Skip to main content
Log in

Quality control and gap-filling of PM10 daily mean concentrations with the best linear unbiased estimator

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

According to the European Directive 2008/50/CE, the air quality assessment consists in the measurement of the concentration fields, and the evaluation of the mean, number of exceedances, etc. of some chemical species dangerous to human health. The measurements provided by an air quality ground-based monitoring network are the main information source but the availability of these data is often limited by several technical and operational problems. In this paper, the best linear unbiased estimator (BLUE) is proposed to validate the pollutant concentration values and to fill the gaps in the measurement of time series collected by a monitoring network. The BLUE algorithm is tested using the daily mean concentrations of particulate matter having aerodynamic diameter less than 10 μ (PM10 concentrations) measured by the air quality monitoring sensors operating in the Lazio Region in Italy. The comparison between the estimated and measured data evidences an error comparable with the measurement uncertainty. Due to its simplicity and reliability, the BLUE will be used in the routine quality test procedures of the Lazio air quality monitoring network measurements.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  • Baklanov, A., Rasmussen, A., Fay, B., Berge, E., & Finardi, S. (2002). Potential and shortcomings of numerical weather prediction models in providing meteorological data for urban air pollution forecasting. Water, Air and Soil Pollut., 2, 43–60. https://doi.org/10.1023/A:1021394126149.

    Article  Google Scholar 

  • Baklanov, A., Hänninen, O., Slørdal, L. H., Kukkonen, J., Bjergene, N., Fay, B., Finardi, S., Hoe, S. C., Jantunen, M., Karppinen, A., Rasmussen, A., Skouloudis, A., Sokhi, R. S., Sørensen, J. H., & Ødegaard, V. (2007). Integrated systems for forecasting urban meteorology, air pollution and population exposure. Atmospheric Chemistry and Physics, 7, 855–874. https://doi.org/10.5194/acp-7-855-2007.

    Article  CAS  Google Scholar 

  • Bennett, A. F. (2002). Inverse Modeling of the Ocean and atmosphere (pp. 234). New York: Cambridge University Press.

  • Brook, R. D., Rajagopalan, S., Pope, C. A. 3rd, Brook, J. R., Bhatnagar, A., Diez-Roux, A. V., et al. (2010). Particulate matter air pollution and cardiovascular disease: an update to the scientific statement from the American Heart Association. Circulation, 2331–2378. https://doi.org/10.1161/CIR.0b013e3181dbece1.

  • Directive 2008/50/EC of the European Parliament and of the Council of 21 May 2008 on ambient air quality and cleaner air for Europe. (2008). Official Journal of the European Union, 152/1-152/44.

  • Dutton, J. A. (1995). Dynamics of atmopheric motion (p. 644). Mineola: Dover Publications.

    Google Scholar 

  • Garratt, J. R. (1994). The atmospheric boundary layer (p. 316). Cambridge: Cambridge University Press.

    Google Scholar 

  • Goovaert, P. (1997). Geostatistics for natural resources evaluation (p. 457). Oxford: Oxford University Press.

    Google Scholar 

  • Jacobson, M. Z. (2005). Fundamentals of Atmospheric modeling (p. 784). Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Kaimal, J. C., & Finnigan, J. J. (1994). Atmospheric boundary layer flows: their structure and measurements. Oxford: Oxford University Press.

    Google Scholar 

  • Kalnay, E. (2003). Atmospheric modeling, data assimilation and predictability (p. 328). Cambridge: Cambridge University Press.

    Google Scholar 

  • Kitanidis, P. K. (1997). Introduction to geostatistics: applications to hydrology (p. 272). Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Kukkonen, J., Olsson, T., Schultz, D. M., Baklanov, A., Klein, T., Miranda, A. I., Monteiro, A., Hirtl, M., Tarvainen, V., Boy, M., Peuch, V. H., Poupkou, A., Kioutsioukis, I., Finardi, S., Sofiev, M., Sokhi, R., Lehtinen, K. E. J., Karatzas, K., San, J. R., Astitha, M., Kallos, G., Schaap, M., Reimer, E., Jakobs, H., & Eben, K. (2012a). Atmospheric Chemistry and Physics, 12, 1–87. https://doi.org/10.5194/acp-12-1-2012.

    Article  CAS  Google Scholar 

  • Kukkonen, J. T., et al. (2012b). A review of operational, regional-scale, chemical weather forecasting models in Europe. Atmospheric Chemistry and Physics, 12, 1–87. https://doi.org/10.5194/acp-12-1-2012.

    Article  CAS  Google Scholar 

  • Pielke, R. A. (1984). Mesoscale meteorological modeling (p. 612). New York: Academic Press.

    Google Scholar 

  • Seaton, A., Godden, D., MacNee, W., & Donaldson, K. (1995). Particulate air pollution and acute health effects. The Lancet, 345(8943), 176–178.

    Article  CAS  Google Scholar 

  • Seinfeld, J. H. & Pandis, S. N. (1998). Atmospheric chemistry and physics from air pollution to climate change. New York: John Wiley and Sons, Incorporated.

  • Silibello, C., Bolignano, A., Sozzi, R., & Gariazzo, C. (2014). Application of a chemical transport model and optimized data assimilation methods to improve air quality assessment. Air Quality, Atmos & Health, 7, 283–296. https://doi.org/10.1007/s11869-014-0235-1.

    Article  CAS  Google Scholar 

  • Silibello, C., D’ Allura, A., Finardi, S., Bolignano, A., & Sozzi, R. (2015). Application of bias adjustment techniques to improve air quality forecasts. Atmospheric Pollution Research, 6, 928–938. https://doi.org/10.1016/j.apr.2015.04.002.

    Article  Google Scholar 

  • Sorbjan, Z. (1989). Structure of the atmospheric boundary layer. New Jersy: Printice Hall.

    Google Scholar 

  • Sozzi, R., Georgiadis, T., & Valentini, M. (2002). Introduzione alla Turbolenza Atmosferica: concetti, stime, misure, Pitagora Editrice, 525 pp.

  • Stull, R. B. (1989). An introduction to boundary layer meteorology (p. 649). New Jersy: Kluwer Academic Publishers.

    Google Scholar 

  • Venkatram, A., & Wyngaard, J. C. (1988). Lectures on air pollution modeling (pp. 390). Chicago: The University of Chicago Press Books.

  • Wackernagel, H. (1998). Multivariate geostatistics. An introduction with applications (pp. 291). Springer-Verlag Berlin Heidelberg

  • World Heath Organization (2013). Review of evidence on health aspects of air pollution. REVIHAAP Project Technical Report. Copenhagen: WHO Regional Office for Europe.

Download references

Acknowledgements

Special thanks to Dr. W.A. Lahoz for his helpful comments and suggestions that helped greatly in improving this manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to S. Argentini.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sozzi, R., Bolignano, A., Ceradini, S. et al. Quality control and gap-filling of PM10 daily mean concentrations with the best linear unbiased estimator. Environ Monit Assess 189, 562 (2017). https://doi.org/10.1007/s10661-017-6273-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-017-6273-z

Keywords

Navigation