Skip to main content
SpringerLink
Log in

A methodology for the evaluation of re-analyzed PM10 concentration fields: a case study over the PO Valley

  • Published:
Air Quality, Atmosphere & Health Aims and scope Submit manuscript

Abstract

This study presents a general MonteCarlo-based methodology for the validation of chemical transport model (CTM) concentration re-analyzed fields over a certain domain. A set of re-analyses is evaluated by applying the observation uncertainty (U) approach (Thunis et al. Atmos Environ 59:476–482 2012b), developed in the frame of Forum for Air Quality Modelling in Europe (FAIRMODE; http://fairmode.ew.eea.europa.eu/). Modeled results from the chemical transport model (Transport and Chemical Aerosol Model (TCAM)) (Carnevale et al. Sci Total Environ 390:166–176 2008) for year 2005 are used as background values. The model simulation domain covers the Po Valley with a 6 × 6 km2 resolution. Measured data for both assimilation and evaluation are provided by approximately 50 monitoring stations distributed across the Po Valley. The main statistical indicators (i.e., bias, root mean square error, correlation coefficient, standard deviation) as well as different types of diagrams (scatter plots and target plots) have been produced and visualized with the Delta evaluation tool V3.6. The target criteria are fulfilled by 97 % of the sites for the re-analyzed fields and by 61 % for the modeled values, showing how the application of the assimilation technique improves TCAM raw fields. The model underprediction of PM10 concentration (normalized mean bias (NMB) up to −70 %) is reduced at almost all sites in the re-analysis (NMB in the range −20–+20 %,). The correlation coefficient R is higher for the re-analyzed fields (0.7–1) compared to the raw ones (0.2–0.8).

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • AQD (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 (No. 152). Official journal.

  • Candiani G, Carnevale C, Finzi G, Pisoni E, Volta M (2013) A comparison of reanalysis techniques: applying optimal interpolation and ensemble Kalman filtering to improve air quality monitoring at mesoscale. Sci Total Environ 458–460:7–14

    Article  Google Scholar 

  • Carnevale C, Finzi G, Pederzoli A, Pisoni E, Turrini E, Volta M (2014) Applying the Delta tool to support the air quality directive: the evaluation of the TCAM chemical transport model. Air Qual Atmos Health 1–12. doi:10.1007/s11869-014-0240-4

  • Carnevale C, Decanini E, Volta M (2008) Design and validation of a multiphase 3D model to simulate tropospheric pollution. Sci Total Environ 390:166–176

    Article  CAS  Google Scholar 

  • Carnevale C, Finzi G, Pisoni E, Thunis P, Volta M (2012) The impact of thermodynamic module in the CTM performances. Atmos Environ 61:652–660

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  • Cuvelier C, Thunis P, Vautard R, Amann M, Bessagnet B, Bedogni M, Berkowicz R, Brandt J, Brocheton F, Builtjes P, Carnevale C, Denby B, Douros J, Graf A, Hellmuth O, Hodzic A, Honore C, Jonson J, Kerschbaumer A, de Leeuw F, Minguzzi E, Moussiopoulos N, Pertot C, Peuch VH, Pirovano G, Rouil L, Sauter F, Schaap M, Stern R, Tarrason L, Vignati E, Volta M, White L, Wind P, Zuber A (2007) CityDelta: a model intercomparison study to explore the impact of emission reductions in European cities in 2010. Atmos 365 Environ 41(1):189–207

    Article  CAS  Google Scholar 

  • Evensen G (1994) Inverse methods and data assimilation in non-linear ocean models. Phys D 77:108–129

    Article  Google Scholar 

  • Fountoukis C, Nenes A, Sullivan A, Weber R, VanReken T, Fischer M, Matias E, Moya M, Farmer D, Cohen R (2009) Thermodynamic characterization of Mexico City aerosol during MILAGRO 2006. Atmos Chem Phys 9:2141–2156

    Article  CAS  Google Scholar 

  • Gou T, Sandu A (2011) Continuous versus discrete advection adjoints in chemical data assimilation with CMAQ. Atmos Environ 45(28):4868–4881

    Article  CAS  Google Scholar 

  • Hogrefe C, Lynn B, Goldberg R, Rosenzweig C, Zalewsky E, Hao W et al (2009) A combined model observation approach to estimate historic gridded fields of PM2.5 mass and species concentrations. Atmos Environ 43(16):2561–2570

    Article  CAS  Google Scholar 

  • Marchuk G (1975) Methods of numerical mathematics Springer, 1975

  • Moody JE, Utans J (1991) Principled architecture selection for neural networks: application to corporate bond rating prediction. In: Moody JE, Hanson SJ, Lippmann RP (eds) Advances in neural information processing systems 4. Morgan Kaufmann Publishers, SanMateo, pp 683–690

    Google Scholar 

  • Nenes A, Pandis S, Pilinis C (1998) ISORROPIA: a new thermodynamic equilibrium model for multiphase multicomponent inorganic aerosols. Aquat Geochem 4(416):123–152

    Article  CAS  Google Scholar 

  • Pepper DW, Kern CD, Long PE (1979) Modelling the dispersion of atmospheric pollution using cubic splines and chapeau functions. Atmos Environ 13:223–237

  • Pernigotti D, Georgieva E, Thunis P, Cuvelier C, de Meij A (2012) The impact of meteorology on air quality simulations over the Po Valley in Northern Italy. Atmos Environ 51:303–310

    Article  CAS  Google Scholar 

  • Pernigotti D, Thunis P, Gerboles M, Belis CA (2013a) Model quality objectives based on measurement uncertainty. Part II: NO2 and PM10. Atmos Environ 79:869–878

    Article  CAS  Google Scholar 

  • Pernigotti D, Thunis P, Cuvelier C, Georgieva E, Gsella A, De Meij A, Pirovano G, Balzarini A, Riva GM, Carnevale C, Pisoni E, Volta M, Bessagnet B, Kerschbaumer A, Viaene P, De Ridder K, Nyiri A, Wind P (2013b) POMI: a model inter-comparison exercise over the Po Valley. Air Qual Atmos Health. doi:10.1007/s11869-013-0211-1

    Google Scholar 

  • Scire JS, Insley EM, Yamartino RJ (1990) Model Formulation and User’s Guide for the CALMET Meteorological Model. Technical Report A025-1, California Air Resources Board, Sacramento, CA

  • Singh V, Carnevale C, Finzi G, Pisoni E, Volta M (2011) A cokriging based approach to reconstruct air pollution maps, processing measurement station concentrations and deterministic model simulations. Environ Model Softw 26:778–786

    Article  Google Scholar 

  • Thunis P, Triacchini G, White L, Maffeis G, Volta M (2009) Air pollution and emission reductions over the Po-valley: air quality modelling and integrated assessment, Proceedings of the 18th World IMACS / MODSIM Congress, Cairns, Australia 13–17 July 2009

  • Thunis P, Cuvelier C, Roberts P, White L, Nyiri A, Stern R, Kerschbaumer A, Bessagnet B, Bergström R, Schaap M (2010) EURODELTA: Evaluation of a sectoral approach to integrated assessment modelling—Second report. Available at http://www.eurosfaire.prd.fr/7pc/doc/1301907090_lbna24474enc_002.pdf?PHPSESSID=75bb8ddcaca78c97a1777f06549d5065

  • Thunis P, Georgieva E, Pederzoli A (2012a) A FAIRMODE benchmarking tool to evaluate air quality model performances in regulatory applications. Environ Model Softw 38:220–230

    Article  Google Scholar 

  • Thunis P, Pederzoli A, Georgieva E, Cuvelier C, Pernigotti D (2014) DELTA V3.6 User’s Guide, Available at http://aqm.jrc.ec.europa.eu/DELTA/Publications.htm

  • Thunis P, Pederzoli A, Pernigotti D (2012b) Performance criteria for evaluating air quality model applications for policy use. Atmos Environ 59:476–482

    Article  CAS  Google Scholar 

  • Thunis P, Pernigotti D, Gerboles M, Belis C (2013) Model quality objectives based on measurement uncertainty. Part I: O3. Atmos Environ 79:861–868

    Article  CAS  Google Scholar 

  • van de Kassteele J, Velders GJ (2006) Uncertainty assessment of local concentrations derived from error-in-variable external drift kriging and its relationship to the 2010 air quality standard. Atmos Environ 40(14):2583–2595

    Article  Google Scholar 

  • Vira J, Sofiev M (2012) On variational data assimilation for estimating the model initial conditions and emission fluxes for short-term forecasting of SOx concentrations. Atmos Environ 46:318–328

    Article  CAS  Google Scholar 

  • Wang X, Mallet V, Berroir J-P, Herlin I (2011) Assimilation of OMI NO2 retrievals into a regional chemistry-transport model for improving air quality forecasts over Europe. Atmos Environ 45(2):485–492

    Article  CAS  Google Scholar 

  • Yamartino RJ, Scire JS, Carmichael GR, Chang YS (1992) The CALGRID mesoscale photochemical grid model—I. Model formulation. Atmos Environ 26A:1493–1512

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical and Industrial Engineering, University of Brescia, Via Branze 38, 25123, Brescia, Italy

    C. Carnevale, G. Finzi, A. Pederzoli, E. Turrini & M. Volta

  2. European Commission, Joint Research Centre (JRC), Via E. Fermi 2749, 21027, Ispra, VA, Italy

    E. Pisoni & P. Thunis

Authors
  1. C. Carnevale
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. G. Finzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. Pederzoli
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. E. Pisoni
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. P. Thunis
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. E. Turrini
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. M. Volta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. Pederzoli.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Carnevale, C., Finzi, G., Pederzoli, A. et al. A methodology for the evaluation of re-analyzed PM10 concentration fields: a case study over the PO Valley. Air Qual Atmos Health 8, 533–544 (2015). https://doi.org/10.1007/s11869-014-0307-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11869-014-0307-2

Keywords

Search

Navigation