Globally, transportation is responsible for about 30% of air pollution, and in large cities, this is even higher. Between 20%-40% of deaths due to serious diseases are caused by air pollution (source: https://www.stateofglobalair.org/sites/default/files/documents/2020-10/soga-global-profile-factsheet.pdf). In Spain, 10.000 people die every year due to air pollution (almost tripling traffic deaths) and in Madrid alone, there are 5000 pollution deaths per year (14/day).

The combination of mobility data (generated from anonymized and aggregated mobile phone data of the telecommunications sector), IoT pollution & climate sensor data from moving vehicles, and Open Data, can provide actionable insights about traffic mobility patterns and pollution such that authorities and policymakers can better measure, predict and manage cities’ mobility and pollution.

This micro project is strategically aligned with Europe’s Green Deal and the EU Data Strategy.

Output

Demonstration with visualizations for pollution in Spanish city

Press release, blog post on organizations’ websites

Potentially scientific publication and patent (TBC)

Presentations

Project Partners:

  • Telefónica Investigación y desarrollo S.A. (TID), Richard Benjamins
  • Volkswagen AG, Richard Niestroj
  • Università di Bologna (UNIBO), Laura Sartori
  • Consiglio Nazionale delle Ricerche (CNR), Fosca Giannotti

Primary Contact: Richard Benjamins, Telefonica

Main results of micro project:

Air pollution is a serious problem in most cities. European regulation requires cities to not exceeding thresholds of pollutants. However, oftentimes measurements are taking place at the district level ignoring the fact that air quality might be different for every street. Moreover, air quality has not the same importance in a residential area versus a more industrial area. And the type of use is also important such as schools, hospitals, sports facilities, et cetera.

We have created a prototype, in collaboration with the city of Madrid, that exploits both privately held data as well as publicly available (open) data to monitor air quality at street level. Data sources include traffic, vegetation, temperature/windspeed and demographics. The system allows cities to perform evidence-based policy- and decision-making. This is the first of a series of three micro projects.

Contribution to the objectives of HumaneAI-net WPs

This project uses industrial data from the telecommunications industry, combined with open data and IOT generated data to solve an important societal problem, which are the two objectives of WP6. It shows a way in which the industry can create new products and services using artificial intelligence and data, very much aligned with the European data strategy. However, using data and AI for more evidence-based policy-making and decision-making by public institutions, also has ethical issues such as bias and undesired discrimination. In the prototype, we not only measure the quality of the air but also how many people are affected by this. In the series of three micro projects, we want to study whether this kind of data driven policy-making introduces undesired bias and inequality. We want to use the results of the other work packages to mitigate those potential problems.

Tangible outputs

Attachments

MP-6.10-airquality_v2_Berlin.pptx

Results Description

Globally, nine out of ten people breath polluted air, and it is the direct death cause of more than seven million people per year . Transportation by combustion engines is responsible for about 30% of air pollution, and in large cities this is higher. Urban areas and their respective local governments are facing immense challenges with accelerating rates of NO2, Ozone, Particle Matter and CO2 emissions amongst other pollutants. In their mission to ensure cleaner air for their cities, the first and most important step is to collect accurate and consistent data to ensure healthy air quality levels for citizens as well as to identify where the major air pollution hotspots are. Moreover, cities are increasingly looking at their transit systems to cut those emissions that impact the public health and the environment.

Until now, monitoring the quality of air has involved great efforts for cities. For local governments, air quality management can be costly due to the required expensive equipment to monitor the key pollutants that worsen the quality of air. There are several sources of pollutants: industrial activities, construction, residential heating, among others, but road traffic of fossil combustion vehicles is the most prevalent source for dangerous pollutants such as NO2 and Ozone (O3). However, the way to investigate the actual traffic volumes is relatively manual, using roadside interview data and manual counters, although IoT sensors to quantify are increasingly deployed. Not only is this expensive, but often it is also inaccurate – providing a small snapshot on how traffic really moves around cities and countries. However, by using mobility data and IoT, the authorities can shift to Big Data and AI. Rather than using small samples, they can now receive insights more frequently, precise, and granular. That is an important complement to inform decisions with respect to air quality, as traffic along the weather conditions are closely correlated with air pollution levels.

European regulation requires cities to not exceeding thresholds of pollutants. However, oftentimes measurements are taking place at the district level ignoring the fact that air quality might be different for every street. Moreover, air quality has not the same importance in a residential area versus a more industrial area. And the type of use is also important such as schools, hospitals, sports facilities, et cetera.

Artificial intelligence algorithms help in increasing the spatio-temporal accuracy of the monitoring activity and in providing predictions on future (dangerous) pollution levels, so authorities can take preventive actions. We have performed a series of innovation activities from the development of a prototype in one city (Madrid), which we subsequently validated in a second city (Valladolid) that also includes a social and ethical impact analysis to understand whether air quality related decisions are affecting social groups in an equal manner. An important feature of the system built, is the collection of heterogeneous data, algorithms, advanced visualization, and filtering control in a single platform. This capacity is key to perform explorative data analysis and to find insights.
This project uses industrial data from the telecommunications industry, combined with open data and IoT generated data to palliate an important societal problem, while at the same time showing a way in which the telco sector can create value using artificial intelligence and data. It is aligned with the European data strategy, the Guidelines for Trustworthy AI, and the European Green Deal.

Publications

https://humane-ai-net.kl.dfki.de/webdav/MicroProjects/WP6/Air%20Quality%20-%20FinalReport%20-%20EN%20-%20Humane_v1.0.pdf

https://unstats.un.org/unsd/undataforum/blog/7-ways-mobile-data-is-being-used-to-change-the-world/

Paper presented as poster on HAI conference, Sweden, 2022.

Links to Tangible results

https://www.youtube.com/watch?v=WBNf5F9Kp7c
https://www.humane-ai.eu/project/improving-air-quality-in-large-cities-using-mobile-phone-and-iot-data/