| dc.description.abstract | Poor air quality can have negative impacts on human health and the environment, especially in urban areas such as Medan City, which experiences high levels of industrial and transportation activity. The presence of numerous interrelated parameters in air quality data complicates direct analysis. Therefore, a statistical method is needed to simplify the data without losing essential information. This study aims to identify the dominant factors influencing air quality in Medan City using the Principal Component Analysis (PCA) method. The data consists of ten parameters: PM₂.₅, PM₁₀, CO, SO₂, NO₂, O₃, temperature, humidity, wind speed, and air pressure, observed over a 30-day period. The analysis was conducted using PCA with the aid of IBM SPSS Statistics software. Data adequacy was tested using the Kaiser-Meyer-Olkin (KMO) measure and Bartlett’s Test of Sphericity, showing that the data is suitable for PCA (KMO = 0.573; p < 0.001). The PCA successfully reduced the ten variables into four principal components, which cumulatively explain 76.885% of the total data variation. These components were interpreted as: (1) atmospheric and photochemical component, (2) particulate and air pressure dynamics component, (3) combustion emissions and sulfur pollution component, and (4) nitrogen gas and thermal effect component. The results indicate that PCA is effective in reducing data complexity and identifying key parameters that significantly influence air quality in Medan City. These findings can serve as a basis for more targeted and efficient air pollution control policies. | en_US |
