Abstract
In order to better understand the formation of air pollution and assess its influence on human beings, the acquisition of high resolution spatial-temporal air pollutant concentration map has always been an important research topic. Existing air-quality monitoring networks require potential improvement due to their limitations on data sources. In this paper, we take advantage of heterogeneous big data sources, including both direct measurements and various indirect data, to reconstruct a high resolution spatial-temporal air pollutant concentration map. Firstly, we predict a preliminary 3D high resolution air pollutant concentration map from measurements of both ground monitor stations and mobile stations equipped with sensors, as well as various meteorology and geography covariates. Our model is based on the Stochastic Partial Differential Equations (SPDE) approach and we use the Integrated Nested Laplace Approximation (INLA) algorithm as an alternative to the Markov Chain Monte Carlo (MCMC) methods to improve the computational efficiency. Next, in order to further improve the accuracy of the predicted concentration map, we model the issue as a convex and sparse optimization problem. In particular, we minimize the Total Variant along with constraints involving satellite observed low resolution air pollutant data and the aforementioned measurements from ground monitor stations and mobile platforms. We transform this optimization problem to a Second-Order Cone Program (SOCP) and solve it via the log-barrier method. Numerical simulations on real data show significant improvements of the reconstructed air pollutant concentration map.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
WHO Air Pollution Estimates; WHO (2014)
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Angela, H.B., McKinsey Global Institute: Big data: The next frontier for innovation, competition, and productivity (2011)
Zhu, Y., Hinds, W.C., Kim, S., Sioutas, C.: Concentration and size distribution of ultrafine particles near a major highway. Journal of the air & waste management association 52(9), 1032–1042 (2002)
Hasenfratz, D., Saukh, O., Walser, C., Hueglin, C., Fierz, M., Thiele, L.: Pushing the spatio-temporal resolution limit of urban air pollution maps. In: 2014 IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 69–77. IEEE, Budapest, March 2014
Hoek, G., Beelen, R., de Hoogh, K., Vienneau, D., Gulliver, J., Fischer, P., Briggs, D.: A review of land-use regression models to assess spatial variation of outdoor air pollution. Atmospheric Environment 42(33), 7561–7578 (2008)
Ryan, P.H., LeMasters, G.K.: A review of land-use regression models for characterizing intraurban air pollution exposure. Inhalation Toxicology 19(S1), 127–133 (2007)
Zheng, Y., Liu, F., Hsieh, H.P.: 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, pp. 1436–1444. ACM, Chicago, August 2013
Cameletti, M., Lindgren, F., Simpson, D., Rue, H.: Spatio-temporal modeling of particulate matter concentration through the SPDE approach. AStA Advances in Statistical Analysis 97(2), 109–131 (2013)
Cameletti, M., Ignaccolo, R., Bande, S.: Comparing spatio-temporal models for particulate matter in Piemonte. Environmetrics 22(8), 985–996 (2011)
Cressie, N.A., Cassie, N.A.: Statistics for spatial data, vol. 900. Wiley, New York (1993)
Cressie, N., Wikle, C.K.: Statistics for spatio-temporal data. John Wiley & Sons (2011)
Lindgren, F., Rue, H., Lindstrm, J.: An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 73(4), 423–498 (2011)
Rue, H., Martino, S., Chopin, N.: Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the royal statistical society: Series b (statistical methodology) 71(2), 319–392 (2009)
Rue, H., Held, L.: Gaussian Markov random fields: theory and applications. CRC Press (2005)
Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenomena 60(1), 259–268 (1992)
Limpert, E., Stahel, W.A., Abbt, M.: Log-normal Distributions across the Sciences: Keys and Clues. BioScience 51(5), 341–352 (2001)
Matérn covariance function - Wikipedia, the free encyclopedia. http://en.wikipedia.org/wiki/Mat%C3%A9rn_covariance_function
Goldfarb, D., Yin, W.: Second-order cone programming methods for total variation-based image restoration. SIAM Journal on Scientific Computing 27(2), 622–645 (2005)
Boyd, S., Vandenberghe, L.: Convex optimization. Cambridge University Press (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Li, Y., Zhu, Y., Yin, W., Liu, Y., Shi, G., Han, Z. (2015). Prediction of High Resolution Spatial-Temporal Air Pollutant Map from Big Data Sources. In: Wang, Y., Xiong, H., Argamon, S., Li, X., Li, J. (eds) Big Data Computing and Communications. BigCom 2015. Lecture Notes in Computer Science(), vol 9196. Springer, Cham. https://doi.org/10.1007/978-3-319-22047-5_22
Download citation
DOI: https://doi.org/10.1007/978-3-319-22047-5_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22046-8
Online ISBN: 978-3-319-22047-5
eBook Packages: Computer ScienceComputer Science (R0)