As particulate matter in the air can cause several kinds of respiratory and cardiovascular diseases, the air quality information predicting attracts more and more attention. Knowing these information in advance is very important to protect human from health problems. With the development of computer technology, the data we can collect is increasingly becoming fine-grained. Most important of all, they need to be analyse in real-time. However, existing methods could not meet the demand of real-time analysis. In this paper, we predict air quality based on a Ipython implementation of random forest algorithm. First, a distributed random forest algorithm is implemented using Ipython on the basis of resilient distributed dataset and shared variable. Then, we build an air quality prediction model using the parallelized random forest algorithm. The proposed method is evaluated with real meteorology data obtained from IIT Madras. The experiment results show that the proposed method is fast in predicting concentration level of PM2.5. And the results also prove the effectiveness and scalability of our method when deal with big data. © IAEME Publication.