99% of the world's population breathes air that exceeds the limits recommended by the World Health Organization (WHO). This scenario is exacerbated in urban areas where more than 50% of the world's population is concentrated. To mitigate the problem of air pollution, considered by the WHO to be the main environmental risk factor for health worldwide, it is crucial to have more reliable and accurate data on the concentration of air pollutants in our cities, especially nitrogen dioxide (NO₂) because of its harmful effects on people's quality of life and the associated economic consequences.

To advance in this line of research, a team of scientists from the Earth System Services group of the Earth Sciences Department at the Barcelona Supercomputing Center - Centro Nacional de Supercomputación (BSC-CNS) has carried out a study that demonstrates that artificial intelligence can be of great use in obtaining reliable information on the probability of exceeding legal air pollution limits throughout the city. The aim of the research, published in the journal Geoscientific Model Development, is to help improve air quality management in urban areas by obtaining hourly maps of NO₂ concentrations at the street level, as well as a quantification of their associated uncertainty.

The new method combines for the first time the results of CALIOPE-Urban, a unique model in Spain that allows air pollution forecasting with very high resolutions of up to ten metres, at different heights and at any point in the city, with an extensive urban database that includes observations from official air quality stations, low-cost sensor campaigns, information on building density, meteorological variables, and a long list of other geospatial information. In this way, areas of the city can be identified where the current monitoring system needs to be improved, helping to optimise strategies to reduce air pollution.

"The combination of CALIOPE-Urban predictions with all these urban data using artificial intelligence allows us to improve the model because where simulation cannot explain the spatial distribution of pollution, with machine learning, we are able to correct and improve this prediction," says Jan Mateu, leader of the BSC Air Quality Services team and one of the main authors of the study.

Using machine learning techniques with observational data obtained with passive dosimeters during previous campaigns represents an important advance since the inherent uncertainties associated with air quality models due to the low density of monitoring stations are reduced. In this way, a better spatial characterisation of excess air pollutants in different parts of the city is achieved.

                                  The map on the left refers to the annual mean of NO₂ for 2019 after applying the presented

                                  correction method. The central figure illustrates the uncertainty field associated with the

                                  methodological correction. The map on the right shows the probability of exceeding the legal

                                  limit of the annual average of 40 μg/m³set by the European Commission in 2019, obtained by

                                  combining the two previous maps.

One of the main conclusions of the study, which has focused in this pilot phase on the city of Barcelona, is that the district of the Catalan capital with the worst air quality is the Eixample, where 95% of its area has more than 50% probability of exceeding the legal limit of the annual average of 40 μg/m³ of NO₂ set by the European Commission (European Air Quality Directive 2008/50/EC).

"The Eixample district, the most populated district of Barcelona, is the most affected area of the city, as the vast majority of its surface has more than a 50% probability of exceeding the annual NO₂ legal limit legislated by the European Commission. Thanks to our methodology, the public administration will be able to design and manage policies that improve air quality in urban areas, which is especially important because air pollution is the main environmental risk factor for human health," adds Álvaro Criado, a researcher in the BSC's Air Quality Services team and also one of the main authors of the study.

The CALIOPE-Urban model

Developed at the BSC, CALIOPE-Urban is a modelling tool that estimates the concentration of nitrogen dioxide (NO₂) at the street scale in Barcelona. However, it could also be applied to other cities or metropolitan areas. NO₂ and its precursors are emitted mainly by combustion sources such as vehicle engines, so monitoring is crucial to combat air pollution in large cities where traffic is often congested.

The unique system in Spain provides citizens and air quality managers with useful information on how traffic affects air pollution in each neighbourhood. This information is essential in designing and implementing effective planning and mitigation strategies to protect citizens from the health threats posed by air pollution. CALIOPE-Urban is currently focused on the city of Barcelona, but work is already underway to extend it to other municipalities in collaboration with different municipal and regional administrations.

CALIOPE-Urban combines the technology of CALIOPE's regional model, the BSC's air quality prediction system, with an urban model that considers air pollution at the street level and uses information on traffic emissions and meteorological data. CALIOPE, the only air quality system that provides operational forecasts for Barcelona, Catalonia, the Iberian Peninsula and Europe, is the only Spanish contributor to the European Union's Copernicus Atmosphere Monitoring Service (CAMS).