# American Institute of Mathematical Sciences

July  2016, 1(2&3): 171-183. doi: 10.3934/bdia.2016003

## Time series based urban air quality predication

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

Received  May 2016 Revised  June 2016 Published  September 2016

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. $PM_{2.5}$, 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 $PM_{2.5}$ based 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 $PM_{2.5}$ concentration data in the year of 2013 (January - December) in Wuhan, China.
Citation: Ruiqi Li, Yifan Chen, Xiang Zhao, Yanli Hu, Weidong Xiao. Time series based urban air quality predication. Big Data & Information Analytics, 2016, 1 (2&3) : 171-183. doi: 10.3934/bdia.2016003
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##### References:
 [1] L. Bin-lian, G. Feng and J. Jian-hua, Analysis of pm2.5 current situation and the prevention control measures, energy and energy conservation, 54-54. [2] D. Hasenfratz, O. Saukh, S. Sturzenegger, and L. Thiele, Participatory air pollution monitoring using smartphones, In the 2nd International Workshop on Mobile Sensing. [3] Y. Jiang, K. Li, L. Tian, R. Piedrahita, X. Yun, O. Mansata, Q. Lv, R. P. Dick, M. Hannigan and L. Shang, Maqs: a personalized mobile sensing system for indoor air quality monitoring, in Proceedings of the 13th international conference on Ubiquitous computing, 2011, 271-280. doi: 10.1145/2030112.2030150. [4] L. N. Lamsal, R. V. Martin, A. V. Donkelaar, M. Steinbacher, E. A. Celarier, E. Bucsela, E. J. Dunlea and J. P. Pinto, Ground-level nitrogen dioxide concentrations inferred from the satellite-borne ozone monitoring instrument, Journal of Geophysical Research, 113 (2008), 280-288. doi: 10.1029/2007JD009235. [5] R. V. Martin, L. Lamsal and A. Van Donkelaar, Satellite remote sensing of surface air quality, Atmospheric Environment, 42 (2008), 7823-7843. doi: 10.1016/j.atmosenv.2008.07.018. [6] S. Vardoulakis, B. E. Fisher, K. Pericleous and N. Gonzalez-Flesca, Modelling air quality in street canyons: A review, Atmospheric environment, 37 (2003), 155-182. doi: 10.1016/S1352-2310(02)00857-9. [7] J. Yuan, Y. Zheng and X. Xie, Discovering regions of different functions in a city using human mobility and pois, in Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, 2012, 186-194. doi: 10.1145/2339530.2339561. [8] F. Zhang, D. Wilkie, Y. Zheng, and X. Xie., Sensing the pulse of urban refueling behavior, Proceedings of Acm International Conference on Ubiquitous Computing Ubicomp 11 Acm. [9] Y. Zhang and L. Y. Yang, On the applications of the additive model and multiplicative model of time series analysis, Statistics and Information Tribune. [10] Y. Zheng, F. Liu and H.-P. Hsieh, U-air: When urban air quality inference meets big data, in Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, 2013, 1436-1444. doi: 10.1145/2487575.2488188. [11] Y. Zheng, Y. Liu, J. Yuan and X. Xie, Urban computing with taxicabs, in Proceedings of the 13th international conference on Ubiquitous computing, ACM, 2011, 89-98. doi: 10.1145/2030112.2030126.

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