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/Expertise/Sensor solutions for IoT/Technology for real-time air quality monitoring

Technology for real-time air quality monitoring

How do you reliably measure something so ephemeral as the air around us? With a dense network of low-cost sensors – powered by calibration algorithms and analytics that ensure a complete and correct picture.

Air quality varies wildly according to location or time of day. So you need to be able to take targeted actions instantly, to ensure that everyone breathes healthy air, all the time.

That’s only possible if you can rely on a fine-grained and real-time air quality monitoring system. Unfortunately, current air quality sensors are either too expensive and bulky, or inaccurate.

Imec’s real-time air quality monitoring approach enables you to use off-the-shelf sensor boxes, that are getting cheaper with time in urban environments. It’s enabled by the Internet of things:

  • Low-cost, off-the-shelf sensors extract raw data such as CO2, NO2 or particle matter levels.
  • Sensor fusion and calibration algorithms transform those measurements into accurate data – which happens in the cloud.
  • AI-based interpolation algorithms create real-time air quality maps and provide meaningful insights.
  • Analysis and visualization software constructs an accessible integrated image that allows users or systems to take appropriate actions.

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Real-time air quality monitoring for smart cities

Governments are under pressure to reduce harmful emissions such as particle matter, NO2 and ozone. Luckily, in the context of a smart city, they can take targeted actions such as adaptive speed limits and dynamic traffic control.

But to take and evaluate targeted actions, you need highly localized air quality data, which the current reference stations can’t provide.

Within the imec City of Things program, we’re testing a large-scale solution that combines data from a wide array of sources:

  • reference stations
  • fixed low-cost sensors from any vendor
  • citizen science sensors
  • mobile sensors, for instance on postal trucks

After automatic calibration and interpolation, this data can be used to make deep analyses and draw a fine-grained real-time air quality map that acts as a digital twin to support smart city policies.

Real-time air quality in Antwerp
Accurate and fine-grained map of the air quality in the city of Antwerp.

Want to join forces with us to scale up this solution?

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Real-time air quality monitoring for smart buildings

Poor indoor air quality is a threat to our health, comfort and productivity. Rising CO2 levels, for example, immediately reduce our ability to focus.

Imec’s technology portfolio allows you to develop an indoor air quality monitoring system based on low-cost sensors. Moreover, we have developed algorithms that make accurate fifteen-minute predictions of the air quality in a room.

real-time indoor air quality monitoring
Visualization of real-time air quality monitoring at imec’s HomeLab.

This combination of sensors and prediction algorithms allows you to develop closed-loop systems for automatic comfort control.    

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Real-time air quality monitoring for smart agriculture

Air quality is also a growing concern for farmers. They need to comply with regulations that limit emissions of harmful gases such as ammonia and NOx.

By deploying low-cost, low-power air quality sensors in their fields – which could be powered by miniature solar panels – they can get real-time insights into the impact of their activities.

Tap into our expertise

Are you developing a system for real-time air quality monitoring? There are several ways in which imec can assist you speeding up your innovation.

  • assembly – Turn to imec for the expert combination of sensing, processing and connectivity elements that make up your sensing unit.
  • benchmarking – Ask our researchers to conduct lab or field experiments to compare the performance of your sensing unit to that of reference equipment.
  • calibration and analytics – Count on imec to develop the algorithms to extract high-quality data from low-cost sensors, and to generate advanced predictions and insights.

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