Advertisement

uAQE: Urban Air Quality Evaluator

  • Claudio RossiEmail author
  • Alessandro Farasin
  • Giacomo Falcone
  • Carlotta Castelluccio
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11912)

Abstract

Knowing the amount of air pollutants in our cities is of great importance to help decision-makers in the definition of effective strategies aimed at maintaining a good air quality, which is a key factor for a healthy life, especially in urban environments. Using a data set from a big metropolitan city, we realize the uAQE: urban Air Quality Evaluator, which is a supervised machine learning model able to estimate air pollutants values using only weather and traffic data. We evaluate the performance of our solution by comparing the predicted pollutant values with the real measurements provided by professional air monitoring stations. We use the predicted pollutants to compute a standard Air Quality Index (AQI) and we map it into a set of five qualitative AQI classes, which can be used for decision making at the city level. uAQE is able to predict the AQI class value with an accuracy of 0.8.

Keywords

Air quality Environment Weather Traffic 

References

  1. 1.
    Amos, J.: Polluted air cause 5.5 million deaths a year new research says. BBC NEWS, Science and Environment (2016)Google Scholar
  2. 2.
    Zheng, Y., Liu, F., Hsieh, H.: U-air: When Urban Air Quality Inference Meets Big Data. In: Microsoft Research Asia. ACM (2013)Google Scholar
  3. 3.
    Juhosa, I., Makrab, L., Ttha, B.: Forecasting of traffic origin NO and NO2 concentrations by support vector machines and neural networks using principal component analysis. Simul. Model. Pract. Theory 16(9), 1488–1502 (2008)CrossRefGoogle Scholar
  4. 4.
    Berkowicz, R., Palmgren, F., Hertel, O., Vignati, E.: A study on effects of weather, vehicular traffic and other sources of particulate air pollution on the city of Delhi for the year 2015. J. Environ. Pollut. Hum. Health 4(2), 24–41 (2016)Google Scholar
  5. 5.
    Gopalaswami, R.: Using measurements of air pollution in streets for evaluation of urban air quality meterological analysis and model calculations. Sci. Total Environ. 189–190, 259–265 (1996)Google Scholar
  6. 6.
    Burden, F., Winkler, D.: Bayesian Regularitazion of Neural Networks. PubMed (2008)Google Scholar
  7. 7.
    Stergiou, C., Siganos, D.: Neural networks. Imperial College London (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Claudio Rossi
    • 1
    Email author
  • Alessandro Farasin
    • 1
    • 2
  • Giacomo Falcone
    • 1
  • Carlotta Castelluccio
    • 3
  1. 1.LINKS FoundationTurinItaly
  2. 2.Polytechnic of TurinTurinItaly
  3. 3.MicrosoftMilanItaly

Personalised recommendations