Low Emission
Zones (LEZs)

Why are LEZs important for smart cities?

Rapid urbanization and increased traffic lead to alarming levels of air pollution. Cities face the challenge of reducing pollutant gas emissions and promoting sustainable mobility. Low-emission zones (LEZs) effectively improve air quality, protect public health and promote a greener lifestyle in urban environments.

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Acceso a la Legislación vigente en España

Access to the European Air Quality Index

Access to the European Environment agency

What solution do we offer at Libelium, and how can it benefit a Smart City?


Data Collection


Model Implementation




ZPE Proposal



IImplementing an EPZ can be a costly and complex process. You aim to ensure that the LEZ positively impacts air quality and citizens' quality of life. LIbelium's Sustainability Impact Assessment (SIA) solution allows cities to predict the impact of the LEZ on overall air quality (not just in the application zone) using pollution dispersion modelling. You can see the effect on air quality throughout the city and make informed decisions before investing resources in implementing the LEZ. Thanks to the ability to predict the results of the LEZ, you can be sure that the implementation will be effective, ensuring that the LEZ does not produce adverse effects in other areas. In addition, the SIA solution also helps address citizens' concerns and resistance. By predicting the impact of the LEZ, you can confidently answer citizens' questions and clearly understand how the new system will work.

Real-time monitoring of air quality using IoT devices with the calculation of official WHO indicators.

The actual situation of the city (obtained from sensors) is displayed on the platform; indicators such as reduced CO2 or reduced number of private trips are shown as part of the results.Se muestra la situación real de la ciudad (obtenida de los sensores) en la plataforma; como parte de los resultados se muestran indicadores como por ejemplo CO2 reducido o número de viajes privados reducidos.

Traffic and pollution dispersion models to identify and control critical pollution areas.

Thanks to the traffic and pollutant dispersion models, we can detect areas where traffic significantly impacts the city's air quality.Gracias a los modelos de tráfico y dispersión de los contaminantes podemos detectar aquellas áreas en las que el tráfico tiene un mayor impacto en la calidad del aire global de la ciudad.

Ability to predict the effect that ZBE measures will have on city traffic.Capacidad de predecir el efecto que las medidas de ZBE van a tener en el tráfico de la ciudad.

Accurate traffic models can be used to study the impact of the measures, including traffic redistribution.

Analysis of the impact on citizens and residents of the adopted policies and regulations.Análisis del impacto a los ciudadanos y residentes de las politicas y regulaciones adoptadas.

If public transport data is included, one result of our solution is an indicator of the use of public transport by citizens, with the possibility of modelling changes in public transport and studying which scenarios encourage public transportation vs private vehicles.Si se incluyen datos de transporte público, un resultado de nuestra solución es un indicador del uso del transporte público por parte de los ciudadanos, con la posibilidad de modelar cambios en el mismo y estudiar qué escenarios fomentan más el uso del transporte público vs vehículo privado.

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How  has the Libelium solution worked in other success stories?

We have successfully developed the LEZs in multiple European cities and participated in European projects, such as AI4Cities in Paris, Helsinki, Amsterdam, Stavanger and Tallinn, or the IMAGINEXT project in Cartagena (Spain) and Lindau (Germany).

How does the Libelium solution work?

Data collection
Model implementation
ZBE proposal

What is the return on all this effort for your city?

Satisfaction of European regulations and Next Generation funds of public investment in actions towards sustainability and resilience. More than 10 Spanish cities benefiting from Next Generation funds have successfully designed their LEZs with Libelium's Sustainable Impact Assessment (SIA) solution for LEZs.

Thanks to Libelium's solution, our success story in Cartagena has implemented a LEZ without any negative impact or significant reduction in mobility services for its citizens.

Success story
Roberto José Liñán Ruiz
Professor of Sustainable Mobility and Transportation at UCAM.

“In a city like Cartagena, it is essential that all technological advances in urban mobility are always carried out most efficiently and sustainably possible while at the same time seeking the highest possible return. It makes no sense to implement technology that later becomes obsolete or to measure parameters that are disconnected from each other. It is essential to measure the entire ecosystem, to go step by step and for each investment to build a long-term project. The rulers pass through the office, but their projects must permanently contribute to the citizens' well-being

The only model in the market that merges traffic models with pollution propagation models. Awarded by EIT Urban Mobility in the ImagiNext Grant in cooperation with CARNET (Volkswagen Group and Universitat Politécnica de Catalunya), Fraunhofer and the leading mobility modelling company (PTV Group), who have joined forces with Libelium for pollution and sustainability modelling.

High capacity of mobility data generation for cities without historical data from pre-trained models with more than 20,000 hours of traffic data (and growing) coming from European cities of different sizes and behaviours.

Compatible with other urban initiatives through open standards for digital transformation such as ETSI NGSI-LD, GAIA-X, and FIWARE and validated by the European Union as a Connecting Europe Facilities service.

Fully available SaaS service with High ENS certification and support for National Interoperability Scheme best practices, Smart Data Models and semantic annotation.

Proven reduction of the design time of LEZs from 12 to 3 months, with the ability to extrapolate and compare mobility models between different cities, including European capitals such as Paris, Helsinki or Madrid.

Innovation: Using artificial intelligence to learn how mobility strategies impact air quality

While urbanisation and populations in urban areas increase, decarbonising cities becomes a harder challenge. In this context, as an unprecedented amount of urban traffic pollutants are generated in cities, IMAGINEXT validates a Software as a Service (SaaS) solution that utilises artificial intelligence (AI) to learn how special mobility strategies and sustainable transport measures impact air quality.

IMAGINEXT SaaS is being validated in two cities so that the AI can start receiving information and learning based on the size and density of each city. IMAGINEXT aims to accelerate market opportunities for mobility decision-makers with the potential of implementing sustainable transport solutions that are known to be effective in improving city air quality based on evidenced cases incorporated in the SaaS and extrapolated to bespoke uses through AI.

The challenge

To help effectively implement Low Emission Zones (LEZs) and decarbonisation strategies in cities.

Main Objective

To provide a tool to help mobility decision-makers implement necessary strategies to meet European environment targets with evidence-based data.

The Result

A SaaS solution that monitors mobility pollutant indicators and traffic conditions in real-time against specific measures and strategies and predicts their impact using AI models.

Project Partners

What impact do some measures and restrictions implemented in low-emission zones have on air quality?

Data related to traffic emissions are modelled through a different software: PTV Vissim. This allows, through vehicle flow data in the network, to calculate by means of statistical models the number of vehicles and their distribution over a time interval, in addition to having an internal model that translates this data directly into emissions of the main pollutants.

We are developing a platform to visualise the impact some measures and restrictions applied in low-emission zones have on air quality.

The operation of this tool is mainly based on an air pollution propagation model called MUNICH (Model of Urban Network of Intersecting Canyons and Highways), which is in charge of simulating air quality with a high resolution in medium-sized areas.

Several modules are coupled to this model to characterise:
  • Meteorology and its interaction with urban morphology.
  • (*) Emissions due to traffic within the represented network.
  • The global state of air quality through other models such as Chimere or direct observation and inference.
  • Mecanismo CB05 (Carbon Bond 05) de reacciones químicas, entre las que se cuentan las oxidaciones de VOCs, compuestos nitrogenados y sulfuros entre otros, además de el papel de la luz solar a través de la fotólisis.

CB05 (Carbon Bond 05) mechanism of chemical reactions, including oxidations of VOCs, nitrogenous compounds and sulfides, and the role of sunlight through photolysis.

Talk later?

Our success stories

Smart streetlights in Cartagena to measure air quality and noise.

March 10, 2023

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Contact with an IoT expert

Let's talk
Andrea Gómez
Smart Cities Sales Lead
Cristina Albesa
Sales Area Manager
Claudia Lacasta
Smart Cities Sales Manager
John David Babyack
Smart Cities Sales Manager
Daniel Sanz
Sales Area Manager South America, Africa, Asia & Oceania
Pablo García-Salguero
Smart Cities Business Development Manager LATAM
Sonia Tovar
Smart Cities Sales Manager

Libelium Cloud

A device management platform that enables complete end-to-end management of your IoT project. Store, visualize and analyze the data received. Send data to the main cloud platforms on the market.

Libelium's Artificial Intelligence (AI Services) team is able to develop customized services for municipalities, linked to the following areas:

● Low Emission Zones (LEZ) modeling and creation
● Crowd monitoring
● Pollen levels
● Heat maps