Regional forecasting of PM2.5 concentrations: A novel model based on the empirical orthogonal function analysis and Nadaraya–Watson kernel regression estimator

Abstract

Predicting regional air pollutant concentrations quickly and accurately is still a challenge when meteorological data that affect pollutant concentrations are not available. To avoid modeling single points, which is tedious, it is necessary to explore more efficient and reliable regional methods with fewer input data. In this study, the principle and procedure of a new hybrid model, EOF-NWKRE, based on empirical orthogonal function (EOF) decomposition and the N–W kernel regression estimator (NWKRE) are proposed, then, the PM2.5 concentrations of 13 cities in Jiangsu Province is simulated by the EOF-NWKRE. The results show that the average prediction accuracy of EOF-NWKRE model is 74.38%, with more than 92% of the cumulative variance. The model is completely self-driven by PM2.5 data without covariate data input, which reduces the computational burden, improves the computational efficiency, and results in better prediction accuracy. It is suitable for the prediction of pollutant concentrations in a large area.