As an emerging technique, machine learning algorithms can capture the complex nonlinear relationships with interactions. Compared to the traditional statistical method, machine learning is more reliable for the air pollution analysis. PM2.5 and O3 are the two secondary ambient pollutants. In this study, we plan to combine machine learning techniques, ground observation, satellite data to generate high resolution PM2.5 products. Students should seek instructor's approval to take this course.