Prediction of PM2.5 Concentration in Beijing Based on Bayesian Hierarchical Autoregressive Spatio-Temporal Model
Year: 2023
Author: Jing Wang, Chunzheng Cao
Journal of Information and Computing Science, Vol. 18 (2023), Iss. 2 : pp. 117–128
Abstract
Here, a hierarchical autoregressive spatio-temporal model under the Bayesian framework is proposed to address the simultaneous multi-site PM2.5 prediction. The true daily average concentration of PM2.5 is regarded as a potential spatio-temporal process, then the temporal correlation is described by the first-order autoregressive process and the spatial correlation is captured based on the Matérn process, which greatly improves the efficiency in dimension reduction and synchronous prediction. In addition, meteorological factors such as daily maximum temperature, relative humidity and wind speed are used as explanatory variables to improve the prediction accuracy. The combination of Bayesian method and MCMC can realize parameter estimation and prediction process due to the model's hierarchical structure. The empirical analysis of daily PM2.5 concentration in Beijing shows that the proposed model has good interpolation or prediction performance in both spatial and temporal dimensions.
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/JICS-2023-008
Journal of Information and Computing Science, Vol. 18 (2023), Iss. 2 : pp. 117–128
Published online: 2023-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 12
Keywords: Bayesian method Hierarchical model Autoregressive Spatio-temporal model PM2.5 prediction Markov Chain Monte Carlo (MCMC).
Author Details
Jing Wang Email
Chunzheng Cao Email