Abstract
Thailand continues to grapple with persistent air pollution, particularly in urban and industrial areas, despite overall improvements in the past decade. The ongoing challenge of elevated \(\textrm{PM}_{2.5}\) levels in the northern and northeastern regions is a cause for concern. Key contributors to this issue include transportation, industrial activities, and power generation, with a heightened focus due to the exacerbating effects of climate change. \(\textrm{PM}_{2.5}\) pollution emerges as a significant environmental and health issue, especially during dry periods. In our research, we employ a combination of non-stationary analysis using the Peak–Over–Threshold method, the Generalized Pareto distribution, and an Artificial Neural Network (ANN) to examine extreme \(\textrm{PM}_{2.5}\) pollution events in Khon Kaen Province. We utilize data spanning from 2012 to 2022, sourced from Thailand’s Pollution Control Department, encompassing various air quality parameters such as \(\textrm{PM}_{2.5}\) , PM \({}_{10}\) , CO, NO, NO \({}_{X}\) , NO \({}_{2}\) , SO \({}_{2}\) , O \({}_{3}\) , wind characteristics, temperature, and precipitation. We employ an ANN based on Maximum Likelihood Estimation to determine model parameters, with model performance assessed using Nash–Sutcliffe Efficiency and Root Mean Squared Error. Our research aims to forecast \(\textrm{PM}_{2.5}\) concentrations for different return intervals (2, 5, 20, 50, and 100 years), offering insights into potential \(\textrm{PM}_{2.5}\) levels in Khon Kaen. Additionally, it sheds light on the intricate relationship between air quality and climatic patterns, providing valuable information for addressing this persistent environmental issue.
