Summary: With the development of industrialization in China, air pollution has brought great harm to human daily life, so it is very important to analyze the factors which influence the air quality badly. Essentially, the process of finding the key air pollution influence elements is eliminating the redundant and uninformative as well as noisy factors, then obtaining the key ones, so each weather element has two states: being selected as a key air pollution factor or not. Therefore, selecting main factors for air pollution is a binary optimization problem actually, and binary ant colony optimization (BACO) combined with fractal theory is introduced and applied to solve it. At the initial stage of BACO, due to the deficient of pheromone, the whole ant population need a long time to construct a path with distinct discrepancy level of pheromone, then binary particle swarm optimization (BPSO) is involved to improve the BACO. Furthermore, the datasets of Beijing, Guangzhou and Shanghai are used to conduct experiments, also 10-fold and SVM are involved to analyze the classification accuracy. Numerical experiments reveal that our method has higher forecasting accuracy and provides a good tool for environmental improvement.