Published Paper Details:

The regulation of air pollutant levels is rapidly becoming one of the most important tasks for the governments of developing countries, especially China. Among the pollutant index, Fine particulate matter (PM2.5) is a significant one because it is a big concern to people's health when its level in the air is relatively high. PM2.5 refers to tiny particles in the air that reduce visibility and cause the air to appear hazy when levels are elevated. However, the relationships between the concentration of these particles and meteorological and traffic factors are poorly understood. To shed some light on these connections, some of these advanced techniques have been introduced into air quality research. These studies utilized selected techniques, such as Support Vector Machine (SVM) and Neural Network, to predict ambient air pollutant levels based on mostly weather and sometimes traffic variables. This project attempted to apply some machine learning techniques to predict PM2.5 levels based on a dataset consisting of daily weather and traffic parameters in Beijing, China. Due to the uncertainty of the specific number PM2.5 level, I simplified the problem to be a binary classification one, that is to classify the PM2.5 level into "High" (> 115 ug/m3) and "low" (<= 115 ug/m3). The value is chosen based on the Air Quality Level standard in China, which set 115 ug/m3 to be mild level pollution.