DOI: 10.1016/j.atmosenv.2016.11.054
Scopus记录号: 2-s2.0-85000642893
论文题名: Relevance analysis and short-term prediction of PM2.5 concentrations in Beijing based on multi-source data
作者: Ni X ; Y ; , Huang H ; , Du W ; P
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2017
卷: 150 起始页码: 146
结束页码: 161
语种: 英语
英文关键词: Multi-source data mining
; PM2.5
; Relevance analysis
; Short-term prediction
Scopus关键词: Artificial intelligence
; Atmospheric humidity
; Backpropagation
; Complex networks
; Correlation methods
; Data flow analysis
; Data mining
; Forecasting
; Learning systems
; Meteorology
; Multivariant analysis
; Neural networks
; Nitrogen oxides
; Pollution
; Rain
; Sulfur dioxide
; Time series
; Wind
; Auto-regressive integrated moving average
; Back propagation neural networks
; Correlation analysis model
; Mathematical correlation
; Multisource data
; Multivariate statistical analysis
; Relevance analysis
; Short term prediction
; Big data
; carbon monoxide
; nitrogen dioxide
; rain
; sulfur dioxide
; artificial neural network
; atmospheric pollution
; climate prediction
; complexity
; concentration (composition)
; data mining
; multivariate analysis
; particulate matter
; real time
; social media
; wind velocity
; air monitoring
; air pollution
; Article
; back propagation
; China
; correlation analysis
; data mining
; environmental monitoring
; humidity
; meteorology
; particulate matter
; pollutant
; prediction
; priority journal
; social media
; statistical analysis
; temperature
; velocity
; wind
; Beijing [China]
; China
Scopus学科分类: Environmental Science: Water Science and Technology
; Earth and Planetary Sciences: Earth-Surface Processes
; Environmental Science: Environmental Chemistry
英文摘要: The PM2.5 problem is proving to be a major public crisis and is of great public-concern requiring an urgent response. Information about, and prediction of PM2.5 from the perspective of atmospheric dynamic theory is still limited due to the complexity of the formation and development of PM2.5. In this paper, we attempted to realize the relevance analysis and short-term prediction of PM2.5 concentrations in Beijing, China, using multi-source data mining. A correlation analysis model of PM2.5 to physical data (meteorological data, including regional average rainfall, daily mean temperature, average relative humidity, average wind speed, maximum wind speed, and other pollutant concentration data, including CO, NO2, SO2, PM10) and social media data (microblog data) was proposed, based on the Multivariate Statistical Analysis method. The study found that during these factors, the value of average wind speed, the concentrations of CO, NO2, PM10, and the daily number of microblog entries with key words ‘Beijing; Air pollution’ show high mathematical correlation with PM2.5 concentrations. The correlation analysis was further studied based on a big data's machine learning model- Back Propagation Neural Network (hereinafter referred to as BPNN) model. It was found that the BPNN method performs better in correlation mining. Finally, an Autoregressive Integrated Moving Average (hereinafter referred to as ARIMA) Time Series model was applied in this paper to explore the prediction of PM2.5 in the short-term time series. The predicted results were in good agreement with the observed data. This study is useful for helping realize real-time monitoring, analysis and pre-warning of PM2.5 and it also helps to broaden the application of big data and the multi-source data mining methods. © 2016 Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/82273
Appears in Collections: 气候变化事实与影响
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作者单位: Institute of Public Safety Research, Department of Engineering Physics, Tsinghua University, Beijing, China; Climate Center, Beijing Meteorological Service, Beijing, China
Recommended Citation:
Ni X,Y,, Huang H,et al. Relevance analysis and short-term prediction of PM2.5 concentrations in Beijing based on multi-source data[J]. Atmospheric Environment,2017-01-01,150