# 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
##### 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. [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.

show all references

##### 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. [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.
 [1] Pankaj Kumar Tiwari, Rajesh Kumar Singh, Subhas Khajanchi, Yun Kang, Arvind Kumar Misra. A mathematical model to restore water quality in urban lakes using Phoslock. Discrete and Continuous Dynamical Systems - B, 2021, 26 (6) : 3143-3175. doi: 10.3934/dcdsb.2020223 [2] Lino J. Alvarez-Vázquez, Néstor García-Chan, Aurea Martínez, Miguel E. Vázquez-Méndez. Optimal control of urban air pollution related to traffic flow in road networks. Mathematical Control and Related Fields, 2018, 8 (1) : 177-193. doi: 10.3934/mcrf.2018008 [3] Yu-Ting Lin, John Malik, Hau-Tieng Wu. Wave-shape oscillatory model for nonstationary periodic time series analysis. Foundations of Data Science, 2021, 3 (2) : 99-131. doi: 10.3934/fods.2021009 [4] Hatim Tayeq, Amal Bergam, Anouar El Harrak, Kenza Khomsi. Self-adaptive algorithm based on a posteriori analysis of the error applied to air quality forecasting using the finite volume method. Discrete and Continuous Dynamical Systems - S, 2021, 14 (7) : 2557-2570. doi: 10.3934/dcdss.2020400 [5] Lianju Sun, Ziyou Gao, Yiju Wang. A Stackelberg game management model of the urban public transport. Journal of Industrial and Management Optimization, 2012, 8 (2) : 507-520. doi: 10.3934/jimo.2012.8.507 [6] Joseph R. Zipkin, Martin B. Short, Andrea L. Bertozzi. Cops on the dots in a mathematical model of urban crime and police response. Discrete and Continuous Dynamical Systems - B, 2014, 19 (5) : 1479-1506. doi: 10.3934/dcdsb.2014.19.1479 [7] Chuang Peng. Minimum degrees of polynomial models on time series. Conference Publications, 2005, 2005 (Special) : 720-729. doi: 10.3934/proc.2005.2005.720 [8] Boguslaw Twarog, Robert Pekala, Jacek Bartman, Zbigniew Gomolka. The changes of air gap in inductive engines as vibration indicator aided by mathematical model and artificial neural network. Conference Publications, 2007, 2007 (Special) : 1005-1012. doi: 10.3934/proc.2007.2007.1005 [9] Antonella Falini, Francesca Mazzia, Cristiano Tamborrino. Spline based Hermite quasi-interpolation for univariate time series. Discrete and Continuous Dynamical Systems - S, 2022  doi: 10.3934/dcdss.2022039 [10] Liu Liu, Weinian Zhang. Genetics of iterative roots for PM functions. Discrete and Continuous Dynamical Systems, 2021, 41 (5) : 2391-2409. doi: 10.3934/dcds.2020369 [11] Theodore Kolokolnikov, Michael J. Ward, Juncheng Wei. The stability of steady-state hot-spot patterns for a reaction-diffusion model of urban crime. Discrete and Continuous Dynamical Systems - B, 2014, 19 (5) : 1373-1410. doi: 10.3934/dcdsb.2014.19.1373 [12] Guangzhou Chen, Guijian Liu, Jiaquan Wang, Ruzhong Li. Identification of water quality model parameters using artificial bee colony algorithm. Numerical Algebra, Control and Optimization, 2012, 2 (1) : 157-165. doi: 10.3934/naco.2012.2.157 [13] Daniel Glasscock, Andreas Koutsogiannis, Florian Karl Richter. Multiplicative combinatorial properties of return time sets in minimal dynamical systems. Discrete and Continuous Dynamical Systems, 2019, 39 (10) : 5891-5921. doi: 10.3934/dcds.2019258 [14] Hanwool Na, Myeongmin Kang, Miyoun Jung, Myungjoo Kang. Nonconvex TGV regularization model for multiplicative noise removal with spatially varying parameters. Inverse Problems and Imaging, 2019, 13 (1) : 117-147. doi: 10.3934/ipi.2019007 [15] Jian Lu, Lixin Shen, Chen Xu, Yuesheng Xu. Multiplicative noise removal with a sparsity-aware optimization model. Inverse Problems and Imaging, 2017, 11 (6) : 949-974. doi: 10.3934/ipi.2017044 [16] Ahmad Deeb, A. Hamdouni, Dina Razafindralandy. Comparison between Borel-Padé summation and factorial series, as time integration methods. Discrete and Continuous Dynamical Systems - S, 2016, 9 (2) : 393-408. doi: 10.3934/dcdss.2016003 [17] Cheng Peng, Zhaohui Tang, Weihua Gui, Qing Chen, Jing He. A bidirectional weighted boundary distance algorithm for time series similarity computation based on optimized sliding window size. Journal of Industrial and Management Optimization, 2021, 17 (1) : 205-220. doi: 10.3934/jimo.2019107 [18] Zhi Liu, Tie Zhang. An improved ARMA(1, 1) type fuzzy time series applied in predicting disordering. Numerical Algebra, Control and Optimization, 2020, 10 (3) : 355-366. doi: 10.3934/naco.2020007 [19] Hassan Khodaiemehr, Dariush Kiani. High-rate space-time block codes from twisted Laurent series rings. Advances in Mathematics of Communications, 2015, 9 (3) : 255-275. doi: 10.3934/amc.2015.9.255 [20] Annalisa Pascarella, Alberto Sorrentino, Cristina Campi, Michele Piana. Particle filtering, beamforming and multiple signal classification for the analysis of magnetoencephalography time series: a comparison of algorithms. Inverse Problems and Imaging, 2010, 4 (1) : 169-190. doi: 10.3934/ipi.2010.4.169

Impact Factor: