Relevance analysis and short-term prediction of PM2.5 concentrations in Beijing based on multi-source data

Atmospheric Environment

Volumn 150, Issue , 2017, Pages 146-161

  Ni, X Y ^a   Huang, H ^a   Du, W P ^b  

^a TSINGHUA UNIVERSITY   (China)

^b Beijing Climate Center   (China)

Author keywords

Multi source data mining; PM2.5; Relevance analysis; Short term prediction

Indexed keywords

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;

EID: 85000642893     PISSN: 13522310     EISSN: 18732844     Source Type: Journal    
DOI: 10.1016/j.atmosenv.2016.11.054     Document Type: Article

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