Bettair winner of the AIRLAB Microsensors Challenge 2023 to measure air quality

Josep Perelló, CEO de Bettair, en la entrega de premios en Bangkok (Tailandia)

Following an update of the results, Bettair maintains its rating as the most accurate outdoor sensor system, without the need for recalibration.

  • The compact devices of Bettair have been recognised worldwide for their accuracy, being also very reliable and robust, passing all the logistical and operational tests of the challenge. 
  • Bettair is distinguished in this study, for ensuring this level of data quality in all tests designed by Airlab, without human intervention to adjust the data. 
  • This award confirms the growing preference in the environmental sector for autonomous and self-sufficient measurement systems that guarantee maximum return on investment. 
Josep Perelló, CEO of Bettair, at the closing event at Bangkok (Thailand) receiving the award
Josep Perelló, CEO of Bettair, at the closing event at Bangkok (Thailand) receiving the award

Bettair has been honoured with the award for the manufacturer of the most accurate multi-pollutant sensor for outdoor environments, awarded by AIRLAB Microsensors Challenge 2023, following an independent evaluation where many technologies measuring air quality were compared. 

The award, which was presented during the closing ceremony of this benchmark competition in the air quality sector in Bangkok and Paris simultaneously, reflects the concern for air quality as one of the main challenges facing cities. The authorities and the public need detailed and personalised information on the suitability of the air, as a basis for understanding and acting with the aim of improving its quality.  I want to share the award with the team. Our achievements at the AIRLAB Challenge reflect our dedication to quality and innovation”, says Josep Perello, CEO and co-founder of Bettair, with a special mention to the work and involvement of the CTO, Leonardo Santiago and CSO, Francisco Ramírez. Josep Perello says that “Bettair is at the forefront of the integration of Machine Learning in Environmental Management thanks to its Data Science team. This innovative approach allows us to deliver solutions that significantly improve ease of use and reduce total cost of operation, while maintaining the highest standards of performance and reliability, without the need for recalibration and human intervention”. 

AIRLAB CHALLENGE – AN INDEPENDENT EVALUATION OF MICROSENSOR TECHNOLOGY

Given that outdoor air pollution causes 4.2 million premature deaths globally per year according to the World Health Organization (WHO), reliable data on air quality is critical to environmental policy and health. Given this scenario and thanks to the growing development of microsensor technology, the independent regional air quality observatory for the Paris Airparif metropolitan region, and its open innovation laboratory AIRLAB, have decided to organise the AIRLAB Microsensors Challenge periodically. 

This event aims to provide a robust and independent evaluation of the performance of microsensors, thanks to a panel of international experts and under real conditions of use. Since 2018, it has evaluated 164 devices over four editions, guiding users in selecting the best sensors and fostering innovation in this field. 

For the first time, the tests have been carried out in both France and Thailand to help better understand the impact of weather conditions, and the different levels and sources of pollution, on the performance of microsensors. This approach adds rigor to benchmarking, knowing that there was no access to baseline data in Thailand. Under these conditions, Bettair’s nodes proved to be accurate, as well as very reliable and robust in data delivery. Apart from being the most compact device by incorporating more air pollutants, including noise pollution. 

BETTAIR RESULTS AND RATING UPDATE

Bettair awardsRecently, following a thorough review of the results by the organisation, Bettair has reaffirmed its position as the most accurate environmental monitoring system on the market without the need for calibration over the life of the sensors.  The highlight of Bettair is its ability to obtain accurate measurements without the need for constant calibrations or reliance on data from external sources. This independent and autonomous approach ensures the reliability and accuracy of the measurement, even in zones where does not exist traditional infrastructure to measure air quality.  This award underlines the growing preference in the environmental sector for autonomous and self-sustaining systems.  As a result, Bettair achieved outstanding results in the main category: Most accurate multi-pollutant sensor system. Bettair also received an award for ozone monitoring – for best overall performance for O3 – maintaining its leadership for ozone pollution since 2021; and, in addition, obtained other results in both places, Thailand and France: CO, NO (the best score in its class above all competitors), and PM2.5 (the best score tied with other manufacturers).  According to the jury, the Bettair static node provides excellent performance for O3, being the leader of this contaminant in the previous and current edition of the challenge. Its performance was consistent in the two outdoor static deployments, varying from very good to excellent for particulate material measurements and average for NO2. By having reduced maintenance and offering a diverse list of specific contaminants, it scores very well on the utility scale for monitoring applications.” 

STRATEGIC ALLIANCE WITH DNOTA

In parallel to this victory, Bettair celebrates its strategic partnership with dnota, a collaboration forged to drive business growth. This strategic alliance positions both companies as key players in the air quality sector. Drawing on its more than 40 years of experience in the traditional air quality business, dnota has created a global network of distributors, ensuring Bettair’s presence in more than 40 countries and consolidating its role as an influential contributor to the air quality market.