DECIPHERING AIR QUALITY PATTERNS THROUGH MULTI-POLLUTANT ASSESSMENT
DOI:
https://doi.org/10.53555/env.v1i1.2574Keywords:
air pollution, multi-pollutant assessment, principal component analysis, K-means clustering, air quality profilesAbstract
Air quality conditions arise from the interactions among particulate and gaseous pollutants, but many of the assessments still focus on the individual pollutants. Thus, the need for integrated analysis for the detection of common patterns as well as different pollutant behavior and recurring air quality profiles. This study aimed to decipher air quality patterns through a multi-pollutant assessment of PM2.5, CO₂, NO₂, SO₂, and O₃. A quantitative cross-sectional analysis was conducted using 5,999 complete observations. Descriptive statistics and distribution plots were used to characterize pollutant concentrations. Pearson correlation analysis assessed inter-pollutant relationships, principal component analysis reduced dimensionality, and K-means clustering identified distinct multi-pollutant profiles. CO₂, NO₂, and SO₂ showed the greatest relative variability, whereas O₃ was comparatively more stable. Moderate positive correlations were observed among CO₂, NO₂, and SO₂, while O₃ showed only weak associations with the remaining pollutants. The first two principal components explained 64.11% of the total variance, and the first three explained 79.18%. PM2.5, CO₂, NO₂, and SO₂ loaded mainly on the first component, whereas O₃ dominated the second. Three clusters were identified: two high-pollution profiles differentiated primarily by O₃ concentration and one lower-pollution profile containing 47.14% of observations. The results show that understanding air quality is best captured from air quality patterns rather than individual measurements. The integrated analytical framework offers a practical foundation for the classification, monitoring and management of pollutants in the environment.
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