (08:00 to 16:00), when patient turnover and clinical activities were at their highest levels. The concentrations of airborne pollutants were quantified using specialized handheld digital monitors, specifically the BR-Smart-126 series for particulate matter and organic vapours. Prior to each monitoring session, the sensors were zeroed in outdoor air away from immediate local sources to establish a baseline and ensure the reliability of subsequent measurements. BR-Smart-126 uses laser scattering sensors to measure PM_2.5 and PM₁₀ concentrations within a range of 0–999 μg/m³ (±10% precision), as well as electrochemical and semiconductor sensors for HCHO and TVOCs, both of which feature a resolution of 0.001 µg/m³. To ensure that the data accurately reflected the breathing zone of both healthcare personnel and patients, all the instruments were mounted on stable platforms at a standardized height of 1.5 m above ground level, strategically positioned away from immediate obstructions or direct expiratory breath exhaust. To maintain statistical rigor and account for transient fluctuations, readings at each sampling point were taken in triplicate. The resulting mean concentrations were subsequently calculated and benchmarked against the WHO 24-hour air quality guidelines [6] to determine the level of environmental risk.

To evaluate the potential health impacts of chronic exposure to indoor air pollutants (PM_2.5, PM₁₀, and HCHO) on hospital staff and patients, a standard HHRA was performed. The assessment followed the framework established by the U.S. EPA [21], focusing on both non-carcinogenic and carcinogenic risks.

The average daily EC for each pollutant was calculated to determine the dose inhaled by individuals within the hospital microenvironments. The calculation used the following formula:

(1) E C = C × E T × E F × E D ÷ A T

where:

C: average concentration of the pollutant (μg/m³).

ET: exposure time (hours/day).

EF: exposure frequency (days/year).

ED: exposure duration (years).

AT: averaging time (hours).

For hospital staff, exposure factors were adopted from the U.S. EPA [21], with an 8-hour shift and a 25-year career duration assumed.

The non-carcinogenic health risk for particulate matter and HCHO was determined using the HQ. This is the ratio of the EC to the reference concentration (RfC), which represents the level below which no adverse health effects are expected [22].

(2) H Q = E C ÷ R f C

According to [23] an HQ > 1 indicates the potential for adverse non-carcinogenic health effects, whereas an HQ < 1 suggests that the risk is negligible. For PM_2.5 and PM₁₀, the mean concentrations were also compared against the WHO Global Air Quality Guidelines [6] to assess the degree of exceedance and associated respiratory risks.

HCHO is classified as a known human carcinogen (Group 1) by the IARC. The lifetime cancer risk (LCR) associated with HCHO inhalation was estimated by multiplying the EC by the inhalation unit risk (IUR) factor [23, 24].

(3) L C R = E C × I U R

The IUR values were sourced from the IRIS Toxicological Review of Formaldehyde [25]. The calculated risk was interpreted on the basis of the following threshold: LCR < 1 × 10⁻⁶: negligible risk. 1 × 10⁻⁶ to 1 × 10⁻⁴: acceptable/tolerant range. LCR > 1 × 10⁻⁴: Unacceptable risk, requiring immediate mitigation [23, 24].

To assess the relationship between environmental air quality and human health, a modified, well-structured, self-administered questionnaire was deployed among hospital personnel. The instrument was designed to capture a comprehensive profile of the study population and was categorized into four distinct modules: demographic profile, occupational exposure and risk factors, respiratory health symptoms and awareness and institutional control measures.

The target population for this study comprised hospital personnel working across various clinical and administrative departments in the two selected healthcare facilities in Benin City. The total accessible population was identified as comprising 246 staff members (164 from the public healthcare facility and 82 from the private hospital). To ensure that the study was adequately powered while maintaining statistical precision, the sample size was determined using the Yamane formula [26]:

(4) n = N 1 + N e 2

where: n = required sample size; N = total population (246); e = margin of error (0.05 or 5%).

On the basis of this calculation, the minimum required sample size was determined to be 152 respondents.

A stratified random sampling technique was employed to ensure a representative and proportional selection of participants from both the public and private sectors. The two hospitals were treated as distinct strata to account for differences in staff density and institutional characteristics. Proportional allocation was used to distribute the calculated sample size (n = 152) across the two strata, ensuring that each hospital’s contribution to the final sample was reflective of its total population size.

A pilot study (pre-testing) was conducted among a representative sub-sample of hospital workers to ensure the clarity of the questions and the ease of completion before full-scale administration. The reliability of the structured questionnaire was established through measures of internal consistency. While standardized protocols were strictly followed during data collection to minimize bias, the instrument’s internal reliability was statistically verified using Cronbach’s alpha coefficient [27]. The analysis yielded a Cronbach’s alpha value of 0.87, which exceeded the recommended threshold of 0.70.

Data were managed and analysed using IBM SPSS Statistics for Windows, version 22.0. The air quality measurements (pollutant concentrations) were summarized using means and standard deviations. Demographic and awareness data from the questionnaires are expressed as frequencies and percentages. Variations in air pollutant concentrations across different hospital wards and between public and private facilities were determined using ANOVA. The chi-square (χ²) test of independence was employed to compare the prevalence of respiratory symptoms and awareness levels between the two hospital types. Furthermore, a binary logistic regression model was used to determine the odds ratios (ORs) for reported respiratory symptoms between the hospitals after adjusting for cofounders such as age, sex, level of education, smoking status, history of respiratory symptoms and duration of work hours. For all the statistical tests, a two-tailed p value < 0.05 was established as the criterion for statistical significance.

The mean concentrations of indoor air pollutants across various departments in private hospitals are summarized in Table 1, with the corresponding spatial analysis results shown in Table S1. During the morning session, the PM_2.5 concentrations remained relatively uniform across departments (p = 0.262), ranging from 13.3 ± 0.7 µg/m³ in the PE to 15.3 ± 0.6 µg/m³ in the CP. However, highly significant spatial variation was observed in the afternoon (p = 0.005), driven primarily by a sharp spike in the AT (56.3 ± 24.1 µg/m³), which significantly exceeded the WHO threshold of 15 µg/m³. Similarly, afternoon PM₁₀ levels significantly differed spatially (p = 0.004), peaking at 74.0 ± 27.7 µg/m³ in the AT. The HCHO concentration exhibited the highest degree of spatial variability in both the morning (p = 0.001) and the afternoon (p = 0.001). The morning levels of the EM (141.7 ± 13.3 µg/m³) and LB (125.1 ± 18.3 µg/m³) were significantly higher than those of other departments and above the WHO limit (100 µg/m³).

While TVOCs were excessively high across all the sampled points, with values exceeding the 300 µg/m³ limit by nearly ten-fold in some wards, the statistical results confirmed that concentrations did not vary significantly by location during either diurnal period (p > 0.05), with values exceeding the 300 µg/m³ limit by nearly ten-fold in some wards.

Table 2 presents the indoor air pollutant profile for the public hospital. The morning concentrations of PM_2.5 were consistently higher than the WHO standards across all departments, and the ANOVA results (Table S1) indicated no significant spatial difference during this period (p = 0.677), suggesting a uniform pollutant load. However, significant spatial variation emerged in the afternoon (p = 0.0024) with the EM (13.8 ± 0.6 µg/m³) and CO (14.7 ± 1.1 µg/m³) maintaining higher levels than the others did. PM₁₀ followed a similar trend, with significant spatial differences occurring only in the afternoon (p = 0.005). In contrast to those in private facilities, the HCHO levels in public hospitals were generally lower and compliant with the WHO standards, yet they exhibited significant spatial heterogeneity (p = 0.001). The highest morning concentration occurred in the CO (46.8 ± 10.9 µg/m³). TVOC levels mirrored the extreme elevations seen in the private facility, with highly significant spatial variation both in the morning (p = 0.002) and in the afternoon (p = 0.001). In the AT, the highest morning TVOC concentration (3,769.9 ± 490.3 µg/m³) was recorded, whereas in the EM, it peaked during the afternoon (3,689.5 ± 405.1 µg/m³).

The results of the paired t test comparing morning (M) and afternoon (A) sessions for both healthcare facilities are presented in Table 3. In the public hospital, significant diurnal variations were observed for particulate matter and HCHO (p < 0.05). Specifically, the mean reductions in PM_2.5 and PM₁₀ were 6.19 µg/m³ (t = 19.78) and 9.89 µg/m³ (t = 13.06), respectively, in the afternoon. The HCHO concentration in the public facility also decreased significantly, by a mean of 9.83 µg/m³ in the afternoon (p = 0.001). However, the variations in TVOC in the public facility were not statistically significant (p > 0.05). In the private hospital, the diurnal trend differed notably. PM_2.5 and PM₁₀ levels did not significantly differ between the morning and afternoon (p > 0.05). Like those in the public facility, the HCHO concentrations in the private hospital were significantly higher in the morning than in the afternoon (p = 0.001). TVOC variations remained statistically insignificant across both diurnal periods in the private facility (p = 0.329).

The independent t test results for comparisons of the air quality parameters between the two hospital types are shown in Table 4. During the morning sessions, significant differences were observed across nearly all the parameters. Compared with private hospitals, public hospitals recorded significantly higher morning concentrations of PM_2.5 (t = 11.64; p = 0.001 and PM₁₀ (t = 10.09; p = 0.001). Conversely, the private hospital exhibited significantly higher morning levels of HCHO (mean Diff. = –66.5 µg/m³; p = 0.001). In the afternoon, the significant difference in particulate matter (PM_2.5 and PM₁₀) between the two hospitals disappeared (p > 0.10). However, compared with public facilities, private hospitals continued to have significantly higher levels of HCHO (p = 0.001). Interestingly, there were no statistically significant differences in TVOC concentrations between the public and private facilities during either the morning (p = 0.577) or afternoon (p = 0.327) sessions, suggesting that both facilities faced similarly high organic vapour loads.

The ambient meteorological conditions (temperature and relative humidity (RH)) across all the sampling points are detailed in Table S2. Statistical analysis revealed that these factors significantly influenced the IAQ dynamics. Specifically, morning RH was significantly positively correlated with (PM₁₀, r = 0.481, p < 0.001), while afternoon RH demonstrated a significant positive correlation with morning HCHO levels (r = 0.414, p < 0.001), as detailed in Table 5.

The results of the non-carcinogenic risk (HQ) and LCR are summarized in Figures 2 and 3, respectively. In accordance with the U.S. EPA criteria, an HQ > 1.0 indicates a threshold at which adverse non-carcinogenic health effects, including respiratory irritation and airway hypersensitivity, are likely to occur. A cancer risk (CR) > 1.0 × 10^–4 represents a carcinogenic risk exceeding the acceptable safety range. In the private facility (Figure 2), particulate matter (PM_2.5 and PM₁₀) maintained an HQ < 1.0 across all wards and sessions. In contrast, HCHO presented a critical health risk profile. During the morning session, the HCHO HQ values were significantly > 1.0 at 85.7% of the sampled points. The most alarming risks were observed in the EM (HQ = 10.04), LB (HQ = 8.86), and RE (HQ = 8.01), indicating exposure levels eight to ten times higher than the safety limit. The LCR for HCHO in private hospitals mirrored these findings. Most wards recorded a CR > 1.0 × 10⁻⁴, with the EM (4.52 × 10⁻⁴) and LB (3.99 × 10⁻⁴) having the highest values. Notably, the individual peak ILCR reached a maximum value of 1.49 × 10⁻⁴ at the highest exposure point during worst-case peak tracking within the private facility’s EM. Compared with the private facility, the public facility (Figure 3) presented slightly higher particulate HQs, although all remained within safe limits (HQ < 0.30). The HCHO risk profile was more localized but still clinically significant. The number of morning sessions in the CO (HQ = 3.32), EM (HQ = 1.94), and PE (HQ = 1.30) exceeded the unity threshold (HQ > 1.0), suggesting the potential for acute respiratory distress and allergic triggers in these environments. In terms of carcinogenic risk, public hospitals were generally safer than private facilities, with most CR values falling within the target safety range (< 1.0 × 10⁻⁴).

The sociodemographic profile of the hospital workers (N = 152) is detailed in Table 6. The study population was predominantly female (62.5%), with a higher concentration of female workers in both the public (60.4%) and private (66.7%) sectors. Statistical analysis revealed no significant difference in sex distribution between the two facilities (p = 0.451). The age distribution was similarly comparable across both hospitals (p = 0.318), with the largest cohort being those aged 18–30 years (39.5%), followed by the 31–40 years group (26.3%). Only a small fraction of the workforce (5.3%) was older than 50 years. Educational attainment was remarkably high, with 88.8% of all respondents possessing tertiary-level education, and no significant variation was observed between the public and private sectors (p = 0.074). Educational attainment was remarkably high, with 88.8% of all respondents possessing tertiary-level education, and no significant variation was observed between the public and private sectors (p = 0.074). A significant disparity was, however, observed regarding occupational role (p = 0.011). Compared with public hospitals, private hospitals had a significantly higher proportion of clinical staff Doctors/Nurses (37.3% in private vs. 21.8% in public). Conversely, public hospitals were characterized by a higher percentage of laboratory personnel (37.6%) and maintenance/security staff (15.8%).

A comparative analysis of self-reported health effects among public and private hospital workers is summarized in Table 7. The results indicate a high baseline prevalence of respiratory irritation across the entire study population (N = 152). Coughing was the most frequently reported symptom, affecting 78.3% of the total workforce. A highly significant difference was observed between facilities (p = 0.004); workers in the public hospital reported a substantially higher prevalence of persistent cough (85.1%) than their counterparts in the private hospital did (64.7%). Similarly, chest pain was significantly different (p = 0.016), with 36.6% of public hospital workers reporting symptoms compared with only 17.6% in private facilities. This suggests a higher burden of thoracic discomfort among personnel in the public sector. While irritation (eyes/skin) was higher in public hospitals (32.7%) than in private hospitals (25.5%), this difference did not reach statistical significance (p = 0.363). Other allergic and asthma indicators were prevalent but did not significantly differ between the two hospital types: sneezing (65.1%, p = 0.536), wheezing (33.6%, p = 0.091), and breathlessness (22.4%, p = 0.752).

The prevalence of self-reported health symptoms among the various occupational groups is summarized in Table 8. Overall, a high burden of respiratory symptoms was observed across the entire study population (N = 152), with cough (78.3%) and sneezing (65.1%) being the most frequently reported complaints. Subgroup analysis revealed that cleaning staff experienced the highest relative prevalence of several critical symptoms. Specifically, 100% of the cleaners reported experiencing a cough, 90% reported sneezing, and 70% reported chest pain. Although the Pearson chi-square test for chest pain did not reach the traditional threshold for statistical significance (p = 0.105), the high absolute percentage among cleaners suggests a clinically relevant occupational risk. Eye and skin irritation trends toward statistical significance (χ²=11.623; p = 0.071), with the highest rates reported by cleaners (50%) and catering staff (50%) compared with a remarkably low rate among maintenance/work personnel (4.8%). Other symptoms, such as wheezing and shortness of breath, were distributed more evenly across the roles, although catering staff reported the highest frequency of wheezing (66.7%).

To further examine the relationship between the workplace environment and respiratory health, a logistic regression analysis was performed (Table 9). The results revealed that compared with workers in private facilities, workers in public hospitals were found to have significantly higher risks for both coughing and chest pain. According to the crude model (COR), public hospital workers were more than three times more likely to report persistent cough (COR = 3.127; 95% CI: 1.413–6.919; p = 0.005). This risk remained robust even after we adjusted for sociodemographic confounders, with an adjusted odds ratio (AOR) of 3.009 (95% CI: 1.277–7.088; p = 0.012). Similarly, the risk for chest pain was significantly higher in the public hospital environment (AOR = 2.681; 95% CI: 1.093–6.578; p = 0.031). These elevated odds ratios suggest that the higher particulate matter levels and localized HCHO “hotspots” in the public facility (as characterized in Tables 2 and 6) correlate with a significantly higher probability of adverse respiratory outcomes among the staff.

Table 10 summarizes the awareness levels and perceived institutional monitoring practices among the workers (N = 152). General awareness of health effects related to air pollution was exceptionally high (94.1%) and uniform across both hospitals (p = 0.989). Similarly, almost all the respondents reported awareness of personal protective equipment (PPE) (98%). Despite this high level of general awareness, specific technical knowledge and institutional support were significantly lacking. Fewer than half of the respondents (49.3%) were aware of regulatory thresholds for indoor air pollutants. Only 9.2% of the total workforce had any knowledge of government interventions related to IAQ. A mere 20.4% of workers were aware of any ongoing air pollution monitoring within their facilities, with rates as low as 13.7% in the private hospital.

Despite the significant findings, this study has several limitations. First, the reliance on self-report questionnaires for respiratory symptoms may introduce recall bias among participants. Second, the monitoring campaign was restricted to the rainy season; therefore, the results may not reflect the seasonal fluctuations in the PM and HCHO levels that could have occurred during the drier Harmattan period. Third, the study utilized the BR-Smart-126 series, a low-cost consumer-grade sensor. The electrochemical sensors utilized for HCHO monitoring are subject to cross-sensitivity with alcohol-based disinfectants and sanitizers prevalent in hospital settings. Consequently, the high HCHO readings (HQ > 10) likely reflect a combined chemical burden rather than isolated HCHO exposure. As field calibrations against reference-grade instruments were not performed, these values should be interpreted as indicating high total chemical loading and should be used as a baseline for future high-precision studies. Finally, while the logistic regression was adjusted for age, sex, and smoking status, it did not account for the workers’ home indoor environments. Domestic exposures, such as the use of biomass fuels for cooking or residences near high-traffic areas, are significant confounders. The lack of data on off-duty air quality exposure remains a limitation, as domestic conditions may independently contribute to the high prevalence of respiratory symptoms observed.

In conclusion, the IAQ in the assessed healthcare facilities poses a significant and immediate threat to the respiratory health of hospital personnel. While particulate matter remained within safe thresholds, HCHO and TVOCs emerged as critical toxicological hazards, particularly in private emergency and laboratory units where CR and non-carcinogenic HQ exceeded international safety limits by up to ten-fold. The high prevalence of persistent cough, chest pain, and AHR among the predominantly young, female workforce is directly associated with clinical activities, poor building design, and a “buildup effect” caused by inadequate ventilation. Furthermore, a profound “knowledge-action gap” exists; despite high general health awareness among staff, the lack of institutional monitoring and regulatory oversight leaves workers vulnerable to toxic exposure levels that are largely ignored by current administrative frameworks. To mitigate these risks, hospital management should prioritise the installation of advanced air filtration systems, such as activated carbon filters specifically designed to capture gaseous VOCs and HCHO. Facilities must transition from a reliance on natural ventilation to regulated mechanical systems that prevent the stagnant night time “buildup effect,” alongside the implementation of stricter protocols for the storage and use of formalin-based fixatives. Architecturally, future hospital designs should ensure greater setbacks from high-traffic areas and CP to reduce outdoor pollutant infiltration. Finally, health institutions must bridge the awareness gap by establishing continuous IAQ monitoring programs and transparent reporting systems, ensuring that highly educated medical staff are not only aware of the risks but also actively protected by enforceable institutional safety thresholds and government interventions.