  Air pollution is one of the most concerns for urban areas. Many countries
have constructed monitoring stations to hourly collect pollution values.
Recently, there is a research in Daegu city, Korea for real-time air quality
monitoring via sensors installed on taxis running across the whole city. The
collected data is huge (1-second interval) and in both Spatial and Temporal
format. In this paper, based on this spatiotemporal Big data, we propose a
real-time air pollution prediction model based on Convolutional Neural Network
(CNN) algorithm for image-like Spatial distribution of air pollution. Regarding
to Temporal information in the data, we introduce a combination of a Long
Short-Term Memory (LSTM) unit for time series data and a Neural Network model
for other air pollution impact factors such as weather conditions to build a
hybrid prediction model. This model is simple in architecture but still brings
good prediction ability.
