Today on our blog we’ve decided to take the mobility and traffic Big Data analysis we started here a little bit further, looking at the relationship between commuting and air pollution. Air quality is clearly a major challenge for large urban areas and according to the WHS, it is also a serious health risk, which is concerning given that in 92% of the world population in 2014 was living in places where the WHO air quality guidelines levels were not met.
Reducing road traffic to improve air quality is proving a struggle for local governments, and to address this issue, they are monitoring a range of harmful gases on a day to day basis. One of these is Nitrogen Dioxide (NO2), and its production correlates directly to the density of motorised vehicle traffic as well as atmospheric conditions.
|Figure 2: Dashboard (TIBCO Spotfire) with the KPIs about the NO2 measurements in Madrid.|
- There is a significant increase of NO2 pollution in September, probably due to the lack of rain and wind (top right).
- Unsurprisingly, there are clear increases during rush hours (from 7:00 to 9:00 in the morning and 20:00 to 22:00 in the evening), although interestingly during the evening rush hour NO2 pollution levels are very similar regardless of whether it is a week day or the weekend (figure 3).
|Figure 3: Hourly average NO2 levels per day of the week.|
|Figure 4: Postcodes in central Madrid represented as coloured polygons according to the density of workers (red denoting a greater density). The markers represent air pollution sensors across the ctiy.|
One should also highlight that there is one station in Madrid’s city centre (in the Plaza del Carmen) which registers high NO2 levels even though it is located close to a pedestrianized zone. However, when we take a closer look at Google Maps, we can see it is located between two car parks, which explains the above-average NO2 readings.
Figure 5: Air quality (NO2) in Madrid Region.
Geographic distribution of the number of hours with values greater than 200
|Figure 6: Heat map generated from density of workers in the region of Madrid.|
We would love to play with the data collected from those mobile sensors in order to create a correlation model of traffic and pollution. However, in the mean time, we have Smart Steps data as a powerful complementary source to find out which areas of the city are affected by NO2 more importantly, making short term forecasts for policy makers to act accordingly.