  The spread of PM2.5 pollutants that endanger health is difficult to predict
because it involves many atmospheric variables. These micron particles can
spread rapidly from their source to residential areas, increasing the risk of
respiratory disease if exposed for long periods. The prediction system of PM2.5
propagation provides more detailed and accurate information as an early warning
system to reduce health impacts on the community. According to the idea of
transformative computing, the approach we propose in this paper allows
computation on the dataset obtained from massive-scale PM2.5 sensor nodes via
wireless sensor network. In the scheme, the deep learning model is implemented
on the server nodes to extract spatiotemporal features on these datasets. This
research was conducted by using dataset of air quality monitoring systems in
Taiwan. This study presents a new model based on the convolutional recursive
neural network to generate the prediction map. In general, the model is able to
provide accurate predictive results by considering the bonds among measurement
nodes in both spatially and temporally. Therefore, the particulate pollutant
propagation of PM2.5 could be precisely monitored by using the model we propose
in this paper.
