[TMP-067] Internet of Things and Artificial Intelligence to improve air quality in cities

Contact person: Richard Benjamins (richard.benjamins@telefonica.com) 

Internal Partners:

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

External Partners:

1. City Council, Valladolid, Pedro de Alarcon, pedroantoniode.alarconsanchez@telefonica.com

Globally, nine out of ten people breathe polluted air, and it is the direct death cause of more than seven million people per year. 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). 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 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 exceed 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.

The combination of mobility data (generated from anonymized and aggregated mobile phone data of the telecommunications sector), IoT pollution and 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.

Results Summary

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. The prototype we built, in collaboration with the city of Madrid, 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/wind speed and demographics. The system allows cities to perform evidence-based policy- and decision-making. 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 exploratory 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 telecom 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. This is the first of a series of three micro projects.

Tangible Outcomes

1. Press release through the participating organizations’ websites raising awareness about the issue: https://unstats.un.org/unsd/undataforum/blog/7-ways-mobile-data-is-being-used-to-change-the-world/ 
2. video explaining the project Air Quality for All (AQ4A) that could be used for government and business presentations https://www.youtube.com/watch?v=WBNf5F9Kp7c
3. Source of the presentation slides https://www.humane-ai.eu/_micro-projects/mps/MP-23/MP-6.10-airquality_v2_Berlin.pptx