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Recently, air pollution is one of the most concerns for big cities.Predicting air quality for any regions and at any time is a criticalrequirement of urban citizens. However, air pollution prediction for the wholecity is a challenging problem. The reason is, there are many spatiotemporalfactors affecting air pollution throughout the city. Collecting as many of themcould help us to forecast air pollution better. In this research, we presentmany spatiotemporal datasets collected over Seoul city in Korea, which iscurrently much suffered by air pollution problem as well. These datasetsinclude air pollution data, meteorological data, traffic volume, averagedriving speed, and air pollution indexes of external areas which are known toimpact Seoul's air pollution. To the best of our knowledge, traffic volume andaverage driving speed data are two new datasets in air pollution research. Inaddition, recent research in air pollution has tried to build models tointerpolate and predict air pollution in the city. Nevertheless, they mostlyfocused on predicting air quality in discrete locations or used hand-craftedspatial and temporal features. In this paper, we propose the usage ofConvolutional Long Short-Term Memory (ConvLSTM) model \cite{b16}, a combinationof Convolutional Neural Networks and Long Short-Term Memory, whichautomatically manipulates both the spatial and temporal features of the data.Specially, we introduce how to transform the air pollution data into sequencesof images which leverages the using of ConvLSTM model to interpolate andpredict air quality for the entire city at the same time. We prove that ourapproach is suitable for spatiotemporal air pollution problems and alsooutperforms other related research.

Spatiotemporal deep learning model for citywide air pollution interpolation and prediction