A Long-Term Prediction Model of Beijing Haze Episodes Using Time Series Analysis.
The rapid industrial development has led to the intermittent outbreak of pm2.5 or haze in developing countries, which has brought about great environmental issues, especially in big cities such as Beijing and New Delhi. We investigated the factors and mechanisms of haze change and present a long-term prediction model of Beijing haze episodes using time series analysis. We construct a dynamic structural measurement model of daily haze increment and reduce the model to a vector autoregressive model. Typical case studies on 886 continuous days indicate that our model performs very well on next day's Air Quality Index (AQI) prediction, and in severely polluted cases (AQI ≥ 300) the accuracy rate of AQI prediction even reaches up to 87.8%. The experiment of one-week prediction shows that our model has excellent sensitivity when a sudden haze burst or dissipation happens, which results in good long-term stability on the accuracy of the next 3-7 days' AQI prediction.
School of Information, Renmin University of China, Beijing 100872, China.
Xiaoping
0000-0002-1613-843X
X
Yang
School of Information, Renmin University of China, Beijing 100872, China.
Zhongxia
0000-0002-0056-8750
Z
Zhang
School of Computer Science, Northeastern University, Shenyang 110819, China.
Zhongqiu
Z
Zhang
School of Information, Renmin University of China, Beijing 100872, China.
Liren
L
Sun
School of Information, Renmin University of China, Beijing 100872, China.
Cui
C
Xu
School of Information, Renmin University of China, Beijing 100872, China.
Li
0000-0001-8503-2535
L
Yu
D000397:Air Pollution / Q000032:analysis*; D000068476:Beijing; D004784:Environmental Monitoring / Q000379:methods*; D006801:Humans; D008137:Longitudinal Studies; D008962:Models, Theoretical; D017711:Nonlinear Dynamics; D052638:Particulate Matter; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models
D052638:Particulate Matter