Influencing factors and prediction of ambient Peroxyacetyl nitrate concentration in Beijing,China

作     者：Boya Zhang Bu Zhao Peng Zuo Zhi Huang Jianbo Zhang 

作者机构：State Key Joint Laboratory of Environmental Simulation and Pollution Control College of Environmental Sciences and Engineering Peking University School of Environment Tsinghua University 

出 版 物：《Journal of Environmental Sciences》 (环境科学学报（英文版）)

年 卷 期：2019年第31卷第3期

页      面：189-197页

核心收录：

学科分类：07[理学] 070602[理学-大气物理学与大气环境] 0706[理学-大气科学] 

基　　金：supported by the "State Key R&D Program" of China.(Nos.2017YFC0212400 2016YFC0202200) 

主　　题：Artificial neural network Conventional atmospheric pollutants Meteorological parameters Concentration prediction Multiple linear regression 

摘      要：Peroxyacyl nitrates(PANs) are important secondary pollutants in ground-level *** prediction of atmospheric pollutant concentrations is crucial to guide effective precautions for before and during specific pollution events. In this study, four models based on the back-propagation(BP) artificial neural network(ANN) and multiple linear regression(MLR) methods were used to predict the hourly average PAN concentrations at Peking University, Beijing, in 2014. The model inputs were atmospheric pollutant data and meteorological parameters. Model 3 using a BP-ANN based on the original variables achieved the best prediction results among the four models, with a correlation coefficient(R) of 0.7089, mean bias error of -0.0043 ppb, mean absolute error of 0.4836?ppb, root mean squared error of 0.5320?ppb, and Willmott s index of agreement of 0.8214. Based on a comparison of the performance indices of the MLR and BP-ANN models, we concluded that the BP-ANN model was able to capture the highly non-linear relationships between PAN concentration and the conventional atmospheric pollutant and meteorological parameters,providing more accurate results than the traditional MLR models did, with a markedly higher goodness of R. The selected meteorological and atmospheric pollutant parameters described a sufficient amount of PAN variation, and thus provided satisfactory prediction results. More specifically, the BP-ANN model performed very well for capturing the variation pattern when PAN concentrations were low. The findings of this study address some of the existing knowledge gaps in this research field and provide a theoretical basis for future regional air pollution control.