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Journal of Sensors
Volume 2016, Article ID 7901245, 15 pages
http://dx.doi.org/10.1155/2016/7901245
Research Article

A Mobile Sensing System for Urban Monitoring with Adaptive Resolution

The Department of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China

Received 30 December 2015; Revised 23 March 2016; Accepted 30 March 2016

Academic Editor: Yasuko Y. Maruo

Copyright © 2016 Hongjie Guo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. LePeule, F. Laden, D. Dockery, and J. Schwartz, “Chronic exposure to fine particles and mortality: an extended follow-up of the Harvard six cities study from 1974 to 2009,” Environmental Health Perspectives, vol. 120, no. 7, pp. 965–970, 2012. View at Publisher · View at Google Scholar · View at Scopus
  2. Hangzhou, http://dwz.cn/ft4lx.
  3. Y. Cheng, X. Li, Z. Li et al., “Aircloud: a cloud-based air-quality monitoring system for everyone,” in Proceedings of the 12th ACM Conference on Embedded Networked Sensor Systems, Memphis, Tenn, USA, November 2014.
  4. 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, Chicago, Ill, USA, August 2013.
  5. D. Hasenfratz, O. Saukh, C. Walser et al., “Deriving high-resolution urban air pollution maps using mobile sensor nodes,” Pervasive and Mobile Computing, vol. 16, pp. 268–285, 2015. View at Publisher · View at Google Scholar · View at Scopus
  6. Y. Hu, J. Fan, H. Zhang, X. Chen, and G. Dai, “An estimated method of urban PM2.5 concentration distribution for a mobile sensing system,” Pervasive and Mobile Computing, pp. 88–103, 2015. View at Publisher · View at Google Scholar · View at Scopus
  7. A. Tomlin, M. Berzins, J. Ware, J. Smith, and M. J. Pilling, “On the use of adaptive gridding methods for modelling chemical transport from multi-scale sources,” Atmospheric Environment, vol. 31, no. 18, pp. 2945–2959, 1997. View at Publisher · View at Google Scholar · View at Scopus
  8. I. Lagzi, D. Kármán, T. Turányi, A. S. Tomlin, and L. Haszpra, “Simulation of the dispersion of nuclear contamination using an adaptive Eulerian grid model,” Journal of Environmental Radioactivity, vol. 75, no. 1, pp. 59–82, 2004. View at Publisher · View at Google Scholar · View at Scopus
  9. I. Lagzi, T. Turányi, A. S. Tomlin, and L. Haszpra, “Modelling photochemical air pollutant formation in Hungary using an adaptive grid technique,” International Journal of Environment and Pollution, vol. 36, no. 1–3, pp. 44–58, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. A. S. Tomlin, S. Ghorai, G. Hart, and M. Berzins, “3-D multi-scale air pollution modelling using adaptive unstructured meshes,” Environmental Modelling and Software, vol. 15, no. 6-7, pp. 681–692, 2000. View at Publisher · View at Google Scholar · View at Scopus
  11. S. Ghorai, A. S. Tomlin, and M. Berzins, “Resolution of pollutant concentrations in the boundary layer using a fully 3D adaptive gridding technique,” Atmospheric Environment, vol. 34, no. 18, pp. 2851–2863, 2000. View at Publisher · View at Google Scholar · View at Scopus
  12. R. K. Srivastava, D. S. McRae, and M. T. Odman, “An adaptive grid algorithm for air-quality modeling,” Journal of Computational Physics, vol. 165, no. 2, pp. 437–472, 2000. View at Publisher · View at Google Scholar · View at Scopus
  13. F. Garcia-Menendez, A. Yano, Y. Hu, and M. Talat Odman, “An adaptive grid version of CMAQ for improving the resolution of plumes,” Atmospheric Pollution Research, vol. 1, no. 4, pp. 239–249, 2010. View at Publisher · View at Google Scholar · View at Scopus
  14. United States Environmental Protection Agency (USEPA), https://www.cmascenter.org/cmaq.
  15. E. M. Constantinescu, A. Sandu, and G. R. Carmichael, “Modeling atmospheric chemistry and transport with dynamic adaptive resolution,” Computational Geosciences, vol. 12, no. 2, pp. 133–151, 2008. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  16. G. R. Carmichael, L. K. Peters, and R. D. Saylor, “The STEM-II regional scale acid deposition and photochemical oxidant model—I. An overview of model development and applications,” Atmospheric Environment Part A: General Topics, vol. 25, no. 10, pp. 2077–2090, 1991. View at Publisher · View at Google Scholar · View at Scopus
  17. Ministry of Environmental Protection of the People's Republic of China, “Technical regulation on ambient air quality index,” HJ 633-2012, 2012. View at Google Scholar
  18. K. Pearson, “The problem of random walk,” Nature, vol. 72, p. 294, 1905. View at Google Scholar
  19. O. Raaschou-Nielsen, Z. J. Andersen, B. Beelen et al., “Air pollution and lung cancer incidence in 17 European cohorts: prospective analyses from the European Study of Cohorts for Air Pollution Effects (ESCAPE),” The Lancet Oncology, vol. 14, no. 9, pp. 799–805, 2013. View at Google Scholar
  20. C. A. Pope III, R. T. Burnett, M. J. Thun et al., “Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution,” The Journal of the American Medical Association, vol. 287, no. 9, pp. 1132–1141, 2002. View at Publisher · View at Google Scholar · View at Scopus
  21. R. M. Harrison, D. J. T. Smith, and A. J. Kibble, “What is responsible for the carcinogenicity of PM 2.5?” Occupational and Environmental Medicine, vol. 61, no. 10, pp. 799–805, 2004. View at Publisher · View at Google Scholar · View at Scopus
  22. S. Vardoulakis, B. E. A. Fisher, K. Pericleous, and N. Gonzalez-Flesca, “Modelling air quality in street canyons: a review,” Atmospheric Environment, vol. 37, no. 2, pp. 155–182, 2003. View at Publisher · View at Google Scholar · View at Scopus
  23. M. W. Gardner and S. R. Dorling, “Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences,” Atmospheric Environment, vol. 32, no. 14-15, pp. 2627–2636, 1998. View at Publisher · View at Google Scholar · View at Scopus
  24. Sharp, Device Specification for PM2:5 Sensor Module, Electronic Components and Devices Division, Sharp Corporation, 2014.
  25. Lighthouse, http://www.golighthouse.nl/en/indoor-air-quality.
  26. X. Xu, P. Zhang, and L. Zhang, “Gotcha: a mobile urban sensing system,” in Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems (SenSys '14), pp. 316–317, Memphis, Tenn, USA, November 2014. View at Publisher · View at Google Scholar
  27. K. Schäfer, “Introduction to the theory of error, von Yardley Beers. Addison-Wesley Publish. Comp. INC., Cambridge 42 Mass. 1953. 1. Aufl. VI, 65 S., brosch. $ 1.25,” Angewandte Chemie, vol. 67, no. 16, pp. 432–432, 1955. View at Publisher · View at Google Scholar
  28. P. Vachhani, R. Rengaswamy, and V. Venkatasubramanian, “A framework for integrating diagnostic knowledge with nonlinear optimization for data reconciliation and parameter estimation in dynamic systems,” Chemical Engineering Science, vol. 56, no. 6, pp. 2133–2148, 2001. View at Publisher · View at Google Scholar · View at Scopus
  29. D. M. Prata, M. Schwaab, E. L. Lima, and J. C. Pinto, “Simultaneous robust data reconciliation and gross error detection through particle swarm optimization for an industrial polypropylene reactor,” Chemical Engineering Science, vol. 65, no. 17, pp. 4943–4954, 2010. View at Publisher · View at Google Scholar · View at Scopus
  30. I. Markovsky and S. Van Huffel, “Overview of total least-squares methods,” Signal Processing, vol. 87, no. 10, pp. 2283–2302, 2007. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  31. Wind Power Sensor, http://www.smartsensor.cn.
  32. China Weather Reuters, http://www.weather.com.cn.