Modelling air quality levels of regulated metals: limitations and challenges


Toxic metals as arsenic (As), cadmium (Cd), nickel (Ni), and lead (Pb) exist in the atmosphere as particulate matter components. Their concentration levels in the European Union (EU) are regulated by European legislation, which sets target and limit values as annual means, and by the World Health Organization (WHO) that defines guidelines and reference values for those metal elements. Modelling tools are recommended to support air quality assessment regarding the toxic metals; however, few studies have been performed and those assessments rely on discrete measurements or field campaigns. This study aims to evaluate the capability of air quality modelling tools to verify the legislation compliance concerning the atmospheric levels of toxic elements and to identify the main challenges and limitations of using a modelling assessment approach for regulatory purposes, as a complement to monitoring. The CAMx air quality model was adapted and applied over Porto and Lisbon urban regions in Portugal at 5 × 5-km2 and 1 × 1-km2 horizontal resolution for the year 2015, and the results were analysed and compared with the few measurements available in three locations. The comparison between modelled and measured data revealed an overestimation of the model, although annual averages are much lower than the regulated standards. The comparison of the 5-km and 1-km resolutions’ results indicates that a higher resolution does not necessarily imply a better performance, pointing out uncertainties in emissions and the need to better describe the magnitude and spatial allocation of toxic metal emissions. This work highlighted that an increase of the spatial and temporal coverage of monitoring sites would allow to improve the model design, contribute to a better knowledge on toxic metals atmospheric emission sources and to increase the capacity of models to simulate atmospheric particulate species of health concern.

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  1. Adani M, Mircea M, D’Isidoro M, Costa MP, Silibello C (2015) Heavy metal modelling study over Italy: effects of grid resolution, lateral boundary conditions and foreign emissions on air concentrations. Water Air Soil Pollut 226.

  2. Albuquerque M, Coutinho M, Rodrigues J, Ginja J, Borrego C (2017) Long-term monitoring of trace metals in PM10 and total gaseous mercury in the atmosphere of Porto, Portugal. Atmos Pollut Res 8:535–544.

    Article  Google Scholar 

  3. Borrego C, Tchepel O, Barros N, Miranda AI (2000) Impact of road traffic emissions on air quality of the Lisbon region. Atmos Environ 34:4683–4690.

    CAS  Article  Google Scholar 

  4. Borrego C, Tchepel O, Costa AM, Amorim JH, Miranda AI (2003) Emission and dispersion modelling of Lisbon air quality at local scale. Atmos Environ 37:5197–5205.

    CAS  Article  Google Scholar 

  5. Borrego C, Tchepel O, Salmin L et al (2004) Integrated modeling of road traffic emissions: application to Lisbon air quality management. Cybern Syst 35:535–548.

    Article  Google Scholar 

  6. Carvalho D, Rocha A, Gómez-Gesteira M, Santos C (2012) A sensitivity study of the WRF model in wind simulation for an area of high wind energy. Environ Model Softw 33:23–34.

    Article  Google Scholar 

  7. 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 129:587–604.<0587:CAALSH>2.0.CO;2

    Article  Google Scholar 

  8. Costa S, Ferreira J, Silveira C, Costa C, Lopes D, Relvas H, Borrego C, Roebeling P, Miranda AI, Paulo Teixeira J (2014) Integrating health on air quality assessment—review report on health risks of two major European outdoor air pollutants: PM and NO 2. J Toxicol Environ Heal Part B 17:307–340.

    CAS  Article  Google Scholar 

  9. DGT (Direção Geral do Território) (2018) Cartografia de Uso e Ocupação do Solo (COS, CLC e Copernicus). Accessed 19 Oct 2018

  10. Dockery DW, Schwartz J, Spengler JD (1992) Air pollution and daily mortality: associations with particulates and acid aerosols. Environ Res 59:362–373.

    CAS  Article  Google Scholar 

  11. Dudhia J (1989) Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J Atmos Sci 46:3077–3107.<3077:NSOCOD>2.0.CO;2

    Article  Google Scholar 

  12. EEA (European Environmental Agency) (2019) Air quality in Europe - 2019 report

  13. ENVIRON (2016) CAMx User’s Guide Version 6.40 Comprehensive Air quality Model with extensions.

  14. Gong SL (2003) A parameterization of sea-salt aerosol source function for sub- and super-micron particles. Glob Biogeochem Cycles 17.

  15. González MA, Vivanco M (2015, 27) Modelling the fine and coarse fraction of Pb , Cd , As and Ni air concentration in Spain. Física la Tierra:11–34.

  16. González MÁ, Vivanco MG, Palomino I, Garrido JL, Santiago M, Bessagnet B (2012) Modelling some heavy metals air concentration in europe. Water Air Soil Pollut 223:5227–5242.

    CAS  Article  Google Scholar 

  17. Grell GA, Freitas SR (2014) A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling. Atmos Chem Phys 14:5233–5250.

    CAS  Article  Google Scholar 

  18. Hong S, Lim J (2006) The WRF single-moment 6-class microphysics scheme (WSM6). J Korean Meteorol Soc 42:129–151

    Google Scholar 

  19. Hong S, Noh Y, Dudhia J (2006) A new vertical diffusion package with an explicit treatment of entrainment processes. Mon Weather Rev 134:2318–2341.

    Article  Google Scholar 

  20. Hutzell WT, Luecken DJ (2008) Fate and transport of emissions for several trace metals over the United States. Sci Total Environ 396:164–179.

    CAS  Article  Google Scholar 

  21. Marshall JD, Granvold PW, Hoats AS, McKone TE, Deakin E, W Nazaroff W (2006) Inhalation intake of ambient air pollution in California’s South Coast Air Basin. Atmos Environ 40:4381–4392.

    CAS  Article  Google Scholar 

  22. Mircea M, Silibello C, Calori G (2013) A study of heavy metals pollution in Italy with the atmospheric modelling system of the MINNI project. In: E3S Web of Conferences

  23. Miri M, Allahabadi A, Ghaffari HR, Fathabadi ZA, Raisi Z, Rezai M, Aval MY (2016) Ecological risk assessment of heavy metal (HM) pollution in the ambient air using a new bio-indicator. Environ Sci Pollut Res 23:14210–14220.

    CAS  Article  Google Scholar 

  24. 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 102:16663–16682.

    CAS  Article  Google Scholar 

  25. Mohanraj R, Azeez PA, Priscilla T (2004) Heavy metals in airborne particulate matter of urban Coimbatore. Arch Environ Contam Toxicol 47:162–167.

    CAS  Article  Google Scholar 

  26. Monteiro A, Ferreira J, Ribeiro I, Fernandes AP, Martins H, Gama C, Miranda AI (2015) Air quality over Portugal in 2020. Atmos Pollut Res 6:788–796.

    CAS  Article  Google Scholar 

  27. NCAR (2010) Model for ozone and related chemical tracers, version 4 (MOZART-4). In: Natl. Cent. Atmos. Res

  28. Ntziachristos L, Ning Z, Geller MD, Sheesley RJ, Schauer JJ, Sioutas C (2007) Fine, ultrafine and nanoparticle trace element compositions near a major freeway with a high heavy-duty diesel fraction. Atmos Environ 41:5684–5696.

    CAS  Article  Google Scholar 

  29. OpenStreetMap contributors (2017) Planet dump [Data file from $date of database dump$]. Retrieved from

  30. Ovadnevaite J, Manders A, De Leeuw G et al (2014) A sea spray aerosol flux parameterization encapsulating wave state. Atmos Chem Phys 14:1837–1852.

    CAS  Article  Google Scholar 

  31. Rizzio E, Giaveri G, Arginelli D, Gini L, Profumo A, Gallorini M (1999) Trace elements total content and particle sizes distribution in the air particulate matter of a rural-residential area in North Italy investigated by instrumental neutron activation analysis. Sci Total Environ 226:47–56.

    CAS  Article  Google Scholar 

  32. Russo MA, Leitão J, Gama C, Ferreira J, Monteiro A (2018) Shipping emissions over Europe: a state-of-the-art and comparative analysis. Atmos Environ 177:187–194.

    CAS  Article  Google Scholar 

  33. Sá E, Martins H, Ferreira J, Marta-Almeida M, Rocha A, Carvalho A, Freitas S, Borrego C (2016) Climate change and pollutant emissions impacts on air quality in 2050 over Portugal. Atmos Environ 131:209–224.

    CAS  Article  Google Scholar 

  34. Seinfeld JH, Pandis SN (2006) Atmospheric chemistry and physics: from air pollution to climate change

  35. Silveira C, Ferreira J, Monteiro A et al (2017) Emissions from residential combustion sector: how to build a high spatially resolved inventory. Air Qual Atmos Heal 11:1–12.

    CAS  Article  Google Scholar 

  36. Skamarock WC, Klemp JB, Dudhi J, et al (2008) A description of the advanced research WRF version 3

  37. Slinn SA, Slinn WGN (1980) Predictions for particle deposition on natural waters. Atmos Environ 14:1013–1016.

    Article  Google Scholar 

  38. Vivanco MG, Gonzalez MA, Palomino I, et al (2011) Modelling arsenic, lead, cadmium and nickel ambient air concentrations in Spain. Proc - 2011 Int Conf Comput Sci Its Appl ICCSA 2011 243–246. doi:

  39. Wang X, Wei W, Cheng S, Li J, Zhang H, Lv Z (2018) Characteristics and classification of PM2.5pollution episodes in Beijing from 2013 to 2015. Sci Total Environ 612:170–179.

    CAS  Article  Google Scholar 

  40. Zhang D, Anthes RA (1982) A high-resolution model of the planetary boundary layer - sensitivity tests and comparisons with SESAME-79 data. J Appl Meteorol 21:1594–1609.<1594:AHRMOT>2.0.CO;2

    Article  Google Scholar 

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This work was supported by the European Union’s LIFE Programme in the framework of the Index-Air LIFE15 ENV/PT/000674 project. This work reflects only the authors’ view and EASME is not responsible for any use that may be made of the information it contains. The project FUTURAR (PTDC/AAG-MAA/2569/2014-POCI-01-0145-FEDER-016752) was funded by FEDER, through COMPETE2020-Programa Operacional Competitividade e Internacionalização (POCI), and by national funds (OE), through FCT/MCTES. The financial support was from CESAM (UID/AMB/50017/2019+UIDB/50017/2020) and C2TN (UID/Multi/04349/2019), to FCT/MCTES through national funds, and the co-funding by the FEDER, within the PT2020 Partnership Agreement and Compete 2020. J. Ferreira is funded by national funds (OE), through FCT–Fundação para a Ciência e Tecnologia, I.P., in the scope of the framework contract foreseen in the numbers 4, 5 and 6 of the article 23, of the Decree-Law 57/2016, of August 29, changed by Law 57/2017, of July 19.

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Ferreira, J., Lopes, D., Rafael, S. et al. Modelling air quality levels of regulated metals: limitations and challenges. Environ Sci Pollut Res (2020).

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  • Toxic metals
  • Air quality guidelines
  • Modelling
  • Monitoring