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Abstract- Air pollution is a significant environmental issue with extensive impacts, particularly concerning particulate matter smaller than 2.5 microns (PM2.5), which poses serious public health risks,especially respiratory diseases such as various diseases, ischemic heart disease, strokes, chronic obstructive pulmonary disease, tracheal, bronchus, lung cancer, and even increased premature death rates. Northern Thailand is one of the areas with the most severe PM2.5 problems, especially during the summer (February to May), primarily due to the large amount of agricultural field burning and forest fires by ethnic groups after the harvest season.This research proposes a hybrid model of Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM) for PM2.5 concentration forecasting using satellite images of four environmental variables: aerosol optical depth, temperature, precipitation, and ozone. These variables are important factors in the occurrence of PM2.5. The efficiency of the CNN-LSTM model was assessed by comparing performance with classification deep learning models (CNN, LSTM), Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX), and Multiple Linear Regression (MLR). The findings indicate that The CNN-LSTM model achieves higher accuracy than the other models, achieving an R2 of 98.38%, MAPE of 2.47%, and significantly lower RMSE (3.0672 μg/m3) and MAE (0.8560 μg/m3). In conclusion, this research highlights the important implications of supporting government policy formulation and public preparedness to address the PM2.5 problem, which varies in severity across seasons.

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