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POMI: a model inter-comparison exercise over the Po Valley

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Abstract

The Po Valley (Italy) model inter-comparison exercise (POMI) has been carried out in order to explore the changes in air quality in response to changes in emissions. The starting point was the evaluation of the simulated particulate matter and ozone (O3) modelled concentrations against observations for the year 2005 of the six participating chemical transport models. As models were run with the same configuration in terms of spatial resolution, boundary condition, emissions and meteorology, the differences presented in the models’ results are only related to their formulation. As described in the paper, significant efforts have been made to improve the accuracy of the anthropogenic emissions and meteorological input data. Nevertheless, none of the models using the proposed meteorology succeeded to fulfil the quality performance criteria set in the 2008 Air Quality Directive and in the literature for particulate matter, while also for ozone the results are not very satisfying. Although the overall performances look better for O3 than for particulate matter with an aerodynamic diameter smaller than 10 μm (PM10), the models tend to exhibit a similar behaviour and show the largest model variability in locations where concentrations are the highest (urban areas for PM10 and suburbs and hilly areas for O3). While differences are significant in terms of standard deviation and bias, the correlation remains quite similar among models indicating that models generally capture well the main temporal variations, especially the seasonal ones. Possible explanations for this common behaviour and a discussion of the differences among models’ results are presented in this paper.

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References

  • Angelino E, Bedogni M, Carnevale C, Minguzzi E, Peroni E, Pertot C, Pirovano G (2005) PM10 long-term assessment of emission reduction scenarios over northern Italy. Proceeding of the 10th Harmonization within Atmospheric Dispersion Modelling for Regulatory Purposes

  • 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

  • Beekmann M, Kerschbaumer A, Reimer E, Stern R, Möller D (2007) PM measurement campaign OVERT in the Greater Berlin area: model evaluation with chemically specified particulate matter observations for a one year period. Atmos Chem Phys 7:55–68

    Article  CAS  Google Scholar 

  • Bessagnet B, Menut L, Curci G, Hodzic A, Guillaume B, Liousse C, Moukhtar S, Pun B, Seigneur C, Schulz M (2009) Regional modeling of carbonaceous aerosols over Europe—focus on secondary organic aerosols. J Atmos Chem 61(3):175–202. doi:10.1007/s10874-009-9129-2

    Article  Google Scholar 

  • Boylan JW, Russell AG (2006) PM and light extinction model performance metrics, goals, and criteria for three-dimensional air quality models. Atmos Environ 40(26):4946–4959. doi:10.1016/j.atmosenv.2005.09.087

    Article  CAS  Google Scholar 

  • CAFE (2005) Impact Assessment, Annex to COM 446 and 447, Brussels

  • Carnevale C, Decanini E, Volta M (2008a) 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, Volta M (2008b) Modelling assessment of PM10 exposure control policies in Northern Italy. Ecol Model 217(3–4):219–229. doi:10.1016/j.ecolmodel.2008.06.005

    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. doi:10.1016/j.atmosenv.2012.06.058

    Article  CAS  Google Scholar 

  • Chemel C, Sokhi RS, Yu Y, Hayman GD, Vincent KJ, Dore AJ, Tang YS, Prain HD, Fisher BEA (2010) Evaluation of a CMAQ simulation at high resolution over the UK for the calendar year 2003. Atmos Environ 44(24):2927–2939. doi:10.1016/j.atmosenv.2010.03.029

    Article  CAS  Google Scholar 

  • Chen F, Dudhia J (2001) Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon Weather Rev 129(4):569–585

    Article  Google Scholar 

  • Cressman GP (1959) An operational objective analysis system. Mon Weather Rev 87(10):367–374

    Article  Google Scholar 

  • Cuvelier C, Thunis P, Vautard R, Amann M, Bessagnet B, Bedogni M, Berkowicz R, Brandt J, Brocheton F, Builtjes P (2007) CityDelta: a model intercomparison study to explore the impact of emission reductions in European cities in 2010. Atmos Environ 41:189–207. doi:10.1016/j.atmosenv.2006.07.036

    Article  CAS  Google Scholar 

  • De Meij A, Wagner S, Cuvelier C, Dentener F, Gobron N, Thunis P, Schaap M (2007) Model evaluation and scale issues in chemical and optical aerosol properties over the greater Milan area (Italy), for June 2001. Atmos Res 85(2):243–267. doi:10.1016/j.atmosres.2007.02.001

    Article  Google Scholar 

  • De Meij A, Gzella A, Cuvelier C, Thunis P, Bessagnet B, Vinuesa JF, Menut L, Kelder HM (2009a) The impact of MM5 and WRF meteorology over complex terrain on CHIMERE model calculations. Atmos Chem Phys 9(17):6611–6632. doi:10.5194/acp-9-6611-2009

    Article  Google Scholar 

  • De Meij A, Thunis P, Bessagnet B, Cuvelier C (2009b) The sensitivity of the CHIMERE model to emissions reduction scenarios on air quality in Northern Italy. Atmos Environ 43(11):1897–1907. doi:10.1016/j.atmosenv.2008.12.036

    Article  Google Scholar 

  • Denby B (2010) Guidance on the use of models for the European Air Quality Directive. A working document of the Forum for Air Quality Modelling in Europe FAIRMODE. ETC/ACC. Available from: http://fairmode.ew.eea.europa.eu/

  • Derwent D, Fraser A, Abbott J, Willis P, Murrells T (2010) Evaluating the performance of air quality models. Department for Environment and Rural Affairs. Available from: http://uk-air.defra.gov.uk/reports/cat05/1006241607_100608_MIP_Final_Version.pdf

  • Deserti M, Bande S, Angelino E, Pession G, Dalan F, Minguzzi E, Stortini M, Bonafè G, De Maria R, Fossati G, Peroni E, et al (2006) Rapporto tecnico sulla applicazione di modellistica al bacino padano adriatico. APAT CTN-ACE [Italian]. Available from: http://www.isprambiente.gov.it/site/_files/aria/rapporto_modellistica_finale_pdf.pdf

  • Dudhia J (1989) Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J Atmos Sci 46(20):3077–3107

    Article  Google Scholar 

  • EMEP (2013) Transboundary acidification, eutrophication and ground level ozone in Europe in 2011. EMEP status report. Available from http://www.emep.int

  • ENVIRON (2010) User’s guide. Comprehensive air quality model with extensions. Version 5.21. ENVIRON International Corporation, Novato

  • EPA (2007) Guidance on the use of models and other analyses for demonstrating attainment of air quality goals for ozone, PM2.5, and regional haze. U.S. Environmental Protection Agency. Available from: http://www.epa.gov/scram001/guidance/guide/final-03-pm-rh-guidance.pdf

  • Gery MW, Whitten GZ, Killus JP, Dodge MC (1989) A photochemical kinetics mechanism for urban and regional scale computer modeling. J Geophys Res 94(D10):12925–12956. doi:10.1029/JD094iD10p12925

    Article  CAS  Google Scholar 

  • Gilardoni S, Vignati E, Cavalli F, Putaud JP, Larsen BR, Karl M, Stenström K, Genberg J, Henne S, Dentener F (2011) Better constraints on sources of carbonaceous aerosols using a combined 14C—macro tracer analysis in a European rural background site. Atmos Chem Phys Discuss 11(1):2503–2547. doi:10.5194/acpd-11-2503-2011

    Article  Google Scholar 

  • Grell GA, Dudhia J, Stau!er DR (1995) A description of the fifth generation Pennsylvania State/NCAR mesoscale model (MM5). National Center for Atmospheric Research, Boulder

    Google Scholar 

  • Henne S, Brunner D, Folini D, Solberg S, Klausen J, Buchmann B (2010) Assessment of parameters describing representativeness of air quality in-situ measurement sites. Atmos Chem Phys 10(8):3561–3581. doi:10.5194/acp-10-3561-2010

    Article  CAS  Google Scholar 

  • Hong S-Y, Pan H-L (1996) Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon Weather Rev 124(10):2322–2339

    Article  Google Scholar 

  • Jongen S, Bonafè G (2006) LAMI verification for air quality forecast and assessment purposes: case studies, special measurement campaigns, long-term evaluation. ARPA-SIM, Bologna. Available from http://www.arpa.emr.it/cms3/documenti/_cerca_doc/meteo/ambiente/report_lm_verif_fv.pdf

  • JRC (2008) 4th intermediate technical/scientific report. Collaborative research project for air pollution reduction in Lombardy region (2006–2010). Available from http://ita.arpalombardia.it/ITA/qaria/doc_ProgettiChiusiJRC.asp

  • Kukkonen J, Olsson T, Schultz DM, Baklanov A, Klein T, Miranda AI, Monteiro A, Hirtl M, Tarvainen V, Boy M, Peuch V-H et al (2012) A review of operational, regional-scale, chemical weather forecasting models in Europe. Atmos Chem Phys 12(1):1–87. doi:10.5194/acp-12-1-2012

    Article  CAS  Google Scholar 

  • Lefebvre W, Vercauteren J, Schrooten L, Janssen S, Degraeuwe B, Maenhaut W, De Vlieger I, Vankerkom J, Cosemans G, Mensink C, Veldeman N et al (2011) Validation of the MIMOSA–AURORA–IFDM model chain for policy support: modeling concentrations of elemental carbon in Flanders. Atmos Environ 45(37):6705–6713. doi:10.1016/j.atmosenv.2011.08.033

    Article  CAS  Google Scholar 

  • Lonati G, Giugliano M, Butelli P, Romele L, Tardivo R (2005) Major chemical components of PM2.5 in Milan (Italy). Atmos Environ 39(10):1925–1934. doi:10.1016/j.atmosenv.2004.12.012

    Article  CAS  Google Scholar 

  • Lonati G, Giugliano M, Ozgen S (2008) Primary and secondary components of PM2.5 in Milan (Italy). Environ Int 34(5):665–670. doi:10.1016/j.envint.2007.12.009

    Article  CAS  Google Scholar 

  • McMurry PH, Shepherd MF, Vickery JS, NARSTO (2004) Particulate matter science for policy makers: a NARSTO assessment. Cambridge University Press, Cambridge

    Google Scholar 

  • Metzger S (2002) Gas/aerosol partitioning: 1. A computationally efficient model. J Geophys Res 107(D16). doi:10.1029/2001JD001102. Available from: http://www.agu.org/pubs/crossref/2002/2001JD001102.shtml

  • Minguzzi E, Bedogni M, Carnevale C, Pirovano G (2005) Sensitivity of CTM simulations to meteorological input. IJEP 24(1/2/3/4):36. doi:10.1504/IJEP.2005.007383

    Article  CAS  Google Scholar 

  • Mlawer EJ, Taubman SJ, Brown PD, Iacono MJ, Clough SA (1997) Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J Geophys Res 102(D14):16663–16682. doi:10.1029/97JD00237

    Article  CAS  Google Scholar 

  • Nenes A, Pandis S, Plinis C (1998) ISORROPIA: a new thermodynamic equilibrium model for multiphase multicomponent inorganic aerosol. Aquat Geochem 4(1):123–152

    Article  CAS  Google Scholar 

  • Pernigotti D, Georgieva E, Thunis P, Bessagnet B (2012a) Impact of meteorology on air quality modeling over the Po valley in northern Italy. Atmos Environ 51:303–310. doi:10.1016/j.atmosenv.2011.12.059

    Article  CAS  Google Scholar 

  • Pernigotti D, Georgieva E, Thunis P, Bessagnet B (2012b) Impact of meteorological modelling on air quality: summer and winter episodes in the Po valley (Northern Italy). Int J Environ Pollut 50:111–119

    CAS  Google Scholar 

  • Pun BK, Seigneur C (2007) Investigative modeling of new pathways for secondary organic aerosol formation. Atmos Chem Phys Discuss 7(1):203–245. doi:10.5194/acpd-7-203-2007

    Article  Google Scholar 

  • Schell B, Ackermann IJ, Hass H, Binkowski FS, Ebel A (2001) Modeling the formation of secondary organic aerosol within a comprehensive air quality model system. J Geophys Res 106(D22):28275–28293. doi:10.1029/2001JD000384

    Article  CAS  Google Scholar 

  • Schmidt H, Derognat C, Vautard R, Beekmann M (2001) A comparison of simulated and observed ozone mixing ratios for the summer of 1998 in Western Europe. Atmos Environ 35:6277–6297

    Article  CAS  Google Scholar 

  • Simpson D, Benedictow A, Berge H, Bergström R, Emberson LD, Fagerli H, Hayman GD, Gauss M, Jonson JE, Jenkin ME, Nyíri A et al (2012) The EMEP MSC-W chemical transport model—part 1: model description. Atmos Chem Phys Discuss 12(2):3781–3874. doi:10.5194/acpd-12-3781-2012

    Article  Google Scholar 

  • Stauffer DR, Seaman NL (1994) Multiscale four-dimensional data assimilation. J Appl Meteorol 33(3):416–434

    Article  Google Scholar 

  • Stern R, Kerschbaumer A, Reimer E (2009) Description of the chemical transport model REM-CALGRID. Institut fuer Meteorologie, Berlin

  • Strader R (1999) Evaluation of secondary organic aerosol formation in winter. Atmos Environ 33(29):4849–4863. doi:10.1016/S1352-2310(99)00310-6

    Article  CAS  Google Scholar 

  • Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res 106(D7):7183–7192. doi:10.1029/2000JD900719

    Article  Google Scholar 

  • Thunis P, Georgieva E, Pederzoli A (2011) The DELTA tool and benchmarking report template. Concepts and user guide, version 2. Available from: http://aqm.jrc.it/DELTA/data/DELTA_SG4_V2.pdf

  • Triacchini G, Pastorello C, Gzella A, Dilara P (2009) Preparation of a spatialised emission inventory as input for modelling. EPA, Baltimore. Available from: http://www.epa.gov/ttn/chief/conference/ei18/session3/triacchini.pdf

  • Van Loon M, Vautard R, Schaap M, Bergström R, Bessagnet B, Brandt J, Builtjes P, Christensen JH, Cuvelier C, Graff A, Jonson JE et al (2007) Evaluation of long-term ozone simulations from seven regional air quality models and their ensemble. Atmos Environ 41(10):2083–2097. doi:10.1016/j.atmosenv.2006.10.073

    Article  Google Scholar 

  • Vautard R, Builtjes P, Thunis P, Cuvelier C, Bedogni M, Bessagnet B, Honoré C, Moussiopoulos N, Pirovano G, Schaap M (2007) Evaluation and intercomparison of Ozone and PM10 simulations by several chemistry transport models over four European cities within the CityDelta project. Atmos Environ 41(1):173–188. doi:10.1016/j.atmosenv.2006.07.039

    Article  CAS  Google Scholar 

  • Wexler AS, Lurmann FW, Seinfeld JH (1994) Modelling urban and regional aerosols—I. Model development. Atmos Environ 28(3):531–546. doi:10.1016/1352-2310(94)90129-5

    Article  CAS  Google Scholar 

  • Yarwood G, Rao S, Yocke M, Whitten GZ (2005) Updates to the carbon bond chemical mechanism: CB05. Novato, California. Available from: http://www.camx.com/publ/pdfs/CB05_Final_Report_120805.pdf

Download references

Acknowledgments

Part of this work has been funded by the authorities of the Lombardy region. Giuseppe Triacchini and Cinzia Pastorello are thanked for their contribution to the elaboration of the emission inventory, Jean Philippe Putaud for providing the EMEP Ispra data and Panagiota Dilara for her support in the project activities. The regional agencies of the Po Valley are acknowledged for providing the meteorological and air quality data used in this work. The Cyprus Institute received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement n° 226144. RSE authors thank Chris Emery and Paola Crippa for their contribution in setting up CAMx simulations.

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  1. A. Kerschbaumer

    Present address: Senate Department for Urban Development and the Environment, Brueckenstr. 6, 10179, Berlin, Germany

Authors and Affiliations

  1. Institute for Environment and Sustainability, European Commission, Joint Research Centre, Via E. Fermi 2749, 21027, Ispra, Varese, Italy

    D. Pernigotti, P. Thunis & C. Cuvelier

  2. National Institute of Meteorology and Hydrology, (NIMH-BAS), 66 Blvd Tsarigradsko chaussee, 1784, Sofia, Bulgaria

    E. Georgieva

  3. European Environment Agency (EEA), Kongens Nytorv 6, 1050, Copenhagen K, Denmark

    A. Gsella

  4. Energy, Environment and Water Research Centre, The Cyprus Institute, 20 Kavafi Street, 1645, Nicosia, Cyprus

    A. De Meij

  5. Ricerca sul Sistema Energetico (RSE SpA), Via Rubattino, 54, 20134, Milan, Italy

    G. Pirovano, A. Balzarini & G. M. Riva

  6. University of Brescia, DIMI, Via Branze 38, 25123, Brescia, Italy

    C. Carnevale, E. Pisoni & M. Volta

  7. Institut National de l’Environnement Industriel et des Risques, INERIS, Parc technologique ALATA, B.P. No.2, 60550, Verneuil en Halatte, France

    B. Bessagnet

  8. Institute of Meteorology, Freie Universitaet Berlin, Carl-Heinrich-Becker-Weg 6-10, 12165, Berlin, Germany

    A. Kerschbaumer

  9. VITO, Boeretang 200, 2400, Mol, Belgium

    P. Viaene & K. De Ridder

  10. Norwegian Meteorological Institute, P.O. Box 43, Blindern, 0313, Oslo, Norway

    A. Nyiri & P. Wind

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Appendix: Statistical indicators

Appendix: Statistical indicators

The root mean square error

$$ \mathrm{RMSE}=\sqrt{\frac{1}{N}{\displaystyle \sum_{i\kern0.5em =\kern0.5em 1}^N{\left({O}_i-{M}_i\right)}^2}} $$

The bias

$$ \overline{M}-\overline{O} $$

The Pearson correlation coefficient (R)

$$ R\kern0.5em =\kern0.5em \frac{{\displaystyle \sum_{i\kern0.5em =\kern0.5em 1}^N\left({M}_i\kern0.5em -\kern0.5em \overline{M}\right)\bullet}\left({O}_i\kern0.5em -\kern0.5em \overline{O}\right)}{\left[\sqrt{{\displaystyle \sum_{i\kern0.5em =\kern0.5em 1}^N{\left({M}_i\kern0.5em -\kern0.5em \overline{M}\right)}^2}}\right]\left[\sqrt{{\displaystyle \sum_{i\kern0.5em =\kern0.5em 1}^N{\left({O}_i\kern0.5em -\kern0.5em \overline{O}\right)}^2}}\right]} $$

M denotes modelled value, O denotes observed value, N is the number of paired values considered and over bars denote averaged values.

The index of agreement (IOA)

$$ \mathrm{IOA}\kern0.5em =\kern0.5em 1\kern0.5em -\kern0.5em \frac{N\bullet {\mathrm{RMSE}}^2}{{\displaystyle \sum_{i\kern0.5em =\kern0.5em 1}^N{\left(\left|{M}_i\kern0.5em -\kern0.5em \overline{O}\right|\kern0.5em +\kern0.5em \left|{O}_i\kern0.5em -\kern0.5em \overline{O}\right|\right)}^2}} $$

Index of agreement (IOA) determines the extent to which magnitudes of Ō are related to the predicted deviations about Ō. The perfect value of IOA is 1.

The mean fractional bias

$$ \mathrm{MFB}\kern0.5em =\kern0.5em \frac{1}{N}{\displaystyle \sum_{i\kern0.5em =\kern0.5em 1}^N\frac{M_i\kern0.5em -\kern0.5em {O}_i}{\left[\left({M}_i\kern0.5em +\kern0.5em {O}_i\right)/2\right]}} $$

Mean fractional bias (MFB) is a useful indicator because it has the advantage of equally weighting positive and negative bias estimates. It has also the advantage of not considering observations as the true value. MFB ranges from −200 to +200 %.

The mean fractional error

$$ \mathrm{MFE}\kern0.5em =\kern0.5em \frac{1}{N}{\displaystyle \sum_{i\kern0.5em =\kern0.5em 1}^N\frac{\left|{M}_i\kern0.5em -\kern0.5em {O}_i\right|}{\left[\left({M}_i\kern0.5em +\kern0.5em {O}_i\right)/2\right]}} $$

Similarly to the MFB, the mean fractional error (MFE) gives equal weight to under- and over-prediction, is not sensitive to a threshold in measured values and does not assume that observations are the truth (i.e. the denominator is the sum of observed and predicted). MFE ranges from 0 to +200 %.

The factor of predictions within a factor of 2 of observations

$$ \mathrm{FAC}2\kern0.5em =\kern0.5em \frac{1}{N}{\displaystyle \sum_{i\kern0.5em =\kern0.5em 1}^N{n}_i}\kern1em \mathrm{with}\kern0.5em {n}_i\kern0.5em =\kern0.5em \left\{\begin{array}{l}1\kern1em \mathrm{for}\kern1em 0.5\le \left|{M}_i/{O}_i\right|\le 2\\ {}0\kern1em \mathrm{else}\end{array}\right. $$

The relative directive error after (Denby 2010)

$$ \mathrm{RDE}\kern0.5em =\kern0.5em \frac{\left|{O}_{\mathrm{LV}}\kern0.5em -\kern0.5em {M}_{\mathrm{LV}}\right|}{\mathrm{LV}} $$

where OLV is the closest observed concentration to the limit value concentration (LV) and MLV is the correspondingly ranked modelled concentrations. The RDE has been defined in relation to the AQD (2008) in order to give a mathematical expression of the “model uncertainty” term in the AQD.

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Pernigotti, D., Thunis, P., Cuvelier, C. et al. POMI: a model inter-comparison exercise over the Po Valley. Air Qual Atmos Health 6, 701–715 (2013). https://doi.org/10.1007/s11869-013-0211-1

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