Skip to main content
Log in

One-day-ahead prediction of maximum carbon monoxide concentration in urban environments

  • Original Paper
  • Published:
Stochastic Environmental Research and Risk Assessment Aims and scope Submit manuscript

Abstract

The adverse health effects associated with exposure to CO range from the more subtle cardiovascular effects at low concentrations to death after acute or chronic exposure to higher concentrations. The forecasting of the daily CO maximum levels is therefore essential in every attempt for protecting and improving public health in urban areas. The objective of this work is to create a suite of statistical models for predicting the one-day-ahead maximum CO levels based on both the meteorological and the pollutant data recorded in six monitoring sites in the greater area of Athens, Greece. The meteorological variables used as input consist of hourly values of the surface air Temperature, the Relative Humidity, the Wind Speed and the Wind Direction, while the pollutant parameters consist of hourly concentrations of nitrogen oxide, nitric dioxide, ozone and sulfur dioxide, all corresponding to the 7-year-period between 2001 and 2007. The models were developed on a seasonal (warm vs. cold period) and hebdomadal (workdays vs. weekends) basis and revealed that the influence of the air pollution levels recorded one day before (day m−1) on the maximum CO concentrations of day m is quite variable and depends on the site/type of the station, the local meteorology and the emission sources. Additionally, the analysis revealed that the CO concentrations are influenced by both local and/or wider area CO sources, suggesting a strong persistence of the CO levels, while only local meteorology (e.g. in the vicinity of the station and especially during working days) plays a role in the formation of present day’s CO levels. The derived models were validated against an independent yearlong data set (2008) through the use of a classical set of validation parameters known as the Model Validation Kit. Indices assessing the ability of the models to predict the CO exceedances of the EC limit value were also used. On the whole, it was found that the prognostic models introduced here manage to predict the CO maximum daily values in a satisfactory level, with Pearson’s correlation coefficients ranging between 0.62 and 0.76 during the warm period and between 0.51 and 0.80 during the cold period of the year. Similarly the index of agreement ranges between 0.50–0.95 during the warm period and 0.57–0.81 during the cold period of the year, revealing a rather adequate model performance.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Alm S, Jantunen MJ, Vartiainen M (1999) Urban commuter exposure to particle matter and carbon monoxide inside an automobile. J Exposure Sci Environ Epidemiol 9:237–244

    Article  CAS  Google Scholar 

  • Barcenas O, Olivas E, Guerrero JD, Valls G, Rodriguez C, Tascon S (2005) Unbiased sensitivity analysis and pruning techniques in neural networks for surface ozone modeling. Ecol Modell 182:149–158

    Article  Google Scholar 

  • Basurko E, Berastegi G, Madariaga I (2006) Regression and multilayer perceptron-based models to forecast hourly O3 and NO2 levels in the Bilbao area. Environ Modell Softw 21:430–446

    Article  Google Scholar 

  • Burrows WR, Benjamin M, Beauchamp S, Lord ER, McCollor D, Thomson B (1995) CART decision-tree statistical analysis and prediction of summer season maximum surface ozone for the Vancouver, Montreal, and Atlantic regions of Canada. J Appl Meteorol 34(8):1848–1862

    Article  Google Scholar 

  • Chaloulakou A, Kassomenos P, Spyrellis N, Demokritou P, Koutrakis P (2003) Measurements of PM10 and PM2.5 particle concentrations in Athens. Greece Atmospheric Environ 37:649–660

    Article  CAS  Google Scholar 

  • Chelani A, Devotta S (2007) Prediction of ambient carbon monoxide concentration using nonlinear time series analysis technique. Transp Res Part D 12:596–600

    Article  Google Scholar 

  • Chien LC, Bangdiwala SI (2012) The implementation of Bayesian structural additive regression models in multi-city time series air pollution and human health studies. Stoch Environ Res Risk A. doi:10.1007/s0047701205624

    Google Scholar 

  • Comrie A, Diem J (1999) Climatology and forecast modeling of ambient carbon monoxide in Phoenix. Arizona Atmospheric Environ 33:5023–5036

    CAS  Google Scholar 

  • 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 of the European Union No. L152, Brussels, p 44

  • Grivas G, Chaloulakou A (2006) Artificial neural network models for prediction of PM10 hourly concentrations in the greater area of Athens. Greece Atmospheric Environ 40:1216–1229

    Article  CAS  Google Scholar 

  • Holloway T, Spak SN, Barker D, Bretl M, Moberg C, Hayhoe K, Van Dorn J, Wuebbles D (2008) Change in ozone air pollution over Chicago associated with global climate change. J Geophys Res. doi:10.1029/2007JD009775

    Google Scholar 

  • Karatzas K, Papadourakis G, Kyriakidis I (2009) Understanding and forecasting air pollution with the aid of artificial intelligence methods in Athens, Greece. In: Koutsojannis C, Sirmakessis S (eds) Tools and applications with artificial intelligence, studies in computational intelligence, vol 166. Springer, Berlin, pp 37–50

  • Kassomenos P (2005) Socioeconomic aspects in an extended contemporary city. Water Air Soil Pollut 162:315–329

    Article  CAS  Google Scholar 

  • Katsoulis BD, Kassomenos PA (2004) Assessment of the air quality over urban areas by means of biometeorological indices. The case of Athens. Greece Environ Technol 25:1293–1304

    CAS  Google Scholar 

  • Kukkonen J, Partanen L, Karpinen A, Ruuskanen J, Junninen H, Kolehmainen M, Niska H, Dorling ST, Chatterton T, Foxall R, Cawley G (2003) Extensive evaluation of neural network models for the prediction of NO2 and PM10 concentrations, compared with a deterministic modeling system and measurements in central Helsinki. Atmospheric Environ 37:4539–4550

    Article  CAS  Google Scholar 

  • Kumar U, Jain VK (2010) ARIMA forecasting of ambient air pollutants (O3, NO, NO2 and CO). Stoch Environ Res Risk A 24:751–760

    Article  Google Scholar 

  • Lalas D, Veirs VR, Karras G, Kallos G (1982) An analysis of the SO2 concentration in Athens. Greece Atmospheric Environ 16(3):531–544

    Article  CAS  Google Scholar 

  • Lioy PJ (1990) Assessing total human exposure to contaminants: a multidisciplinary approach. Environ Sci Technol 24:938–945

    Article  CAS  Google Scholar 

  • Martin ML, Turias IJ, Gonzalez FJ, Galindo PL, Trujillo FJ, Puntonet CG, Gorriz JM (2007) Prediction of CO maximum ground level concentrations in the Bay of Algeciras, Spain using artificial neural networks. Chemosphere 70:1190–1195

    Article  Google Scholar 

  • Martins DK, Stauffer RM, Thompson AM, Knepp TN, Pippin M (2012) Surface ozone at a coastal suburban site in 2009 and 2010: relationships to chemical and meteorological processes. J Geophys Res. doi:10.1029/2011JD016828

    Google Scholar 

  • Mott JA, Wolfe MI, Alverson CJ, Macdonald SC, Bailey CR, Ball LB, Moorman JE, Somers JH, Mannino DM, Redd SC (2002) National vehicle emissions policies and practices and declining US Carbon Monoxide-related mortality. J Am Med Assoc 288(8):955–988

    Article  Google Scholar 

  • Murena F, Ricciardi G (2004) CO residence times on urban roads in the Naples area using air quality monitoring data. Atmos Environ 39:1993–2001

    Google Scholar 

  • Olesen HR (1995) The model validation exercise at Mol: overview of results, workshop on operational short-range atmospheric dispersion models for environmental impact assessment in Europe. Int J Environ Pollut 5:761–784

    CAS  Google Scholar 

  • Ordieres JB, Vergara E, Capuz R, Salazar R (2005) Neural network prediction model for fine particulate matter (PM2.5) on the US–Mexico border in El Paso (Texas) and Ciudad Juarez (Chihuahua). Environ Modell Softw 20:547–559

    Article  Google Scholar 

  • Paschalidou AK (2009) Adaptation of the BOXURB model in a southeastern European Environment. The case of Athens. Environ Monit Assess 158(1–4):265–278

    Article  CAS  Google Scholar 

  • Paschalidou AK, Kassomenos PA (2004) Comparison of air pollutant concentrations between weekdays and weekends in Athens, Greece for various meteorological conditions. Environ Technol 25:1241–1255

    Article  CAS  Google Scholar 

  • Paschalidou AK, Kassomenos P, Bartzokas A (2009) A comparative study on various statistical techniques predicting ozone concentrations: implications to environmental management. Environ Monit Assess 148:277–289

    Article  CAS  Google Scholar 

  • Paschalidou AK, Karakitsios S, Kleanthous S, Kassomenos PA (2011) Forecasting hourly PM10 concentrations in Cyprus through artificial neural networks and multiple regression models: implications to local environmental management. Environ Sci Pollut Res 18:316–327

    Article  CAS  Google Scholar 

  • Perez P, Palacios R, Castillo A (2004) Carbon monoxide concentration forecasting in Santiago. Chile J Air Waste Manage Assoc 54:908–913

    Article  CAS  Google Scholar 

  • Raub JA, Mathieu-Nolf M, Hampson NB, Thom SR (2000) Carbon monoxide poisoning—a public health perspective. Toxicology 145(1):1–14

    Article  CAS  Google Scholar 

  • Saide P, Carmichael GR, Spak SN, Gallardo L, Osses AE, Mena-Carrasco MA, Pagowski M (2011) Forecasting urban PM10 and PM2.5 pollution episodes in very stable nocturnal conditions and complex terrain using WRF-Chem CO tracer model. Atmospheric Environ 45:2769–2780

    Article  CAS  Google Scholar 

  • Schlink U, Dorling St, Pelikan E, Nunnari G, Cawley G, Junninen H, Greig A, Foxall R, Eben K, Chatterton T, Vondracek J, Richter M, Dostal M, Bertucco, Kolehmainen M, Doyle M (2003) A rigorous inter- comparison of ground level ozone predictions. Atmos Environ 37:3237–3253

  • Schlink U, Herbarth O, Richter M, Dorling ST, Nunnari G, Cawley G, Pelikan E (2005) Statistical models to assess the health effects and to forecast ground level ozone. Environ Modell Softw 21:547–558

    Article  Google Scholar 

  • Slini Th, Karatzas K, Moussiopoulos N (2002) Statistical analysis of environmental data as the basis of forecasting: an air quality application. Sci Total Environ 288:227–237

    Article  CAS  Google Scholar 

  • Thomas S, Jacko RB (2007) Model for forecasting expressway fine particulate matter and carbon monoxide concentration: application of regression and neural network models. J Air Waste Manage Assoc 57(4):480–488

    Article  CAS  Google Scholar 

  • Vlachogianni A, Kassomenos P, Karppinen A, Karakitsios S, Kukkonen J (2011) Evaluation of a multiple regression model for the forecasting of the concentrations of NOx and PM10 in Athens and Helsinki. Sci Total Environ 409(8):1559–1571

    Article  CAS  Google Scholar 

  • Ziomas I, Melas D, Zerefos C, Paliatsos A (1995) Forecasting peak pollutant levels from meteorological variables. Atmospheric Environ 29:3703–3711

    Article  CAS  Google Scholar 

Download references

Acknowledgments

Authors would like to thank the Ministry of Environment that kindly offered the data used in this study.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to A. K. Paschalidou.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kassomenos, P.A., Paschalidou, A.K. & Vlachogianni, A. One-day-ahead prediction of maximum carbon monoxide concentration in urban environments. Stoch Environ Res Risk Assess 27, 561–572 (2013). https://doi.org/10.1007/s00477-012-0601-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00477-012-0601-1

Keywords

Navigation