Air pollution prediction based on factory-aware attentional LSTM neural network

With air quality issues becoming ever greater global concerns, many countries are facing numerous air pollution problems. Among all the particulate matter of air pollution, PM2.5 (whose aerodynamic diameter is 2.5 μm or less) is of particular concern. Long-term exposure to high concentrations of PM2.5 can negatively affect human health. The negative effects of PM2.5 make forecast its concentration an urgent need. In this paper, we propose a novel Factory-aware Attentional LSTM Model (FAA-LSTM) for PM2.5 air pollution predictions. The proposed model collects air pollution data from both the monitor stations and micro air quality sensors, in which a spatial transformation is designed to obtain the local area data of PM2.5 grids. Next, a novel factory-aware attention mechanism over a long short-term memory (LSTM) neural network is proposed to extract the hidden features of industrial factors and derive their factory attention weights on the influences of PM2.5 concentrations. The influence of neighboring factory data over local PM2.5 grids can be weighted to discover the importance of PM2.5 concentrations of neighboring areas. Moreover, the model combines these heterogeneous data and the global station data to forecast PM2.5 concentrations. The experiment’s evaluation is conducted using both air pollution data and industrial data. The results show that the factory-aware attention mechanism helps to improve the prediction performance by exploring the effect of the factory distribution on PM2.5 pollutants in the local areas. While monitoring stations have been established to collect air quality information and forecast air quality, few studies have taken the different monitoring areas and industrial features into account. The proposed novel model considering PM2.5 concentrations from local neighboring areas and global station, and industrial data as features can effectively indicate the impact of PM2.5 pollution with industrial emissions and spatial relationships. Our research work can improve the prediction accuracy of PM2.5 and contribute to increasing the practical value for air quality management.