Scientists develop a pioneering artificial intelligence method to combat urban air pollution
(Nanowerk News) 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. In order to reduce the problem of air pollution, which is considered by WHO as a major environmental risk factor for health worldwide, it is very important to have more reliable and accurate data on the concentration of air pollutants in our cities, especially nitrogen dioxide (NO2).2) because of its harmful effects on people’s quality of life and the associated economic consequences.
To advance this research, a team of scientists from the Earth System Services group of the Department of Earth Sciences at the Barcelona Supercomputing Center – Centro Nacional de Supercomputación (BSC-CNS) has conducted research which shows that artificial intelligence can be very useful in obtaining reliable information about possible exceedances legal limits for city-wide air pollution.
Research objectives, published in journals Geoscience Model Development (“A method that enables data fusion uncertainty to map road-scale NO on an hourly basis2 in Barcelona: a case study with CALIOPE-Urban v1.0″), is to help improve air quality management in urban areas by obtaining hourly NO maps2 concentration at the street level, as well as measuring the associated uncertainty.
This new method combines for the first time the results of CALIOPE-Urban, a unique model in Spain that allows forecasting air pollution at very high resolutions of up to ten meters, at different altitudes and at any point in the city, with large urban areas. database that includes observations from official air quality stations, low-cost sensor campaigns, building density information, meteorological variables, and a long list of other geospatial information. In this way, areas of the city where the current monitoring system needs to be improved can be identified, helping to optimize strategies for reducing air pollution.
“The combination of the CALIOPE-Urban predictions with all this urban data using artificial intelligence allows us to improve the model because if the simulation cannot explain the spatial distribution of pollution, we can use machine learning to correct and improve these predictions,” said Jan Mateu, Quality Services team leader Air BSC and one of the lead authors of this study.
The use of machine learning techniques with observational data obtained during previous campaigns using passive dosimeters is an important advance, as it reduces the uncertainty associated with air quality models due to the low density of monitoring stations. This provides a better spatial characterization of excess air pollution in different parts of the city.
One of the main conclusions of the study, which in this pilot phase focused on the city of Barcelona, is that the district with the worst air quality in the Catalan capital is Eixample, where 95% of the area has more than a 50% probability of exceeding the average NO. annual2 the limit of 40 μg/m3 set by the European Commission (European Air Quality Directive 2008/50/EC).
“The Eixample district, the most populous district in Barcelona, is the most affected area in the city, because most of its surface area has a probability of more than 50% to exceed the annual NO.2 limits set by the European Commission. Thanks to our methodology, public administrations will be able to design and manage policies to improve air quality in urban areas, which is very important because air pollution is a major environmental risk factor for human health,” added Álvaro Criado, a researcher on BSC’s Air Quality Service Team and one of one of the lead authors of this study.
Model CALIOPE-Urban
Developed at BSC, CALIOPE-Urban is a modeling tool that estimates nitrogen dioxide (NO22) at the street level in the city of Barcelona, although it can also be applied in other cities or metropolitan areas. NO2 and its precursors are mostly emitted from combustion sources, such as vehicle engines, so monitoring is critical to combating air pollution in large cities where traffic is often heavy.
The system, which is unique in Spain, provides citizens and air quality managers with useful information about how traffic affects air pollution in each neighborhood. This information is critical to 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 underway to expand it to other cities in collaboration with various municipal and regional administrations.
CALIOPE-Urban combines the technology of the CALIOPE regional model, the BSC air quality prediction system, with an urban model that considers air pollution at the street level, using 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 Spain’s only contributor to the European Union’s Copernicus Atmospheric Monitoring Service (CAMS).
