Advertisement

Particulate Matter Concentration Estimation from Satellite Aerosol and Meteorological Parameters: Data-Driven Approaches

  • Thi Nhat Thanh NguyenEmail author
  • Viet Cuong Ta
  • Thanh Ha Le
  • Simone Mantovani
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 244)

Abstract

Estimation of Particulate Matter concentration (PM1, PM2.5 and PM10) from aerosol product derived from satellite images and meteorological parameters brings a great advantage in air pollution monitoring since observation range is no longer limited around ground stations and estimation accuracy will be increased significantly. In this article, we investigate the application of Multiple Linear Regression (MLR) and Support Vector Regression (SVR) to make empirical data models for PM1/2.5/10 estimation from satellite- and ground-based data. Experiments, which are carried out on data recorded in two year over Hanoi - Vietnam, not only indicate a case study of regional modeling but also present comparison of performance between a widely used technique (MLR) and an advanced method (SVR).

Keywords

Root Mean Square Error Multiple Linear Regression Support Vector Regression Meteorological Parameter Aerosol Optical Thickness 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Global Climate Observing System Essential Climate Variables, http://gosic.org/ios/MATRICESECVECV-matrix.htm
  2. 2.
    Balaguer, N.C.: Combining models and monitoring. A survey to elicit expert opinion on the spatial representativeness of ground based monitoring data. Fairmode activity for WG2-SG1 (2012)Google Scholar
  3. 3.
    Kaufman, Y.J., Tanre, D.: Algorithm for remote sensing of tropospheric aerosol from modis. In: MODIS ATBD (1997)Google Scholar
  4. 4.
    Remer, L.A., Tanré, D., Kaufman, Y.J.: Algorithmfor remote sensing of tropospheric aerosol from MODIS: Collection 5. In: MODIS ATBD (2004)Google Scholar
  5. 5.
    Nguyen, T., Mantovani, S., Bottoni, M.: Estimation of Aerosol and Air Quality Fields with PMMAPPER, An Optical Multispectral Data Processing Package. In: ISPRS TC VII Symposium 100 Years ISPRS-Advancing Remote Sensing Science, vol. XXXVIII(7A), pp. 257–261 (2010)Google Scholar
  6. 6.
    Campalani, P., Nguyen, T.N.T., Mantovani, S., Bottoni, M., Mazzini, G.: Validation of PM MAPPER aerosol optical thickness retrievals at 1x1 km2 of spatial resolution. In: The 19th International Conference on Proceeding of Software, Telecommunications and Computer Networks (SoftCOM), pp. 1–5 (2011)Google Scholar
  7. 7.
    Chu, D.A., Kaufman, Y.J., Zibordi, G., Chern, J.D., Mao, J., Li, C., Holben, B.N.: Global monitoring of air pollution over land from the Earth Observing System-Terra Moderate Resolution Imaging Spectroradiometer (MODIS). Journal of Geophysical Research Atmospheres 108(D21), 4661 (2003)CrossRefGoogle Scholar
  8. 8.
    Wang, J., Chirstopher, S.A.: Intercomparison between satellite-derived aerosol optical thickness and PM2.5 mass: Implication for air quality studies. Geophysical Research Letter 30(21), 2095 (2003)CrossRefGoogle Scholar
  9. 9.
    Engel-Cox, J.A., Holloman, C.H., Coutant, B.W., Hoff, R.M.: Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban scale air quality. Atmospheric Environment 38, 2495–2509 (2004)CrossRefGoogle Scholar
  10. 10.
    Kacenelenbogen, M., Leon, J.F., Chiapello, I., Tanre, D.: Characterization of aerosol pollution events in France using ground-based and POLDER-2 satellite data. Atmospheric Chemistry and Physics 6, 4843–4849 (2006)CrossRefGoogle Scholar
  11. 11.
    Pelletier, B., Santer, R., Vidot, J.: Retrieving of particulate matter from optical measurements: A semiparametric approach. Journal of Geophysical Research: Atmospheres 112(D6208) (2007)Google Scholar
  12. 12.
    Schaap, M., Apituley, A., Timmermans, R.M.A., Koelemeijer, R.B.A., Leeuw, G.D.: Exploring the relation between aerosol optical depth and PM2.5 at Cabauw, the Netherlands. Atmospheric Chemistry and Physics 9, 909–925 (2009)CrossRefGoogle Scholar
  13. 13.
    Gupta, P., Christopher, S.A., Wang, J., Gehrig, R., Lee, Y., Kumar, N.: Satellite remote sensing of particulate matter and air quality assessment over global cities. Atmospheric Environment 40, 5880–5892 (2006)CrossRefGoogle Scholar
  14. 14.
    Gupta, P., Christopher, S.A.: Seven year particulate matter air quality assessment from surface and satellite measurements. Atmospheric Chemistry and Physics 8, 3311–3324 (2008)CrossRefGoogle Scholar
  15. 15.
    Gupta, P., Christopher, S.A.: Particulate matter air quality assessment using integrated surface, satellite, and meteorological products: Multiple regression approach. Journal of Geophysical Research: Atmospheres 114(D14205) (2009)Google Scholar
  16. 16.
    Gupta, P., Christopher, S.A.: Particulate matter air quality assessment using integrated surface, satellite, and meteorological products: A neural network approach. Journal of Geophysical Research: Atmospheres 114(D20205) (2009)Google Scholar
  17. 17.
    Zha, Y., Gao, J., Jiang, J., Lu, H., Huang, J.: Monitoring of urban air pollution from MODIS aerosol data: effect of meteorological parameters. Tellus B 62(2), 109–116 (2010)CrossRefGoogle Scholar
  18. 18.
    Lee, H.J., Liu, Y., Coull, B.A., Schwartz, J., Koutrakis, P.: A novel calibration ap-proach of MODIS AOD data to predict PM2.5 concentrations. Atmospheric Chemistry and Physics 11, 7991–8002 (2011)CrossRefGoogle Scholar
  19. 19.
    Yap, X.Q., Hashim, M.: A robust calibration approach for PM10 prediction from MODIS aerosol optical depth. Atmospheric Chemistry and Physics 13, 3517–3526 (2013)CrossRefGoogle Scholar
  20. 20.
    Yahi, H., Santer, R., Weill, A., Crepon, M., Thiria, S.: Exploratory study for estimating atmospheric low level particle pollution based on vertical integrated optical measurements. Atmospheric Environment 45, 3891–3902 (2011)CrossRefGoogle Scholar
  21. 21.
    Hirtl, M., Mantovani, S., Krger, B.C., Triebnig, G., Flandorfer, C.: AQA-PM: Extension of the Air-Quality model for Austria with satellite based Particulate Matter estimates. In: European Geosciences Union, General Assembly 2013, Austria (2013)Google Scholar
  22. 22.
    Ichoku, C., Chu, D.A., Mattoo, S., Kaufiman, Y.J.: A spatio-temporal approach for global validation and analysis of MODIS aerosol products. Geophysical Research Letter 29(12), 1616 (2002)CrossRefGoogle Scholar
  23. 23.
    Vapnik, V.: The nature of statistical learning theory. Springer, Berlin (1995)CrossRefzbMATHGoogle Scholar
  24. 24.
    Chang, C., Lin, C.: LIBSVM: A Library for Support Vector Machines (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Thi Nhat Thanh Nguyen
    • 1
    Email author
  • Viet Cuong Ta
    • 1
  • Thanh Ha Le
    • 1
  • Simone Mantovani
    • 2
    • 3
  1. 1.University of Engineering and Technology, VNUHanoiVietnam
  2. 2.MEEO S.r.l.FerraraItaly
  3. 3.SISTEMA GmbHViennaAustria

Personalised recommendations