Advertisement

Computational Intelligence for Evaluating the Air Quality in the Center of Madrid, Spain

  • Jamal ToutouhEmail author
  • Irene Lebrusán
  • Sergio Nesmachnow
Conference paper
  • 47 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1173)

Abstract

This article presents the application of data analysis and computational intelligence techniques for evaluating the air quality in the center of Madrid, Spain. Polynomial regression and deep learning methods to analyze the time series of nitrogen dioxide concentration, in order to evaluate the effectiveness of Madrid Central, a set of road traffic limitation measures applied in downtown Madrid. According to the reported results, Madrid Central was able to significantly reduce the nitrogen dioxide concentration, thus effectively improving air quality.

Keywords

Smart cities Air pollution Computational intelligence 

Notes

Acknowledgements

I. Lebrusán has been partially funded by RCC Harvard program. J. Toutouh has been partially funded by EU’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 799078; and by the Spanish MINECO and FEDER projects TIN2017-88213-R, RTI2018-100754-B-I00, and UMA18-FEDERJA-003.

References

  1. 1.
    Barrionuevo, J., Berrone, P., Ricart, J.: Smart cities, sustainable progress. IESE Insight 14(14), 50–57 (2012)CrossRefGoogle Scholar
  2. 2.
    Camero, A., Toutouh, J., Stolfi, D.H., Alba, E.: Evolutionary deep learning for car park occupancy prediction in smart cities. In: Battiti, R., Brunato, M., Kotsireas, I., Pardalos, P.M. (eds.) LION 12 2018. LNCS, vol. 11353, pp. 386–401. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-05348-2_32CrossRefGoogle Scholar
  3. 3.
    Cantillo, V., Ortúzar, J.: Restricting the use of cars by license plate numbers: a misguided urban transport policy. DYNA, 81(188), 75–82 (2014)CrossRefGoogle Scholar
  4. 4.
    European Commission: Directive 2004/107/EC of the European Parliament and of the Council of 15 December 2004 relating to arsenic, cadmium, mercury, nickel and polycyclic aromatic hydrocarbons in ambient air. Official Journal of the European Union 23, 3–16 (2004)Google Scholar
  5. 5.
    European Commission: 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 152, 1–44 (2008)Google Scholar
  6. 6.
    European Environment Agency: Air quality in Europe: 2019 report. https://www.eea.europa.eu/publications/air-quality-in-europe-2019. Accessed 11 Nov 2019
  7. 7.
    Fabbiani, E., Nesmachnow, S., Toutouh, J., Tchernykh, A., Avetisyan, A., Radchenko, G.: Analysis of mobility patterns for public transportation and bus stops relocation. Program. Comput. Softw. 44(6), 508–525 (2018)CrossRefGoogle Scholar
  8. 8.
    Foggin, S.: Does restricting the use of private vehicles tackle Medellín’s pollution problem? Latin America Reports. https://latinamericareports.com/does-restricting-the-use-of-private-vehicles-tackle-medellins-pollution-problem/1567/. Accessed 1 Nov 2019
  9. 9.
    Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. The MIT Press, Cambridge (2016)zbMATHGoogle Scholar
  10. 10.
    Honarvar, A., Sami, A.: Towards sustainable smart city by particulate matter prediction using urban big data, excluding expensive air pollution infrastructures. Big Data Res. 17, 56–65 (2019)CrossRefGoogle Scholar
  11. 11.
    Lebrusán, I., Toutouh, J.: Assessing the environmental impact of car restrictions policies: Madrid Central case. In: Nesmachnow, S., Hernández Callejo, L. (eds.) ICSC-CITIES 2019. CCIS, vol. 1152, pp. 9–24. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-38889-8_2CrossRefGoogle Scholar
  12. 12.
    Li, J., Li, X.B., Li, B., Peng, Z.R.: The effect of nonlocal vehicle restriction policy on air quality in Shanghai. Atmosphere 9(8), 299 (2018)CrossRefGoogle Scholar
  13. 13.
    Liguang, F., Haozhi, Z., Yulin, J., Zhaorong, W.: Evaluation on the effect of car use restriction measures in Beijing. In: 51st Annual Transportation Research Forum, pp. 1–10 (2010)Google Scholar
  14. 14.
    Liu, Y., Yan, Z., Liu, S., Wu, Y., Gan, Q., Dong, C.: The effect of the driving restriction policy on public health in Beijing. Nat. Hazards 85(2), 751–762 (2016)CrossRefGoogle Scholar
  15. 15.
    Massobrio, R., Nesmachnow, S.: Urban data analysis for the public transportation systems of Montevideo, Uruguay. In: Nesmachnow, S., Hernández Callejo, L. (eds.) ICSC-CITIES 2019. CCIS, vol. 1152, pp. 199–214. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-38889-8_16CrossRefGoogle Scholar
  16. 16.
  17. 17.
    Orłowski, A., Marć, M., Namieśnik, J., Tobiszewski, M.: Assessment and optimization of air monitoring network for smart cities with multicriteria decision analysis. In: Nguyen, N.T., Tojo, S., Nguyen, L.M., Trawiński, B. (eds.) ACIIDS 2017. LNCS (LNAI), vol. 10192, pp. 531–538. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-54430-4_51CrossRefGoogle Scholar
  18. 18.
    O’Ryan, R., Martinez, F., Larraguibel, L.: A neural networks approach to evaluating urban policies: the case of Santiago, Chile. WIT Trans. Built Environ. 23, 127–139 (1996)Google Scholar
  19. 19.
    Reuters: Paris bans up to 60% of its cars as heatwave worsens pollution. https://www.reuters.com/article/us-france-pollution/paris-bans-up-to-60-of-its-cars-as-heatwave-worsens-pollution. 10 Nov 2019
  20. 20.
    Soni, N., Soni, N.: Benefits of pedestrianization and warrants to pedestrianize an area. Land Use Policy 57, 139–150 (2016)CrossRefGoogle Scholar
  21. 21.
    Stevenson, M., et al.: Land use, transport, and population health: estimating the health benefits of compact cities. Lancet 388(10062), 2925–2935 (2016)CrossRefGoogle Scholar
  22. 22.
    Viard, V., Fu, S.: The effect of Beijing’s driving restrictions on pollution and economic activity. J. Public Econ. 125, 98–115 (2015)CrossRefGoogle Scholar
  23. 23.
    Wang, L., Xu, J., Zheng, X., Qin, P.: Will a driving restriction policy reduce car trips? A case study of Beijing, China. Transp. Res. Part A: Policy Pract. 67, 279–290 (2014) Google Scholar
  24. 24.
    WHO: Mortality and burden of disease from ambient air pollution (2017). https://doi.org/10.1787/9789264257474-en. Accessed 11 Nov 2019
  25. 25.
    Zhang, L., Long, R., Chen, H.: Do car restriction policies effectively promote the development of public transport? World Dev. 119, 100–110 (2019)CrossRefGoogle Scholar
  26. 26.
    Zheng, X., et al.: Big data for social transportation. IEEE Trans. Intell. Transp. Syst. 17(3), 620–630 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.CSAILMassachusetts Institute of TechnologyCambridgeUSA
  2. 2.IGLPHarvard UniversityCambridgeUSA
  3. 3.Universidad de la RepúblicaMontevideoUruguay

Personalised recommendations