Sequential prediction of quantitative health risk assessment for the fine particulate matter in an underground facility using deep recurrent neural networks

Abstract

Particulate matter with aerodynamic diameter less than 2.5 µm (PM2.5) in indoor public spaces such as subway stations, has represented a major public health concern; however, forecasting future sequences of quantitative health risk is an effective method for protecting commuters’ health, and an important tool for developing early warning systems. Despite the existence of several predicting methods, some tend to fail to forecast long-term dependencies in an effective way. This paper aims to implement a multiple sequences prediction of a comprehensive indoor air quality index (CIAI) traced by indoor PM2.5, utilizing different structures of recurrent neural networks (RNN). A standard RNN (SRNN), long short-term memory (LSTM) and a gated recurrent unit (GRU) structures were implemented due to their capability of managing sequential, and time-dependent data. Hourly indoor PM2.5 concentration data collected in the D-subway station, South Korea, were utilized for the validation of the proposed method. For the selection of the most suitable predictive model (i.e. SRNN, LSTM, GRU), a point-by-point prediction on the PM2.5 was conducted, demonstrating that the GRU structure outperforms the other RNN structures (RMSE = 21.04 µg/m3, MAPE = 32.92%, R2 = 0.65). Then, this model is utilized to sequentially predict the concentration and quantify the health risk (i.e. CIAI) at different time lags. For a 6-h time lag, the proposed model exhibited the best performance metric (RMSE = 29.73 µg/m3, MAPE = 29.52%). Additionally, for the rest of the time lags including 12, 18 and 24 h, achieved an acceptable performance (MAPE = 29–37%).