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The spread of PM2.5 pollutants that endanger health is difficult to predictbecause it involves many atmospheric variables. These micron particles canspread rapidly from their source to residential areas, increasing the risk ofrespiratory disease if exposed for long periods. The prediction system of PM2.5propagation provides more detailed and accurate information as an early warningsystem to reduce health impacts on the community. According to the idea oftransformative computing, the approach we propose in this paper allowscomputation on the dataset obtained from massive-scale PM2.5 sensor nodes viawireless sensor network. In the scheme, the deep learning model is implementedon the server nodes to extract spatiotemporal features on these datasets. Thisresearch was conducted by using dataset of air quality monitoring systems inTaiwan. This study presents a new model based on the convolutional recursiveneural network to generate the prediction map. In general, the model is able toprovide accurate predictive results by considering the bonds among measurementnodes in both spatially and temporally. Therefore, the particulate pollutantpropagation of PM2.5 could be precisely monitored by using the model we proposein this paper.

A Novel Prediction Approach for Exploring PM2.5 Spatiotemporal Propagation Based on Convolutional Recursive Neural Networks