1 research outputs found
Predictive Modelling of Air Quality in Madrid.
As global concerns about climate change and deteriorating air quality intensify, the European
Environment Agency (EEA) and other international organizations are making copious efforts to undo
the damage that so many human activities and industries have done to our ecosystems, especially to
the air we breathe.
The Barcelona Institute for Global Health (Instituto de Salud Global de Barcelona) annually
publishes a ranking that studies mortality attributable to air pollution in more than 1,000 European
cities. The Spanish capital, Madrid, leads the ranking associated with deaths caused by nitrogen
dioxide.
This end-of-degree dissertation provides a holistic assessment of Madrid City Council’s
current air quality system. It is demonstrated that this system is rather rudimentary and needs urgent
actualization. Not only is this air quality control system only composed of 24 static measurement
stations, but also, the data is vastly incomplete. Furthermore, two predictive models have been
developed (an ARIMA Time Series and an LSTM recurrent neural network) to study how time series
models adapt to this type of data. These models highlight the importance for Madrid’s City Council to
have a robust air quality control system. The results of both predictive models are used to make
recommendations to the City Council on improving its air quality system. A stronger air quality
system will allow Madrid’s City Council to act proactively in reducing pollution and making efficient
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