Abstract
With most city dwellers in China subjected to air pollution, forecasting extreme air pollution spells is of paramount significance in both scheduling outdoor activities and ameliorating air pollution. In this paper, we integrate the autoregressive conditional duration model (ACD) with the recurrence interval analysis (RIA) and also extend the ACD model to a spatially autoregressive conditional duration (SACD) model by adding a spatially reviewed term to quantitatively explain and predict extreme air pollution recurrence intervals. Using the hourly data of six pollutants and the air quality index (AQI) during 2013–2016 collected from 12 national air quality monitoring stations in Beijing as our test samples, we attest that the spatially reviewed recurrence intervals have some general explanatory power over the recurrence intervals in the neighbouring air quality monitoring stations. We also conduct a one-step forecast using the RIA-ACD(1,1) and RIA-SACD(1,1,1) models and find that 90% of the predicted recurrence intervals are smaller than 72 hours, which justifies the predictive power of the proposed models. When applied to more time lags and neighbouring stations, the models are found to yield results that are consistent with reality, which evinces the feasibility of predicting extreme air pollution events through a recurrence-interval-analysis-based autoregressive conditional duration model. Moreover, the addition of a spatial term has proved effective in enhancing the predictive power.
Highlights
Regardless of the air quality monitoring stations’ commitment to present the latest air quality reports and exhaustive air quality information, Chinese residents are tending to suffer a longer stretch of stifling air pollution periods[1]
The spatial interaction and similarity between stations are taken into account and the spatial reviewed recurrence intervals are proposed to check whether the recurrence intervals of neighbouring stations are of high correlations
A simple simulation shows that when the original time series are of high correlations, the generated recurrence interval series are more likely to maintain high correlations, whereby we attempt to add the spatial reviewed term into the autoregressive conditional duration model to fully incorporate the spatial effects
Summary
Regardless of the air quality monitoring stations’ commitment to present the latest air quality reports and exhaustive air quality information, Chinese residents are tending to suffer a longer stretch of stifling air pollution periods[1]. Wang et al established an early-warning system using a hybrid forecasting model based on some data processing methods (such as support vector machine and fuzzy set theory)[2,3] Whereas their quest for optimal distributions to model the air pollution time series is partially similar to ours, differences do exist in the models to deal with the distributions. We integrate the recurrence interval analysis with the autoregressive conditional duration model to measure the recurrence statistics of extreme air pollution events, which accords with Herrera and Schipp’s methodology to predict the value at risk of stock market index[16]. Peaks over threshold (POT) method is employed to generate POT series and the autoregressive conditional duration (ACD) model is applied to analyze the generated series In this regard, our paper follows Herrera and Schipp’s framework and tries to apply the ACD model into the recurrence intervals analysis. There is evidence that the spatial ACD model is slightly better than the ACD model due to the use of spatial information in predicting extreme pollution events
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