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STUDY ON PREDICTION MODEL OF SPACE-TIME DISTRIBUTION OF AIR POLLUTANTS BASED ON ARTIFICIAL NEURAL NETWORK
- Source :
- Environmental Engineering and Management Journal. 18:1575-1590
- Publication Year :
- 2019
- Publisher :
- OAIMDD - EcoZone Publishing House, 2019.
-
Abstract
- Along with the development of industrialization and urbanization, the pollution of the atmosphere increases and the air quality decreases gradually, especially in the developed regions such as Beijing, Tianjin and Hebei in China. The frequent occurrence of haze leads to the decrease of visibility day by day, which causes great inconvenience to people s mind and body. Therefore, an in-depth study of regional weather quality impact factors can improve the air quality in the regions, which is of important significance to haze warning. This study mainly explores the main causes of haze formation and forecasts the haze, and puts forward some suggestions on haze control. This study proposes a space-time prediction model of air pollutants based on artificial neural network. The selected research regions are cities in Beijing-Tianjin-Hebei region. The data are real-time daily data of monitoring stations published by Beijing Environmental Protection Monitoring Center and China Meteorological Administration, and the air environment changes of 13 cities in Beijing-Tianjin-Hebei region in 2017 are studied. The impact factors include PM2.5, PM10, SO2, NO2, CO and O3. Through collecting and visualizing the data, evaluating with air quality index (AQI), this study designs, establishes, trains, and simulates the backpropagation neural network, adjusts the corresponding parameters, obtains the optimal model and the main research conclusion, as well as provides scientific reasonable suggestion.
- Subjects :
- Pollution
0209 industrial biotechnology
Environmental Engineering
Haze
Meteorology
media_common.quotation_subject
02 engineering and technology
Management, Monitoring, Policy and Law
Backpropagation
020901 industrial engineering & automation
Beijing
Urbanization
0202 electrical engineering, electronic engineering, information engineering
Environmental science
020201 artificial intelligence & image processing
Train
Visibility
Air quality index
media_common
Subjects
Details
- ISSN :
- 18433707 and 15829596
- Volume :
- 18
- Database :
- OpenAIRE
- Journal :
- Environmental Engineering and Management Journal
- Accession number :
- edsair.doi...........f32ac43f10b49f5a7ba4ade8a85eb6a9