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     Advance Journal of Food Science and Technology

Research Article   |   OPEN ACCESS

Prediction of PM2.5 Concentration in a Agricultural Park Based on BP Artificial Neural Network

1Bo Chen, Xiaoping Wang, 1Lixuan Yu, 1Huan Wang, 2Yanfang Li, 2Junqi Chen, 2Jiangang Zhu, 3Hailong Nan and 3Limin Hou
1College of Agricultural Parkry, Beijing Agricultural Parkry University, Beijing 100083, China
2College of Soil and Water Conservation, Beijing Agricultural Parkry Carbon Administration, Beijing 100013, China
3College of Landscape Architecture, Daxing Bureau of Landscape and Agricultural Parkry, Beijing 102600, China
Advance Journal of Food Science and Technology  2016  4:274-280
http://dx.doi.org/10.19026/ajfst.11.2410  |  © The Author(s) 2016
Received: April ‎19, ‎2015  |  Accepted: May ‎10, ‎2015  |  Published: June 05, 2016
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Abstract

The accurate prediction of PM2.5 concentration in a agricultural park is important to understand the role agricultural park plays in regulating PM2.5 pollution and guide public close to the nature healthily. An artificial neural network model was established, with meteorological data, atmospheric PM2.5 concentration outside the agricultural park and agricultural park structure as the input factors and PM2.5 hourly average concentration inside the agricultural park as the output factors. Its prediction accuracy was also evaluated in this study. The results show that it can be concluded that BP artificial neural network model is a promising approach in predicting PM2.5 concentration inside a agricultural park.

Keywords:

Agricultural park structure, BP artificial neural network, multiple linear regression, PM2.5,

References