Setting-up a Real-Time Air Quality Forecasting system for Serbia: a WRF-Chem feasibility study with different horizontal resolutions and emission inventories

  • Zorica PodrascaninEmail author
Research Article


In this paper, the influence of the horizontal model grid size and anthropogenic gridded emissions on the air quality forecast in Serbia was analyzed using the online-coupled Weather Research and Forecasting model with Chemistry (WRF-Chem). For that purpose, six simulations were performed. The model horizontal grid size was 20 × 20 km, 10 × 10 km, and 5 × 5 km. Two anthropogenic gridded emission inventories with different grid sizes were used, the global RETRO (REanalysis of the TROpospheric chemical composition) and the EMEP (The European Monitoring and Evaluation Program) for each model horizontal grid size. The modeled O3, NO2, and PM10 concentrations in all six simulations were compared with the measured hourly data at the Serbian Environmental Protection Agency (SEPA) stations and an EMEP station during August 2016. The analysis shows that the influence of the model grid size is larger on PM10 than on the O3 and NO2 concentration. The concentration of O3 and PM10 has a similar dependence on the emissions and the model grid size, while NO2 has a larger dependence on the emission than on the model grid size. The simulation with the 5 × 5 km grid size and the EMEP anthropogenic emissions has optimal performance compared with the measured concentration. In this optimal simulation, the modeled O3 concentrations overestimated the measured values at 3 stations and underestimated the measured values at 2 stations. At most stations, the modeled NO2 concentrations underestimated the measured values. The modeled PM10 concentrations highly underestimated the measured values at all stations.


Air quality modeling WRF-Chem model Model grid size Anthropogenic gridded emissions 



The author would also like to thank the Serbian Environmental Protection Agency for providing the measured data. The computing resources for this research were provided by AXIOM, which is operated by the Faculty of Sciences, Novi Sad, Serbia.

Funding information

The paper is a part of the research done within the project “Air quality forecast in the Vojvodina region” (142-451-3608/2017-01), financed by the government of the Autonomous Province of Vojvodina, and “Studying climate change and its influence on the environment: impacts, adaptation and mitigation” (III43007), financed by the Ministry of Education and Science of the Republic of Serbia within the framework of integrated and interdisciplinary research and technological development for the period of 2011–2018.


  1. Badia A, Jorbal O, Voulgarakis A, Dabdub D, García-Pando CP, Hilboll A, Gonçalves M, Janjic Z (2017) Description and evaluation of the Multiscale Online Nonhydrostatic AtmospheRe CHemistry model (NMMB-MONARCH) version 1.0: gas-phase chemistry at global scale. Geosci Model Dev 10:609–638CrossRefGoogle Scholar
  2. Baklanov A, Korsholm U, Mahura A, Petersen C, Gross A (2008) Enviro-HIRLAM: on-line coupled modeling of urban meteorology and air pollution. Adv Sci Res 2:41–46CrossRefGoogle Scholar
  3. Baklanov A, Schlünzen K, Suppan P, Baldasano J, Brunner D, Aksoyoglu S, Carmichael G, Douros J, Flemming J, Forkel R, Galmarini S, Gauss M, Grell G, Hirtl M, Joffre S, Jorba O, Kaas E, Kaasik M, Kallos G, Kong X, Korsholm U, Kurganskiy A, Kushta J, Lohmann U, Mahura A, Manders-Groot A, Maurizi A, Moussiopoulos N, Rao ST, Savage N, Seigneur C, Sokhi RS, Solazzo E, Solomos S, Sørensen B, Tsegas G, Vignati E, Vogel B, Zhang Y (2014) Online coupled regional meteorology chemistry models in Europe: current status and prospects. Atmos Chem Phys 14:317–398CrossRefGoogle Scholar
  4. Chen F, Dudhia J (2001) Coupling an advanced land surface/hydrology model with the Penn State/NCAR MM5 modeling system. Part I: model description and implementation. Mon Wea Rev 129:569–585CrossRefGoogle Scholar
  5. Chin M, Savoie DL, Huebert BJ, Bandy AR, Thornton DC, Bates TS, Quinn PK, Saltzman ES, De Bruyn WJ (2000) Atmospheric sulfur cycle simulated in the global model GOCART: comparison with field observations and regional budgets. J Geophys Res 105(D20):24689–24712CrossRefGoogle Scholar
  6. Chuang MT, Zhang Y, Kang DW (2011) Application of WRF-Chem-MADRID for real-time air quality forecasting over the southeastern United States. Atmos Environ 45:6241–6250CrossRefGoogle Scholar
  7. Damian V, Sandu A, Damian M, Potra F, Carmichael GR (2002) The kinetic preprocessor KPP a software environment for solving chemical kinetics. Comput Chem Eng 26:1567–1579CrossRefGoogle Scholar
  8. Freitas SR, Longo KM, Alonso MF, Pirre M, Marecal V, Grell G, Stockler R, Mello RF, Sanchez GM (2011) PREP-CHEM-SRC-1.0: a preprocessor of trace gas and aerosol emission fields for regional and global atmospheric chemistry models. Geosci Model Dev 4:419–433CrossRefGoogle Scholar
  9. Grell GA, Peckham SE, Schmitz R, McKeen SA, Frost G, Skamarock W, Eder B (2005) Fully coupled “online” chemistry within the WRF model. Atmos Environ 39:6957–6975CrossRefGoogle Scholar
  10. Guenther A, Karl T, Harley P, Wiedinmyer C, Palmer PI, Geron C (2006) Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature). Atmos Chem Phys 6:3181–3210CrossRefGoogle Scholar
  11. Hong SY, Noh Y, Dudhia J (2006) A new vertical diffusion package with an explicit treatment of entrainment processes. Mon Wea Rev 134:2318–2341CrossRefGoogle Scholar
  12. Im U, Bianconi R, Solazzo E, Kioutsioukis I, Badia A, Balzarini A, Baro R, Bellasio R, Brunner D, Chemel C, Curci G, Flemming J, Forkel R, Giordano L, Jimenez-Guerrero P, Hirtl M, Hodzic A, Honzak L, Jorba O, Knote C, Kuenen JJP, Makar PA, Manders-Groot A, Neal L, Perez JL, Pirovano G, Pouliot G, San Jose R, Savage N, Schroder W, Sokhi RS, Syrakov D, Torian A, Tuccella P, Werhahn K, Wolke R, Yahya K, Žabkar R, Zhang Y, Zhang J, Hogrefe C, Galmarini S (2015a) Evaluation of operational online-coupled regional air quality models over Europe and North America in the context of AQMEII phase 2. Part I: ozone. Atmos Environ 115:404–420CrossRefGoogle Scholar
  13. Im U, Bianconi R, Solazzo E, Kioutsioukis I, Badia A, Balzarini A, Baro R, Bellasio R, Brunner D, Chemel C, Curci G, Denier van der Gon HAC, Flemming J, Forkel R, Giordano L, Jimenez-Guerrero P, Hirtl M, Hodzic A, Honzak L, Jorba O, Knote C, Makar PA, Manders-Groot A, Neal L, Perez JL, Pirovano G, Pouliot G, San Jose R, Savage N, Schroder W, Sokhi RS, Syrakov D, Torian A, Tuccella P, Werhahn K, Wolke R, Yahya K, Žabkar R, Zhang Y, Zhang J, Hogrefe C, Galmarini S (2015b) Evaluation of operational online-coupled regional air quality models over Europe and North America in the context of AQMEII phase2. Part II: particulate matter. Atmos Environ 115:404–420CrossRefGoogle Scholar
  14. Kleinman LI, Daum PH, Imre DG, Lee JH, Lee YN, Nunnermacker LJ, Springston SR, Weinstein-Lloyd J, Newman L (2000) Ozone production in the New York City urban plume. J Geophys Res 105:14495–14512CrossRefGoogle Scholar
  15. Korsholm US, Baklanov A, Gross A, Mahura A, Sass BH, Kaas E (2008) Online coupled chemical weather forecast-ing based on HIRLAM – overview and prospective of Enviro-HIRLAM. HIRLAM Newsl 54:151–168Google Scholar
  16. Kuik F, Lauer A, Churkina G, Hugo AC, van der Gon D, Fenner D, Mar KA, Butler TM (2016) Air quality modelling in the Berlin-Brandenburg region using WRF-Chem v3.7.1: sensitivity to resolution of model grid and input data. Geosci Model Dev Discuss 9:4339–4363CrossRefGoogle Scholar
  17. Mahura A, Nuterman R, Gonzalez-Aparicio I, Amstrup B, Yang X, Baklanov A (2016) Meteorological and chemical urban scale modelling for Shanghai metropolitan area, Geophys Res Abstr, 18, EGU2016-1394, EGU General Assembly 2016, Vienna, AustriaGoogle Scholar
  18. Mahura A, Amstrup B, Nuterman R, Yang X, Baklanov A (2017) Multi-scale Enviro-HIRLAM forecasting of weather and atmospheric composition over China and its megacities, Geophys Res Abstr, 19, EGU2017-9564, EGU General Assembly 2017, Vienna, AustriaGoogle Scholar
  19. Meij DA, Bossioli E, Penard C, Vinuesa JF, Price I (2015) The effect of SRTM and Corine Land Cover data on calculated gas and PM10 concentrations in WRF-Chem. Atmos Environ 101:177–193CrossRefGoogle Scholar
  20. Misenis C, Zhang Y (2010) An examination of sensitivity of WRF/Chem predictions to physical parameterizations, horizontal grid spacing, and nesting options. Atmos Res 97:315–334CrossRefGoogle Scholar
  21. 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):16663–16682CrossRefGoogle Scholar
  22. Ritter M, Müller MD, Tsai MY, Parlow E (2013) Air pollution modeling over very complex terrain: an evaluation of WRF-Chem over Switzerland for two 1-year periods. Atmos Res 132–133:209–222CrossRefGoogle Scholar
  23. Sandu A, Sander R (2006) Technical note: simulating chemical systems in Fortran90 and Matlab with the kinetic preprocessor KPP-2.1. Atmos Chem Phys 6:187–195CrossRefGoogle Scholar
  24. Sandu A, Daescu D, Carmichael GR (2003) Direct and adjoint sensitivity analysis of chemical kinetic systems with KPP: part I-theory and software tools. Atmos Environ 37:5083–5096CrossRefGoogle Scholar
  25. Silibello C, D’Allura A, Finardi S, Bolignano A, Sozzi R (2015) Application of bias adjustment techniques to improve air quality forecasts. Atmos Pollut Res 6:928–938CrossRefGoogle Scholar
  26. Stockwell WR, Kirchner F, Kuhn M, Seefeld S (1997) A new mechanism for regional atmospheric chemistry modelling. J Geophys Res 102:847–879CrossRefGoogle Scholar
  27. Tie X, Brasseur G, Ying Y (2010) Impact of model resolution on chemical ozone formation in Mexico City: application of the WRF-Chem model. Atmos Chem Phys 10:8983–8995CrossRefGoogle Scholar
  28. Vogel B, Vogel H, Bäumer D, Bangert M, Lundgren K, Rinke R, Stanelle T (2009) The comprehensive model system COSMO-ART – radiative impact of aerosol on the state of the atmosphere on the regional scale. Atmos Chem Phys 9:8661–8680CrossRefGoogle Scholar
  29. Wolke R, Schröder W, Schrödner R, Renner E (2012) Influence of grid resolution and meteorological forcing on simulated European air quality: a sensitivity study with the modeling system COSMO-MUSCAT. Atmos Environ 53:110–130CrossRefGoogle Scholar
  30. Yahya K, Zhang Y, Vukovich JM (2014) Real-time air quality forecasting over the southeastern United States using WRF/Chem-MADRID: multiple-year assessment and sensitivity studies. Atmos Environ 92:318–338CrossRefGoogle Scholar
  31. Žabkar R, Honzak L, Skok G, Forkel R, Rakovec J, Ceglar A, Žagar N (2015) Evaluation of the high resolution WRF-Chem (v3.4.1) air quality forecast and its comparison with statistical ozone predictions. Geosci Model Dev 8:2119–2137CrossRefGoogle Scholar
  32. Zhang Y, Bocquet M, Mallet V, Seigneur C, Baklanov A (2012) Real-time air quality forecasting, part I: history, techniques, and current status. Atmos Environ 60:632–665CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Sciences, Department of PhysicsUniversity of Novi SadNovi SadSerbia

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