Dynamic Data Fusion Approach for Air Quality Assessment

Conference paper
First Online:
Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

Data fusion procedures are developed to fill the gap between monitoring networks and CTMs. However, they often do not account for temporal dynamics, leading to potential inaccurate air quality assessment and forecasting. We propose a statistical data fusion strategy for combing the CTM output with monitoring data in order to improve air quality assessment and forecasting in the Emilia-Romagna region, Italy. We employ a dynamic linear model to accommodate dependence across time and obtain air pollution assessment and forecasting for the current and next two days. Finally, air pollution forecast maps are provided at high spatial resolution using universal kriging and exploiting the CTM output. We apply our strategy to particulate matter (PM10) concentrations during winter 2013.

Keywords

PM10 Concentration Dynamic Linear Model Data Assimilation Strategy Data Fusion Modeling White Noise Error Term 
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.
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Notes

Acknowledgments

This research was carried out as part of the “Supersito” Project, which was supported and financed by Emilia-Romagna Region and Regional Agency for Prevention and Environment. The first and third author were also supported by FIRB 2012 grant (project no. RBFR12URQJ) provided by the Italian Ministry of Education, Universities and Research.

References

  1. Borrego C, Monteiro A, Pay M, Ribeiro I, Miranda A, Basart S, Baldasano J (2011) How bias-correction can improve air quality forecasts over portugal. Atmos Environ 45:6629–6641CrossRefGoogle Scholar
  2. Cressie N (1993) Statistics for spatial data. Wiley, New YorkGoogle Scholar
  3. Durbin J, Koopman S (2001) Time series analysis by state space methods. Oxford University Press, OxfordGoogle Scholar
  4. Kang D, Mathur R, Rao ST, Yu S (2008) Bias adjustment techniques for improving ozone air quality forecasts. J Geophys Res 113(D23308). doi: 10.1029/2008JD010151
  5. Menut L, Bessagnet B, Khvorostyanov D, Beekmann M, Blond N, Colette A, Coll I, Curci G, Foret G et al (2013) CHIMERE 2013: a model for regional atmospheric composition modelling. Geosci Model Dev 6:981–1028CrossRefGoogle Scholar
  6. Stortini M, Deserti M, Bonaf`e G, Minguzzi E (2007) Long-term simulation and validation of ozone and aerosol in the Po Valley. In: Borrego C, Renner E (eds) Developments in environmental sciences, vol 6, pp 768–770. ElsevierGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.University of BolognaBolognaItaly
  2. 2.Regional Agency for Environmental Protection in the Emilia-RomagnaBolognaItaly

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