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Exploration of Madrid’s pollution levels during the COVID-19 lockdown in Spain.

  • Studied NO2 levels across all air quality control stations in Madrid city to see the effect of lockdown in each station.
  • The average impact detected was a reduction in NO2 levels of the 53%.
  • Used weather and temporal data to study the impact of each variable in NO2 levels.
  • Used spatialisation of stations to observe differences and patterns with PCA.
  • Built a linear model for each station, month and day of the week that predicted NO2 levels (RMSE ~ 6.7 µg/m3)
  • Detected stations with a higher impact because of lockdown. Stations in the north of Madrid were more affected than station in the south. The highest impact was observed in the station located at Madrid's airport (~70%), and the lowest impact in the station located at Villa de Vallecas (~20%).
  • The results of this project could be used to improve Madrid's air quality by looking at the effect of mobility restrictions around the area of each station.

Packages

Python Version: 3.8.3

Packages used: pandas, numpy, scipy, statsmodels, scikit-learn, matplotlib, seaborn, folium

Requirements: pip install -r requirements.txt

Source of data

The data was downloaded from Madrid City Council's open data portal. Pollution levels hourly data and hourly weather data.

Data cleaning

The following changes were made to the original data:

  • For each type of data (air quality and weather):
    • Converted row time series to column time series to better handle de data.
    • In the air quality data, selected only the NO2 pollutant, that was measured in all stations.
    • Selected only verified instances.
    • Merged both datasets by date and time.
  • Repeated the process for the months of March, April, May and Jun for 2019 and 2020.
  • Merged all the months together.
  • Added a "Year" column, a "Day of the week" column and a "Lockdown" column.
  • Removed instances with incorrect data (one of the instances contained a temperature below 30ºC)
  • Evaluated Mahalanobis distance of each instance and removed outliers.

The final data contained 96.2% of the original data.

EDA

The temporal evolution of NO2 levels looked like this:

And the correlation coefficients were the following (the second heatmap shows correlation per station with NO2 levels):

A map of the locations of the stations was saved as an HTML file. The colours of the stations are obtained with spatialisation.

Model building

The log of the NO2 levels was used to model the linear regression. The log of wind fitted better the values than wind.

  • The first model used only Wind, Pressure, Temperature, Humidity and Lockdown as variables. RMSE ~ 9.57 µg/m3
  • After modelling each month and day of the week separately, used a Fourier transformation to obtain the periodicity of the peaks in hourly NO2 levels. Used the top 6 frequencies (see the figure bellow)

  • The final model had a RMSE of 6.69 µg/m3.

The improvement in residuals from the first model to the last is shown in the two figures bellow:

Conclusions

The average % of decrease in NO2 levels for all of the stations was estimated to be of the 53% as shown bellow:

And per station:

Brownish stations correspond to southern Madrid, while greenish stations correspond to northern stations. In general, northern stations had a higher impact. The highest impact was observed at station 55, located at Madrid's airport.

Pre-print

The pre-print containing all the information of the project is also in this github as MadridPollutionCovid19.pdf.