  Tackling air pollution is an imperative problem in South Korea, especially in
urban areas, over the last few years. More specially, South Korea has joined
the ranks of the world's most polluted countries alongside with other Asian
capitals, such as Beijing or Delhi. Much research is being conducted in
environmental science to evaluate the dangerous impact of particulate matters
on public health. Besides that, deterministic models of air pollutant behavior
are also generated; however, this is both complex and often inaccurate. On the
contrary, deep recurrent neural network reveals potent potential on forecasting
out-comes of time-series data and has become more prevalent. This paper uses
Recurrent Neural Network (RNN) with Long Short-Term Memory units as a framework
for leveraging knowledge from time-series data of air pollution and
meteorological information in Daegu, Seoul, Beijing, and Shenyang.
Additionally, we use encoder-decoder model, which is similar to machine
comprehension problems, as a crucial part of our prediction machine. Finally,
we investigate the prediction accuracy of various configurations. Our
experiments prevent the efficiency of integrating multiple layers of RNN on
prediction model when forecasting far timesteps ahead. This research is a
significant motivation for not only continuing researching on urban air quality
but also help the government leverage that insight to enact beneficial policies
