In recent years, the frequent fouling accidents has posed a serious threat to people's life and property safety. Due to the wide distribution of pollution sources and variable meteorological factors, it is a very time-consuming and labour-intensive task to map the pollution distribution by traditional methods. In this work, a study on the mapping of pollution distribution based on satellite remote sensing is carried out in Yunnan Province, China, as an example. Several machine learning methods (e.g. KNN, SVM, etc.) are used to analyze the effects of conditions such as multiple air pollution data and meteorological data on pollution distribution map levels. The results indicate that the ensemble learning model has the highest accuracy of 71.2\% in this application. The new pollution distribution map using this classifier has 5,506 more pixels in the most severe pollution level than the traditional. Lastly, The remote sensing-based map and the manual measurement-based map were combin
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