A. F. EGHOMWANRE

This study investigated the occupational health risks at an airport, focusing on air pollutant concentrations, their spatiotemporal variation, and the associated respiratory health effects among airport workers (N=129). Air quality monitoring was conducted in four areas (Runway, Parking Lot, Departure area, and Reception) during the morning and afternoon periods for carbon dioxide (CO2 ), coarse particulate matter (PM10 ), and fine particulate matter (PM2.5 ). The data were analysed via ANOVA and paired samples t tests. A survey collected data on workers' sociodemographic, workrelated risks, and self-reported respiratory symptoms. Environmental monitoring identified PM2.5 as the primary air quality hazard. The highest observed mean PM2.5 concentration (24.1 μg/m3) exceeded the strict WHO 24-hour guideline. Statistical analysis confirmed significant spatial variation in PM2.5, with the Runway and Parking Lot acting as hotspots and a highly significant temporal spike in the afternoon (p=0.001). Coughing was the most prevalent reported symptom (57.4% of workers). Chi-square tests revealed a strong correlation between respiratory symptoms (cough, phlegm, shortness of breath, and chest pain) and both smoking and a family history of chronic respiratory issues (p≤0.007 for all). Furthermore, workers demonstrated critical systemic failure in safety protocols, with 83.7% lacking knowledge of the proper use of personal protective equipment (PPE) and 96.9% unaware of routine air quality monitoring. The airport environment presents a substantial and avoidable health risk driven by noncompliant PM2.5 levels and systemic safety management failures. Recommendations include implementing mandatory, hands-on PPE training, establishing a transparent PM2.5 monitoring system in hotspots, and creating an occupational health program to screen and counsel high-risk

Meteorological conditions play an important role in ambient air pollution by influencing the emissions, transport, formation, and deposition of air pollutants both directly and indirectly. The relationships between meteorological parameters and particulate matter concentrations were investigated in ten (10) randomly selected locations within the University of Benin, Benin City, Nigeria. The particulates (PM1.0, PM2.5, and PM10), relative humidity (RH), and temperature were quantified using Dienmern DM106A Air Quality Detector, while the wind speed (WS) was monitored using the Holdpeak 866B digital anemometer respectively for four weeks. The ambient temperature and relative humidity and the concentrations of PM2.5 and PM10 obtained in this study were 34.1 to 36.1℃; 62.9 to 38.1%; 0.;8 to 1.9m/s 25.9 and 30.5µg/m3 .and 57.0 to 62.2 µg/m3. The values obtained were all above the recommended allowable limits. There were generally weak associations between air pollutants and meteorological parameters (R=-0.261, - 0.282, and -0.271; p<0.001)). The air quality index assessment revealed that the particulate matter AQI status was primarily unhealthy. The high levels of particulate matter obtained in the study call for the urgent need for awareness of air pollution-related health effects and preventive measures to reduce the concentrations of Particulate matter pollution. within the University

This study evaluated indoor air quality and examined major risk factors for respiratory symptoms among students who used University of Benin libraries and laboratories. The concentrations of particulate matter, carbon dioxide, and formaldehyde were measured during the morning and afternoon sampling periods, and a structured questionnaire was used to collect demographic, environmental, and health data. Statistical analyses included paired-samples t tests and chi- square tests, with significance set at p < 0.01. The mean pollutant concentrations range from 14.5 ± 0.6 to 24.1 ± 3.1 µg/m³ (PM₂.₅), 27.0 ± 1.5 to 43.2 ± 6.7 µg/m³ (PM₁₀), 399.5 ± 0.3 to 404.4 ± 4.7 ppm (CO₂), and 0.003 ± 0.000 to 0.026 ± 0.012 mg/m³ (HCOH), 24.6 ± 0.2 °C to 32.6 ± 3.7 °C (Temperature), and 69.1 ± 0.5% RH to 78.3 ± 0.6% RH (Relative Humidity).The mean pollutant concentrations did not differ significantly between the morning and afternoon samples, indicating temporal stability. In contrast, environmental factors were significantly associated with symptom prevalence. Notably, a highly significant but inverse relationship emerged for cleaning frequency: participants reporting daily cleaning reported the highest symptom prevalence compared with those reporting monthly cleaning. Demographic variables such as age and sex were not significant predictors of symptoms. These findings indicate that respiratory health risks in these indoor settings are driven less by short-term fluctuations in measured pollutants and more by shortcomings in building maintenance.