Data mining of particulate matter (PM2.5) using Principal Component Analysis (PCA) in Isfahan - Sohrab Hasheminejad; Samane Shahrabi; Reza Peykanpour Fard (اکولوژی انسانی, 2024)
Elevated concentrations of particulate matter (PMs), particularly PM2.5, are significantly influenced by various anthropogenic activities, including industrial processes, population growth, and fossil fuel combustion, especially during peak urban hours. The burgeoning volume of environmental data often leads to crucial decisions being made with inadequate information. Data mining techniques offer a powerful approach to extract knowledge, compress data, and facilitate informed environmental decision-making. Regarding PM2.5 in Isfahan, understanding the characteristics and origins of each monitoring station is paramount. Specifically, determining the influence of various factors on each station and classifying them based on pollution source is crucial. Principal Component Analysis (PCA) was employed for this purpose. This study suggests that the urban stations (Parvin, Kharazi, Rodki, Ahmadabad, and Ostandari) are likely heavily influenced by fossil fuel combustion from transportation and building heating. The Estandari station, located in the city center with high traffic density, requires special attention due to its relatively large green space, which may influence particulate deposition and accumulation. The Segzi plain, characterized by severe wind erosion, predominantly exhibits PMs of natural origin. Mitigation strategies, such as mulching or afforestation with drought-resistant plants, are necessary to reduce wind erosion and subsequent particulate dispersion. Finally, the Mubarakeh area, a significant industrial hub in Isfahan, displays PMs primarily originating from industrial activities.