Multi-Site and Multi-Pollutant Air Quality Data Modeling

Abstract

This study proposes a new method for predicting air quality in major industrialized cities around the world. In some big cities, multiple air quality measurement stations are deployed at different locations to monitor air pollutants, such as NO2, CO, PM 2.5, and PM 10, over time. At every monitoring timestamp t, we observe one station × feature matrix xt of the pollutant data, which represents a spatio-temporal process. Traditional methods of prediction of air quality typically use data from one station or can only predict a single pollutant (such as PM 2.5) at a time, which ignores the spatial correlation among different stations. Moreover, the air pollution data are typically highly non-stationary. This study has explicitly overcome the limitations of these two aspects, forming its unique contributions. Specifically, we propose a de-trending graph convolutional LSTM (long short-term memory) to continuously predict the whole station × feature matrix in the next 1 to 48 h, which not only captures the spatial dependency among multiple stations by replacing an inner product with convolution, but also incorporates the de-trending signals (transforms a non-stationary process to a stationary one by differencing the data) into our model. Experiments on the air quality data of the city of Chengdu and multiple major cities in China demonstrate the feasibility of our method and show promising results.