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HomeScienceA groundbreaking approach to combat urban air pollution using artificial intelligence.

A groundbreaking approach to combat urban air pollution using artificial intelligence.


The workflow of the two methodologies for integrating the studied data. Hourly data from the monitoring stations are combined with the results of the hourly dispersion model (UK-DM) and the microscale unvaried time LUR basemap (UK-DM-LUR). PBL stands for planetary boundary layer. credit: Geological model development (2023). DOI: 10.5194/gmd-16-2193-2023

99% of the world’s population breathes air that exceeds the limits recommended by the World Health Organization. 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, which is considered by the World Health Organization to be the main environmental risk factor for health worldwide, it is necessary to obtain more reliable and accurate data on the concentration of air pollutants in our cities, especially nitrogen dioxide (NO).2) because of their harmful effects on people’s quality of life and associated economic consequences.

To advance this type of research, a team of scientists from the Earth System Services Group of the Department of Geosciences of the Barcelona Supercomputing Center – Centro Nacional de Supercomputación (BSC-CNS) has conducted a study showing that artificial intelligence can be of great benefit in obtaining Reliable information about the possibility of exceeding legal limits for air pollution around the city.

The aim of the research published in the journal Geological model developmentto help improve urban air quality management by obtaining hourly maps NO2 Concentrations at street level, as well as measure associated uncertainty.

The new method combines for the first time the results of CALIOPE-Urban, a model unique in Spain that allows forecasting air pollution with very high accuracy of up to ten meters, at different altitudes and at any point in the city, with a large-scale urban area. A 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 improvement, which helps to improve strategies to reduce air pollution.

“Combining the CALIOPE-Urban predictions with all this urban data using artificial intelligence allows us to improve the model because in the event that the simulation cannot explain the spatial distribution of pollution, we can use machine learning to correct and improve this prediction,” says Jean Mathieu. BSC Air Quality Services team leader and one of the study’s lead authors.

The use of machine learning techniques with monitoring data obtained during previous campaigns using passive dosimeters represents an important advance, as it reduces the inherent uncertainty associated with air quality models due to the lower 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 area with the worst air quality in the Catalan capital is the Eixample, with 95% of its area having more than a 50% probability of exceeding the average annual NO2 limit of 40. µg/m3 defined by the European Commission (European Air Quality Directive 2008/50/EC).

“The Eixample, the most populous district of Barcelona, ​​is the most affected area of ​​the city, since the vast majority of its area has a greater than 50% probability of exceeding annual NO.2 limit set by the European Commission. Thanks to our methodology, public administration will be able to design and manage policies to improve air quality in urban areas, which is particularly important because air pollution is the main environmental risk factor for human health, adds Alvaro Criado, researcher at BSC Air Quality Services Team and one of the lead authors of the study .

CALIOPE-Urban model

Developed at BSC, CALIOPE-Urban is a modeling tool that estimates nitrogen dioxide (NO2) at street level in the city of Barcelona, ​​although it can also be applied to other cities or metropolitan areas. no2 Its precursors are mainly emitted by combustion sources, such as vehicle engines, so monitoring is crucial 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 essential for the design and implementation of 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 expand it to other municipalities in collaboration with various municipal and regional administrations.

CALIOPE-Urban combines CALIOPE’s regional model technology, the BSC Air Quality Prediction System, with an urban model that takes into account air pollution at street level, using information on traffic emissions and meteorological data. CALIOPE is the only air quality system providing operational forecasts for Barcelona, ​​Catalonia, the Iberian Peninsula and Europe, and is the only Spanish contributor to the European Union’s Copernicus Atmosphere Monitoring Service (CAMS).

more information:
Alvaro Criado et al, Methods enabling uncertainty in data fusion for hourly street scale mapping NO2 In Barcelona: a case study with CALIOPE-Urban v1.0, Geological model development (2023). DOI: 10.5194/gmd-16-2193-2023

Provided by the Barcelona Supercomputing Center

the quote: Pioneering Artificial Intelligence Method to Combat Urban Air Pollution (2023, April 25) Retrieved April 25, 2023 from https://phys.org/news/2023-04-artustry-intelligence-method-urban-air.html

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