A Review of PCA and FA Applications in Air Pollution Source Apportionment Studies (۲۰۱۸–۲۰۲۵)

Air pollution remains a critical environmental and public health concern, particularly in rapidly urbanizing and industrializing regions. Identifying and quantifying the sources of airborne pollutants is a fundamental step toward developing effective mitigation and policy strategies. Receptor models, especially Principal Component Analysis (PCA) and Factor Analysis (FA), have emerged as powerful statistical tools in air quality research. These multivariate techniques allow for the reduction of complex datasets and the identification of latent variables representing major pollution sources. This review provides a comprehensive evaluation of recent applications of PCA and FA in air pollution studies published between ۲۰۱۸ and ۲۰۲۵. A synthesis of selected studies across different global regions reveals that these techniques have been successfully applied to characterize pollutants such as PM₂.₅, PM₁₀, VOCs, trace elements, and organic carbon compounds. Commonly identified sources include vehicular emissions, industrial processes, biomass burning, construction dust, and natural soil dust. The comparative analysis shows that PCA is often preferred for exploratory analysis and dimensionality reduction, whereas FA is more frequently used for grouped source interpretation. Notably, studies that combined PCA or FA with other receptor models (e.g., PMF or CMB) demonstrated improved accuracy in source resolution and interpretability. Despite some limitations, such as subjectivity in factor naming and assumptions about data normality, both techniques continue to provide valuable insights into the origin and behavior of atmospheric pollutants. This review underscores the importance of these methods in environmental monitoring and supports their integration with complementary tools for more robust air quality management and policy development.

کلیدواژه ها:

Air Pollution ، PCA ، FA ، Receptor Models