NO2 sensors being tested for air-quality management

While wearable technology that measures body parameters has become increasingly popular in recent years, the Intuitive Internet of Things (I²oT) is next on the horizon. This connects everybody and everything everywhere and stores data in the cloud. The I²oT is expected to manage the sustainability of our world.

Health issues resulting from poor air quality are a growing concern, therefore, there is an increased demand for accurate monitoring. Air quality is typically measured in just a few distinct locations per city, with specialised equipment. Many current gas sensors are large in size, have high power consumption and are too cost prohibitive to be implemented on a large scale for I²oT applications. To address this, imec and Holst Centre have developed small, low power and high quality autonomous sensors that wirelessly communicate with the environment and the cloud.

The NO₂ sensors have been integrated into the Aireas air quality network, a multiple sensor network in the city centre of Eindhoven. The purpose of this was to test the stability of the sensors, and benchmark them against established reference sensors. The sensors have been operational since May this year and provide valuable outdoor sensor data. During traffic rush hours, the sensors detect a significant increase of NO₂ concentration up to the health safety limits.

imec and Holst Centre are currently deploying a similar sensor network inside the Holst Centre building in Eindhoven to test the sensors for indoor air quality monitoring. This environmental monitoring platform features the proprietary NO₂ sensors and commercial sensors for temperature, relative humidity and CO₂. The measured levels can be monitored live, over the internet. Next, the companies plan to add proprietary low-cost, low-power sensors for CO₂, VOCs, Ozone and particle matter to the sensor network.

Kathleen Philips, Director of Perceptive Systems for the Intuitive Internet of Things R&D Programme, imec. “Data fusion methodology and advanced algorithms enable us to combine data from different sensors such as temperature, several gasses, humidity, human presence detection and to derive contextual knowledge. This information contributes to a correct interpretation of the situation and helps us to take adequate actions to solve the problem. In this way, we have developed a context-aware intuitive sensing system.”