Abstract
To study urban air quality, this paper proposes a novel categorical time series model, which is based on a linear combination of bounded Poisson distribution and discrete distribution to describe the dynamic and systemic features of air quality, respectively. Daily air quality level data of three major cities in China, including Beijing, Shanghai and Guangzhou, are analyzed. It is concluded that the air quality in Beijing is the worst among the three cities but is gradually improving, and its dynamics is also the most pronounced. Theoretically, the design of our model increases the flexibility of the probabilistic structure while ensuring a dynamic feedback mechanism without high computational stress. We estimate the parameters through an adaptive Bayesian Markov chain Monte Carlo sampling scheme and show the satisfactory finite sample performance of the model through simulation studies.
Highlights
Air quality has become a common concern, both for healthsensitive individuals and for the academics interested in it
To study urban air quality, this paper proposes a novel categorical time series model, which is based on a linear combination of bounded Poisson distribution and discrete distribution to describe the dynamic and systemic features of air quality, respectively
We focus on the air quality in recent years in three of China’s most developed first-tier cities, including Beijing, Shanghai and Guangzhou
Summary
Air quality has become a common concern, both for healthsensitive individuals and for the academics interested in it. Data on air quality levels of each city over time naturally form a categorical time series. This paper proposes an observation-driven model to study the data of air quality level, in which the observations are supposed to follow a novel distribution based on a linear combination of a bounded Poisson distribution and a discrete distribution. In China, suspended dust, coal combustion, industrial dust, vehicle emissions, biomass burning and secondary particulate matter contribute to urban pollution sources (World Bank 2012), where suspended dust, coal combustion, biomass burning and secondary particulate matter are seasonal factors, but there exists a considerable non-seasonal part of them due to almost fixed climate and topography and the high maturity of the industrial structure in each city This part is treated as systemic, while industrial dust, vehicle emissions and the non-systemic part of seasonal factors are considered dynamic. The daily air quality level data for three major cities in China, including Beijing, Shanghai and Guangzhou, are analyzed.
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