Spatial ensemble prediction of hourly PM2.5 concentrations around Beijing railway station in China

Abstract

Air pollution has a serious negative impact on human health and economic development. Railway stations have the maximum flow of people in a city. It is necessary to provide precise air pollution forecasting for railway station areas. In this study, a spatial ensemble model is proposed to predict hourly PM2.5 concentrations for the Beijing railway station. In the proposed model, the spatial analysis is realized by a spatial feature selection and a spatial ensemble. The spatial feature selection method can recognize the correlated monitoring sites around the Beijing railway station and rank them. An optimization method is utilized to optimize the weighting coefficients of different correlated sites. Base predictors of different correlated sites are integrated according to the weighting coefficients. Besides, a data decomposition method is also utilized to enhance the performance of the spatial model. In this study, data processing methods and spatial analysis methods are combined with each other to build the spatial ensemble model. Four quarters of PM2.5 concentration data are utilized to verify the effectiveness and stability of the proposed model. The proposed spatial ensemble model can outperform other comparison models.