Skip to main content

Air quality modeling in the Oviedo urban area (NW Spain) by using multivariate adaptive regression splines

Abstract

The aim of this research work is to build a regression model of air quality by using the multivariate adaptive regression splines (MARS) technique in the Oviedo urban area (northern Spain) at a local scale. To accomplish the objective of this study, the experimental data set made up of nitrogen oxides (NO x ), carbon monoxide (CO), sulfur dioxide (SO2), ozone (O3), and dust (PM10) was collected over 3 years (2006–2008). The US National Ambient Air Quality Standards (NAAQS) establishes the limit values of the main pollutants in the atmosphere in order to ensure the health of healthy people. Firstly, this MARS regression model captures the main perception of statistical learning theory in order to obtain a good prediction of the dependence among the main pollutants in the Oviedo urban area. Secondly, the main advantages of MARS are its capacity to produce simple, easy-to-interpret models, its ability to estimate the contributions of the input variables, and its computational efficiency. Finally, on the basis of these numerical calculations, using the MARS technique, conclusions of this research work are exposed.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

References

  • Akkoyunku A, Ertürk FA (2003) Evaluation of air pollution trends in Istanbul. Int J Environ Pollut 18:388–398

    Article  Google Scholar 

  • Anderson HR (2009) Air pollution and mortality: a history. Atmos Environ 43(1):142–152

    Article  CAS  Google Scholar 

  • Anderson W, Prescott GJ, Packham S, Mullins J, Brookes M, Seaton A (2001) Asthma admissions and thunderstorms: a study of pollen, fungal spores, rainfall, and ozone. Q J Med 94(8):429–433

    Article  CAS  Google Scholar 

  • Bishop CM (2006) Pattern recognition and machine learning. Springer, New York

    Google Scholar 

  • Boznar M, Lesjack M, Mlakar P (1993) A neural network based method for short-term predictions of ambient SO2 concentrations in highly polluted industrial areas of complex terrain. Atmos Environ 270:221–230

    Article  Google Scholar 

  • Brimblecombe P (2011) Air pollution episodes. Enc Environ Health 39–45

  • Chaloulakou A, Saisana M, Spyrellis N (2003) Comparative assessment of neural networks and regression models for forecasting summertime ozone in Athens. Sci Total Environ 313:1–13

    Article  CAS  Google Scholar 

  • Chou S–M, Lee T–S, Shao YE, Chen I–F (2004) Mining the breast cancer pattern using artificial neural networks and multivariate adaptive regression splines. Expert Syst Appl 27(1):133–142

    Article  Google Scholar 

  • Colbeck I (2008) Environmental chemistry of aerosol. Wiley, New York

    Book  Google Scholar 

  • Comrie AC, Diem JE (1999) Climatology and forecast modeling of ambient carbon monoxide in Phoenix. Atmos Environ 33:5023–5036

    Article  CAS  Google Scholar 

  • Cooper CD, Alley FC (2002) Air pollution control. Waveland Press, New York

    Google Scholar 

  • de Cos Juez FJ, Sánchez Lasheras F, García Nieto PJ, Suárez Suárez MA (2009) A new data mining methodology applied to the modelling of the influence of diet and lifestyle on the value of bone mineral density in post-menopausal women. Int J Comput Math 86(10):1878–1887

    Article  Google Scholar 

  • Domike JR, Zacaroli AC (2013) The Clean Air Act handbook. American Bar Association, Washington

    Google Scholar 

  • Efron B, Tibshirani R (1997) Improvements on cross-validation: the .632+ bootstrap method. J Am Stat Assoc 92(438):548–560

    Google Scholar 

  • Elbir T, Muezzinoglu A, Bayram A (2000) Evaluation of some air pollution indicators in Turkey. Environ Int 26(1–2):5–10

  • Freedman D, Pisani R, Purves R (2007) Statistics. W.W. Norton & Company, New York

    Google Scholar 

  • Friedlander SK (2000) Smoke, dust and haze: fundamentals of aerosol dynamics. Oxford University Press, New York

    Google Scholar 

  • Friedman JH (1991) Multivariate adaptive regression splines. Ann Stat 19:1–141

    Article  Google Scholar 

  • Friedman JH, Roosen CB (1995) An introduction to multivariate adaptive regression splines. Stat Methods Med Res 4:197–217

    Article  CAS  Google Scholar 

  • García Nieto PJ (2001) Parametric study of selective removal of atmospheric aerosol by coagulation, condensation and gravitational settling. Int J Environ Heal R 11:151–162

    Article  Google Scholar 

  • García Nieto PJ (2006) Study of the evolution of aerosol emissions from coal-fired power plants due to coagulation, condensation, and gravitational settling and health impact. J Environ Manag 79(4):372–382

    Article  Google Scholar 

  • García Nieto PJ, Sánchez Lasheras F, de Cos Juez FJ, Alonso Fernández JR (2011) Study of cyanotoxins presence from experimental cyanobacteria concentrations using a new data mining methodology based on multivariate adaptive regression splines in Trasona reservoir (Northern Spain). J Hazard Mater 195:414–421

    Article  Google Scholar 

  • García Nieto PJ, Alonso Fernández JR, Sánchez Lasheras F, de Cos Juez FJ, Díaz Muñiz C (2012) A new improved study of cyanotoxins presence from experimental cyanobacteria concentrations in the Trasona reservoir (northern Spain) using the MARS technique. Sci Total Environ 430:88–92

    Article  Google Scholar 

  • García Nieto PJ, Combarro EF, del Coz Díaz JJ, Montañés E (2013) A SVM-based regression model to study the air quality at local scale in Oviedo urban area (northern Spain): a case study. Appl Math Comput 219(17):8923–8937

    Article  Google Scholar 

  • Gardner MW, Dorling SR (1999) Neural network modelling and prediction of hourly NO x and NO2 concentrations in urban air in London. Atmos Environ 33(5):709–719

    Article  CAS  Google Scholar 

  • Godish T (2004) Air quality. Lewis Publishers, Boca Raton

    Google Scholar 

  • Hastie T, Tibshirani R, Friedman J (2003) The elements of statistical learning. Springer, New York

    Google Scholar 

  • Haykin S (1999) Neural networks, comprehensive foundation. Prentice Hall, New Jersey

    Google Scholar 

  • Hewitt CN, Jackson AV (2009) Atmospheric science for environmental scientists. Wiley, New York

    Google Scholar 

  • Hooyberghs J, Mensink C, Dumont D, Fierens F, Brasseur O (2005) A neural network forecast for daily average PM10 concentrations in Belgium. Atmos Environ 39(18):3279–3289

    Article  CAS  Google Scholar 

  • James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning: with applications in R. Springer, New York

    Book  Google Scholar 

  • Jerrett M, Burnett RT, Arden Pope C III, Ito K, Thurston G, Krewski D, Shi Y, Calle E, Thun M (2009) Long-term ozone exposure and mortality. New Engl J Med 360(11):1085–1095

    Article  CAS  Google Scholar 

  • Karaca F, Alagha O, Ertürk F (2005) Statistical characterization of atmospheric PM10 and PM2.5 concentrations at a non-impacted suburban site of Istanbul, Turkey. Chemosphere 59(8):1183–1190

    Article  CAS  Google Scholar 

  • Karaca F, Nikov A, Alagha O (2006) NN-AirPol: a neural-network-based method for air pollution evaluation and control. Int J Environ Pollut 28(3–4):310–325

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  • Lantz B (2013) Machine learning with R. Packt Publishing, Birmingham

    Google Scholar 

  • Lucking AJ, Lundback M, Mills NL, Faratian D, Barath SL, Pourazar J, Cassee FR, Donaldson K, Boon NA, Badimon JJ, Sandstrom T, Blomberg A, Newby DE (2008) Diesel exhaust inhalation increases thrombus formation in man. Eur Heart J 29(24):3043–3051

    Article  CAS  Google Scholar 

  • Lutgens FK, Tarbuck EJ (2012) The atmosphere: an introduction to meteorology. Prentice Hall, New York

    Google Scholar 

  • Monteiro A, Lopes M, Miranda AI, Borrego C, Vautard R (2005) Air pollution forecast in Portugal: a demand from the new air quality framework directive. Int J Environ Pollut 5:1–9

    Google Scholar 

  • Phalen RN (2011) Introduction to air pollution science. Jones & Bartlett Learning, Burlington

    Google Scholar 

  • Picard R, Cook D (1984) Cross-validation of regression models. J Am Stat Assoc 79(387):575–584

    Article  Google Scholar 

  • Schnelle KB, Brown CA (2001) Air pollution control technology handbook. CRC Press, Boca Raton

    Book  Google Scholar 

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

    Google Scholar 

  • Sekulic SS, Kowalski BR (1992) MARS: a tutorial. J Chemometr 6:199–216

    Article  CAS  Google Scholar 

  • Singal SP (2012) Air quality monitoring and control strategy. Alpha Science International, Oxford

    Google Scholar 

  • Suárez Sánchez A, García Nieto PJ, Riesgo Fernández P, del Coz Díaz JJ, Iglesias-Rodríguez FJ (2011) Application of a SVM-based regression model to the air quality study at local scale in the Avilés urban area (Spain). Math Comput Model 54(5–6):1453–1466

    Article  Google Scholar 

  • Törnqvist HK, Mills NL, Gonzalez M, Miller MR, Robinson SD, Megson IL, MacNee W, Donaldson K, Söderberg S, Newby DE, Sandström T, Blomberg A (2007) Persistent endothelial dysfunction in humans after diesel exhaust inhalation. Am J Resp Crit Care Med 176(4):395–400

    Article  Google Scholar 

  • Vapnik V (1999) The nature of statistical learning theory. Springer, New York

    Google Scholar 

  • Vidoli F (2011) Evaluating the water sector in Italy through a two stage method using the conditional robust nonparametric frontier and multivariate adaptive regression splines. Eur J Oper Res 212(13):583–595

    Article  Google Scholar 

  • Vincent JH (2007) Aerosol sampling: science, standards, instrumentation and applications. Wiley, Chichester, England

    Book  Google Scholar 

  • Wang LK, Pereira NC, Hung YT (2004) Air pollution control engineering. Humana Press, New York

    Book  Google Scholar 

  • Wark K, Warner CF, Davis WT (1997) Air pollution: its origin and control. Prentice Hall, New York

    Google Scholar 

  • Weinhold B (2008) Ozone nation: EPA standard panned by the people. Environ Health Persp 116(7):A302–A305

    Article  Google Scholar 

  • Xu QS, Daszykowski M, Walczak B, Daeyaert F, de Jonge MR, Heeres J, Koymans LMH, Lewi PJ, Vinkers HM, Janssen PA, Massart DL (2004) Multivariate adaptive regression splines—studies of HIV reverse transcriptase inhibitors. Chemometr Intell Lab 72(1):27–34

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The authors wish to acknowledge the computational support provided by the Department of Mathematics at the University of Oviedo. Additionally, this paper has been funded by the Government of the Principality of Asturias through funds from the Programme of Science, Technology and Innovation (PCTI) of Asturias 2006–2009, cofinanced by 80 % within the priority Focus 1 of the Operational Programme FEDER of the Principality of Asturias 2007–2013 (research project FC–11–PC10–19). Finally, we would like to thank Anthony Ashworth for his revision of English grammar and spelling of the manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to P. J. García Nieto.

Additional information

Responsible editor: Michael Matthies

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Nieto, P.J.G., Antón, J.C.Á., Vilán, J.A.V. et al. Air quality modeling in the Oviedo urban area (NW Spain) by using multivariate adaptive regression splines. Environ Sci Pollut Res 22, 6642–6659 (2015). https://doi.org/10.1007/s11356-014-3800-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11356-014-3800-0

Keywords

  • Air quality modeling
  • Air monitoring data
  • Statistical machine learning
  • Pollutant substances
  • Atmospheric fate
  • Multivariate adaptive regression splines (MARS)