Air pollution is a growing problem in many countries. Many countries, especially in fast growing countries in Asia, have increasing air pollution problems due to the increasing urbanization and modernization of their societies. South Korea, for example, has one of the largest GDP in Eastern Asia and has have issues due to airquality in the region due to various factors. The Seoul Metroplitical Government in South Korea has several measurement stations to collect pollution data to help with their management of air quality. Using data collected over 24 hours for 3 years, we have granularity into the pollution patterns in Seoul. We are interested in building a forecasting model which might be able to predict pollution levels of various chemicals which could potentally be used to help with air pollution policies. We use a vector autoregression(VAR) model. VAR is a unique time series based model used to capture the linear interdependencies between predictors among multiple time series. As the assumption is that the pollutants and their relative level are correlated to each other, we use this model to better forecast future values of pollutants given pollutant levels of the other predictors.