Analysis and forecasting of the particulate matter (PM) concentration levels over four major cities of China using hybrid models
Analysis and forecasting of the particulate matter (PM) concentration levels over four major cities of China using hybrid models
复制标题
利用混合模型对中国四个主要城市的颗粒物（PM）浓度水平进行分析和预测
DOI：
10.1016/j.atmosenv.2014.09.046
复制
发表时间：
2014-12-01
影响因子：
5
通讯作者：
Sun, Beibei
中科院分区：
文献类型：
--
作者：
Qin, Shanshan;Liu, Feng;Sun, Beibei
颗粒物浓度的分析和预测对于制定颗粒物减排控制规划和预防措施具有重要意义。然而，建立预警系统所需的精确PM预测仍然是一个巨大的挑战和关键问题。确定如何解决准确的预测问题成为一个更加重要和紧迫的任务。基于灰色关联分析（GCA）、包围经验模态分解（EEMD）、布谷鸟搜索（CS）和BP人工神经网络（BPANN），提出了颗粒物浓度预测的CS-EEMD-BPANN模型。在建立该模型之前，灰色关联已被唯一地用于寻找可能的预测PM的其他空气污染物（CO，NO2，O-3和SO2）和气象环境（风速，风向，温度，湿度和压力）。该方法在中国四个主要城市（北京，上海，广州和兰州）的气候，地形和排放源的不同特点进行了调查。灰色关联分析的结果表明，CO、NO2和SO2与PM的关联度更高，这些预测因子的加入可以显著提高模型的预测性能，表明了该方法的有效性。(C)2014爱思唯尔有限公司版权所有。
The analysis and forecasting of PM concentrations play a significant role in regulatory planning on the reduction and control of PM emission and precautionary strategies. However, accurate PM forecasting, which is needed to establish an early warning system, is still a huge challenge and a critical issue. Determining how to address the accurate forecasting problem becomes an even more significant and urgent task. Based on gray correlation analysis (GCA), Ensemble Empirical Mode Decomposition (EEMD), Cuckoo search (CS) and Back-propagation artificial neutral networks (BPANN), this paper proposes the CS-EEMD-BPANN model for forecasting PM concentrations. Prior to establishing this model, gray correlation has been uniquely used to search for poSsible predictors of PM among other air pollutants (CO, NO2, O-3 and SO2) and meteorological environments (wind speed, wind direction, temperature, humidity and pressure). The proposed method was investigated in four major cities of China (Beijing, Shanghai, Guangzhou and Lanzhou) with different characteristics of climatic, terrain and emission sources. The results of the gray correlation analysis indicate that CO, NO2 and SO2 are more related to PM and that the incorporation of these predictors can significantly improve the model performance predictability, suggesting the effectiveness of our developed method. (C) 2014 Elsevier Ltd. All rights reserved.