A Long-Term Prediction Model of Beijing Haze Episodes Using Time Series Analysis.

Xiaoping Yang,Zhongqiu Zhang,Zhongxia Zhang,Li Yu,Cui Xu,Liren Sun

doi:10.1155/2016/6459873

Abstract

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.

Highlights

Industry of developing countries is mainly centralized around big cities, accompanied by a large population, consumption, and pollution
The main component of haze is pm2.5, and the concentration of pollution is described with Air Quality Index (AQI, the concentration of pm2.5)
More and more papers have referred to the haze episodes and the consequences in Northern China [7,8,9,10,11]

Summary

Introduction

Industry of developing countries is mainly centralized around big cities, accompanied by a large population, consumption, and pollution. Together with Tianjin city and Hebei province, Northern China has become one of the most prosperous and polluted areas on Earth. More and more papers have referred to the haze episodes and the consequences in Northern China [7,8,9,10,11]. Researchers pointed out that, over the coming years, haze episodes would continue to burst frequently in Northern China [12]. This paper presents an AQI prediction model of Beijing based on time series analysis. Statistical methods are used to obtain the maximum likelihood estimation of the prediction model. Both short-term and longterm experiments are carried out to test the accuracy and robustness of our model.

Related Work

The Prediction Model of Beijing’s Daily AQI

Model Evaluations

Findings

Conclusion and Future Work