Deep learning spatiotemporal air pollution data in China using data fusion

An efficient and effective spatiotemporal prediction algorithm for PM2.5 (i.e. particulate matter with a diameter of less than 2.5 micrometers) is urgently needed to study the distribution of PM2.5 over a continuous spatiotemporal domain, which not only helps to make scientific decisions on the prevention and control of PM2.5 pollution but also promotes meaningful assessment of the quantitative relationship between adverse health effects and PM2.5 concentrations over time. Existing spatiotemporal interpolation algorithms are usually based on the assumption that interpolation models follow explicit and simple mathematical descriptions. Unfortunately, the real world does not really follow these perfect mathematical models. Combining data fusion techniques and a Long Short-Term Memory (LSTM) recurrent neural network (RNN), we present a novel spatiotemporal interpolation model, which is able to achieve high estimation accuracies over a long time period and a large area. By fusing the daily PM2.5 data, meteorological data, elevation data, and land-use data collected from China in 2016, four experiments were conducted in this study to evaluate the efficiency and effectiveness of the proposed approach. Results showed that applying LSTM RNN on the fused dataset can achieve consistent and high accuracy in different geographies.