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National University of Defense Technology, China 109, Deya Road, Changsha, Hunan, China

Urban air pollution post a great threat to human health, and has been a major concern of many metropolises in developing countries. Lately, a few air quality monitoring stations have been established to inform public the real-time air quality indices based on fine particle matters, e.g. PM2.5, in countries suffering from air pollutions. Air quality, unfortunately, is fairly difficult to manage due to multiple complex human activities from driving to smelting. We observe that human activities' hidden regular pattern offers possibility in predication, and this motivates us to infer urban air condition from the perspective of time series. In this paper, we focus on PM2.5based urban air quality, and introduce two kinds of time-series methods for real-time and fine-grained air quality prediction, harnessing historical air quality data reported by existing monitoring stations. The methods are evaluated based in the real-life PM2.5concentration data in the year of 2013 (January-December) in Wuhan, China.
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Keywords Urban air quality; PM2.5; time series; multiplicative model; ARIMA

Citation: Ruiqi Li, Yifan Chen, Xiang Zhao, Yanli Hu, Weidong Xiao. TIME SERIES BASED URBAN AIR QUALITY PREDICATION. Big Data and Information Analytics, 2016, 1(2): 171-183. doi: 10.3934/bdia.2016003


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  • 1. Wenjun Zhang, Zhanpeng Guan, Jianyao Li, Zhu Su, Weibing Deng, Wei Li, Chinese cities’ air quality pattern and correlation, Journal of Statistical Mechanics: Theory and Experiment, 2020, 2020, 4, 043403, 10.1088/1742-5468/ab7813

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Copyright Info: 2016, Ruiqi Li, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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