Skip to main content

Application of a chemical transport model and optimized data assimilation methods to improve air quality assessment


The combined use of air quality monitoring data and state-of-the art dispersion models provides a more realistic representation of the spatial distribution of pollutants and allows a reduction in the uncertainties involved in the assessment of the exposure in epidemiological studies. Data assimilation is a method which combines such information to produce an optimal representation of the state of the atmosphere. In this work, we tested two approaches to merge these information sets: the successive corrections method (SCM) and the statistical optimal interpolation (OI). These methods have been extended in order to take into account the spatial representativeness of measurements. PM10, NO2, and O3 concentration fields produced by an air quality modeling system, run with two nested domains covering much of Central Italy and the Rome urban area, have been used to identify the optimal values for the horizontal and vertical scaling distances that are key parameters for the SCM and OI methods. A statistical analysis of the results obtained from the application of these methods demonstrated that lower RMSE values resulted from the use of the OI method. Further, PM2.5 modeling results over the Rome urban area and additional measurements collected during experimental campaigns, performed within the population exposure to polycyclic aromatic hydrocarbons (EXPAH) LIFE+ Project, allowed the evaluation of this approach in reconstructing PM2.5 levels at EXPAH monitoring sites, which were not used in the data assimilation process. The results confirmed the potential of these methods to improve the estimation of modeled concentrations, by taking into account local phenomena not resolved by the model, but clear from the observations, and also in providing more reliable data to be used in exposure studies.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6


  1. Adhikary B, Kulkarni S, D’Allura A, Tang Y, Chai T, Leung LR, Qian Y, Chung CE, Ramanathan V, Carmichael GR (2008) A regional scale chemical transport modeling of Asian aerosols with data assimilation of AOD observations using optimal interpolation technique. Atmos Environ 42:8600–8615

    CAS  Article  Google Scholar 

  2. Binkowsk FS (1999) The aerosol portion of Models-3 CMAQ. In science algorithms of the EPA Models-3 Community Multiscale Air Quality (CMAQ) Modeling System, edited by D.W. Byun, and J.K.S. Ching, EPA-600/R-99/030, 1–23

  3. Binkowski FS, Roselle SJ (2003) Models-3 community multiscale air quality (CMAQ) model aerosol component 1. Model description. J Geophys Res 108(D6):4183

    Article  Google Scholar 

  4. Borrego C, Monteiro A, Payb MT, Ribeiro I, Miranda AI, Basart S, Baldasano JM (2011) How bias-correction can improve air quality forecasts over Portugal. Atmos Environ 45:6629–6641

    CAS  Article  Google Scholar 

  5. Bratseth AM (1986) Statistical interpolation by means of successive corrections. Tellus 38A:439–447

    Article  Google Scholar 

  6. Carter WPL (1990) A detailed mechanism for the gas-phase atmospheric reactions of organic compounds. Atmos Environ 24A:481–518

    CAS  Article  Google Scholar 

  7. Caserini S, Fraccaroli A, Monguzzi AM, Moretti M, Angelino E (2007) New insight into the role of wood combustion as key PM source in Italy and in Lombardy region. Proc. of 16th Annual International Emissions Inventory Conference “Emission Inventories: Integration, Analysis, and Communications” Raleigh, North Carolina, May 14–17, 2007

  8. Cotton WR, Pielke RA, Walko RL, Liston GE, Tremback CJ, Jiang H, McAnelly RL, Harrington JY, Nicholls ME, Carrio GG, McFadden JP (2003) RAMS 2001: current status and future directions. Meteorog Atmos Phys 82:5–29

    Article  Google Scholar 

  9. Daley R (1991) Atmospheric data analysis. Cambridge University Press, Cambridge

    Google Scholar 

  10. Delle Monache L, Wilczak J, McKeen S, Grell G, Pagowski M, Peckham S, Stull R, McHenry J, McQueen J (2008) A Kalman-filter bias correction of ozone deterministic, ensemble-averaged, and probabilistic forecasts. Tellus B 60:238–249

    Article  Google Scholar 

  11. Denby B (Editor), Brandt J, Elbern H, Frydendall J, Heemink A, Hvidberg M, Kahnert M, Tarrason L, van Loon M, Walker SE, Zlatev Z (2006) Data assimilation in regional scale atmospheric chemical models. NMR Workshop at NILU, Kjeller, Norway, 15 November 2005. NILU: OR 43/2006. ISBN:82-425-1766-5

  12. Denby B, Spang W (2010) The combined use of models and monitoring for applications related to the European Air Quality Directive: A working sub-group of Fairmode. Proc. of 13th International Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes, Paris, France

  13. Denby B, Horálek J, Walker SE, Eben K, Fiala J (2005) Interpolation and assimilation methods for European scale air quality assessment and mapping. Part I: Review and recommendations. ETC/ACC Technical Paper 2005/7. December 2005. Final draft

  14. Denby B, Garcia V, Holland DM, Hogrege C (2009) Integration of air quality modeling and monitoring data for enhanced health exposure assessment. Environmental Management. Springer-Verlag, New York, NY, pp 46–49

    Google Scholar 

  15. EC (2008) 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. Official Journal L 152, 11(6), 2008, pp. 1–44

  16. Elbern H, Schmidt H (2000) Ozone episode analysis by four-dimensional variational chemistry data assimilation. J Geophys Res 106(D4):3569–3590

    Article  Google Scholar 

  17. Elbern H, Strunk A, Schmidt H, Talagrand O (2007) Emission rate and chemical state estimation by 4-dimensional variational inversion. Atmos Chem Phys 7:3749–3769

    CAS  Article  Google Scholar 

  18. Finardi S (Editor), Baklanov A, Clappier A, Fay B, Joffre S, Karppinen A, Ødegard V, Slørdal LH, Sofiev M, Sokhi RS, Stein A (2005) Improved interfaces and meteorological pre-processors for urban air pollution models. FUMAPEX Report D5.2e3, Milan, Italy, 55 pp,

  19. Gariazzo C, Silibello C, Finardi S, Radice P, Piersanti A, Calori G, Cecinato A, Perrino C, Nussio F, Cagnoli M, Pelliccioni A, Gobbi GP, Di Filippo P (2007) A gas/aerosol air pollutants study over the urban area of Rome using a comprehensive chemical transport model. Atmos Environ 41:7286–7303

    CAS  Article  Google Scholar 

  20. Horálek J, Kurfürst P, Denby B, de Smet P, de Leeuw F, Brabec M, Fiala J (2005) Interpolation and assimilation methods for European scale air quality assessment and mapping. Part II: Development and testing new methodologies. ETC/ACC Technical Paper 2005/8. December 2005. Final draft

  21. IIASA (2001) RAINS-Europe

  22. Janssen S, Dumont G, Fierens F, Mensink C (2008) Spatial interpolation of air pollution measurements using CORINE land cover data. Atmos Environ 42:4884–4903

    CAS  Article  Google Scholar 

  23. Kalnay E (2003) Atmospheric modeling, data assimilation and predictability. Cambridge University Press, Cambridge

    Google Scholar 

  24. Messina P, D’Isidoro M, Maurizi A, Fierli F (2011) Impact of assimilated observations on improving tropospheric ozone simulations. Atmos Environ 45:6674–6681

    CAS  Article  Google Scholar 

  25. Nanni A, Radice P (2004) Sensitivity analysis of three EF methodologies for PM10 in use with climatological dispersion modelling in urban Italian study cases. Proc. of 9th Int. Conf. on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes. Garmisch-Partenkirchen (Germany) 1:309–314

    Google Scholar 

  26. Pagowski M, Grell GA, McKeen SA, Peckham SE, Devenyi D (2010) Three-dimensional variational data assimilation of ozone and fine particulate matter observations: Some results using the Weather Research and Forecasting – Chemistry model and Grid-point Statistical Interpolation. Q J R Meteorol Soc 136:2013–2024

    Article  Google Scholar 

  27. Petritoli A, Palazzi E, Giovanelli G, DiNicolantonio W, Ballista G, Carnevale C, Finzi G, Pisoni E, Volta M (2011) Combined use of space-borne observations of NO2 and regional CTM model for air quality monitoring in northern Italy. Int J Environ Pollut 47:158–166

    CAS  Article  Google Scholar 

  28. Physick WL, Cope ME, Lee S, Hurley PJ (2007) An approach for estimating exposure to ambient concentrations. J Expo Sci Environ Epidemiol 17:76–83

    CAS  Article  Google Scholar 

  29. Silibello C, Calori G, Brusasca G, Giudici A, Angelino E, Fossati G, Peroni E, Buganza E (2008) Modelling of PM10 concentrations over Milan urban area using two aerosol modules. Environ Model Softw 23:333–343

    Article  Google Scholar 

  30. Silibello C, D’Allura A, Finardi S, Radice P (2013) EXPAH-Technical report on FARM model capability to simulate PM2.5 and PAHs in the base case – Action 4.5. ARIANET R2013.06. May 2013 (available at–06_ARIANET_EXPAH_A4.5_final.pdf).

  31. Tombette M, Mallet V, Sportisse B (2009) PM10 data assimilation over Europe with the optimal interpolation method. Atmos Chem Phys 9:57–70

    CAS  Article  Google Scholar 

  32. Vialetto G, Contaldi M, De Lauretis R, Lelli M, Mazzotta V, Pignatelli T (2005) Emission scenarios of air pollutants in Italy using integrated assessment models. Pollut Atmosphérique 185:71

    CAS  Google Scholar 

  33. Walker SE, Schaap M, Slini L (2006) Data assimilation, Air4EU WP6 synthesis. Milestone Report 6:8

    Google Scholar 

  34. Zanini G, Pignatelli T, Conforti F, Vialetto G, Vitali L, Brusasca G, Calori G, Finardi S, Radice P, Silibello C (2005) The MINNI Project: an integrated assessment modeling system for policy making. proceedings of the 15th MODSIM congress: “Advances and applications for management and decision making”, Melbourne, Australia

  35. Zhang Y, Bocquet M, Mallet V, Seigneur C, Baklanov A (2012) Real-time air quality forecasting, part II: state of the science, current research needs, and future prospects. Atmos Environ 60:656–676

    CAS  Article  Google Scholar 

Download references


The LIFE+ EU financial program (EC 614/2007) is acknowledged for the provision of funding for EXPAH project (LIFE09 ENV/IT/082). The authors wish to thanks Paola Radice (Arianet S.r.l.) for her support in preparing emission data and Sandro Finardi and Alessio D’Allura (Arianet S.r.l.) for preparing meteorological fields and managing QualeAria forecast system. The authors would also like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper.

Author information



Corresponding author

Correspondence to Camillo Silibello.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Silibello, C., Bolignano, A., Sozzi, R. et al. Application of a chemical transport model and optimized data assimilation methods to improve air quality assessment. Air Qual Atmos Health 7, 283–296 (2014).

Download citation


  • Air quality models
  • Spatial analysis
  • Data assimilation
  • Emission inventories
  • Exposure modeling