This study assessed the seasonal dynamics and pollutant interactions of indoor air quality (IAQ) across three coastal communities in Akwa Ibom State, Nigeria, viz, Ibeno, Eastern Obolo, and Ikot Abasi, during the wet and dry seasons. IAQ parameter measurements were carried out to determine concentrations of particulate matter (PM₁, PM₂, PM₅, PM₁₀), carbon monoxide (CO), carbon dioxide (CO₂), nitrogen monoxide (NO), nitrogen dioxide (NO₂), ozone (O₃), relative humidity (RH), and temperature (TEMP) using Portable Air Quality Monitors (Fluke 985 Particle Counter, Fluke 975 AirMeter, and Aeroqual Series 500 Monitor). Descriptive statistics, correlation matrix analysis, and Principal Component Analysis (PCA) were used to explore spatial-seasonal variations and pollutant interrelationships. Elevated pollutant levels were recorded during the dry season, especially for PM and combustion gases, driven by limited ventilation, fuel-based cooking, and poor indoor air circulation. Ibeno recorded high PM concentrations in the dry season, while Ikot Abasi showed CO and NOx peaks, indicating indoor fuel usage. Eastern Obolo had moderate pollution but strong seasonal contrasts, with wet seasons showing lower pollutants accumulation but higher RH levels. Correlation analysis revealed strong positive relationships among PM₁, PM₂, PM₅, PM₁₀, CO, and CO₂ in the dry season, pointing to indoor combustion as a common source, while O₃ showed weak or negative correlations with NO, reflecting typical indoor photochemistry. PCA identified three main components: PC1 (over 50% variance) linked to PMs and combustion gases, PC2 to gaseous pollutants (NO, NO₂, O₃), and PC3 to meteorological factors (RH, TEMP). These patterns were consistent across locations but varied by season and space. The results underscore the health risks of poor indoor air quality in rural and peri-urban households and highlight the need for cleaner fuels, better ventilation, and seasonal exposure management. The combination of correlation analysis and PCA provides a strong basis for understanding pollutant dynamics and guiding community-based IAQ interventions.
Abai et al. (Sat,) studied this question.