本文著录格式：Xie, K., Meng, F., Zhang, D. (2023). Regional forecasting of PM2.5 concentrations: A novel model based on the empirical orthogonal function analysis and Nadaraya–Watson kernel regression estimator. Environmental Modelling & Software, 170, 105857.
摘要：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.
关键词：PM2.5 forecast; EOF analysis; Nadaraya–Watson kernel regression estimator; Regional EOF forecasting model
期刊介绍:
Environmental Modelling & Software创刊于1997年，月刊，由Elsevier出版商出版，期刊被SCIE收录，2022-2023最新影响因子4.9，WOS分区为一区。
Environmental Modelling & Software期刊致力于推进计算机跨学科应用领域的研究，涵盖了计算机跨学科应用领域的各个方面，促进计算机跨学科应用领域的更好协作和信息共享。该期刊定期发表与计算机跨学科应用研究领域相关的原创研究论文、简短交流和评论，以及行业研究的高水平学术成果、最新科研理论，为全球同行业研究人员架起沟通的桥梁。
投稿方式（网址）：https://www.sciencedirect.com/journal/environmental-modelling-and-software
作者简介:
解康，现为数学与信息科学学院专任教师。主要研究方向为非参数统计、区域预测模型建立与应用。相关成果发表于Earth and Space Science、Geoscience Letters、Environmental Modelling & Software等期刊。
（供稿/倪盈盈 审核/张伟锋）