While the number and types of indoor air pollutants is rising, much is suspected but little is known about the impact of their potentially synergistic interactions, upon human health. Highly susceptible populations include children, allergy and asthma sufferers, and those from low socioeconomic backgrounds, however no specific guidance or interventions are available.

SynAir-G aims to reveal and quantify synergistic interactions between different pollutants affecting health, from mechanisms to real life, focusing on the school setting.

“We will develop a comprehensive and responsive multipollutant monitoring system, advance environmentally friendly interventions, and disseminate the generated knowledge to relevant stakeholders in accessible and actionable formats.”


To achieve this vision, SynAir-G will construct and deploy novel and improved sensors of chemical and biological (allergens, microbes) pollutants. These will be tested in a real-world setting, in participating schools of 5 countries around Europe and eventually combined into a multisensing platform.

In the same setting, pollutants will be linked to their sources and two eco-friendly air-purifying devices will be assessed.

Health outcome data will be obtained from children using a gamified application and prospective monitoring, respecting privacy. Highly susceptible children, such as those with allergy or asthma, will act as sentinels to increase sensitivity of the system, that will be able to provide stratified (susceptibility-specific) alerts. Explainable AI will support the near-real time analysis and response.

In parallel, cell and mouse models will evaluate the mechanisms and complex dose-responses of the synergistic parameters. SynAir-G will thus provide FAIR data on air pollutants and their sources, a comprehensive and personalized user-friendly solution to monitoring indoor air quality, and proposals for possible interventions and an improved regulatory framework, robustly supporting the Zero Pollution Action Plan.

SynAir-G is part of a larger cluster of EU funded research projects known as the Indoor air and health cluster where further synergies will be identified.


Work Packages

Lead: EPFL

WP1 is based on four core goals which focus on developing a novel sensing system targeting atmospheric pollutants in indoor air spaces. The sensing system will be optimized for size, cost-effectiveness, and near-time reporting. The calibration and evaluation of the sensing system will be performed in realistic indoor and outdoor environments.

Lead: NKUA

The sensing system developed in WP1 will be the main monitoring tool used achieved the five goals of WP2. The sensing system will be used to establish a multi-pollutant realistic setting, which considers geographical, cultural and socioeconomic cohort of schoolchildren. This will be used to meticulously track biological and chemical indoor air pollutants in the cohort. A gamified e/mHealth tracking system will also be developed to collect participant input/output, and together with the outcomes of the sensing system, they will be utilised to evaluate overall, respiratory, immune and the mental health of the participants, and associate them with the indoor air quality determinants.  

Lead: CHUM

WP3 aims to collect data and model them to identify the sources of chemical and biological indoor air pollutants in and around schools, compare and depict them with indoor and outdoor air quality interactions. The WP3 also aims to link air pollutants in and around schools to their respective sources, and finally, to develop the SynAir-G Inhalation Exposure Assessment Tool.

Lead: UER

WP4 aims to measure the effects of pollutant co-exposures on airway and immune cells in-vitro, an to develop a testing system that evaluate the acute synergistic effect of pollutants by using precision-cut lung slices (PCLS). Moreover, it seeks to generate a novel model assessing complex dose-responses of pollutant synergies on airway barrier and immune functions, by using lung organoids. Finally, it targets to validate the synergistic effects of multiple exposures in mouse models in-vivo, to informing quality standards.


WP5 will deploy the sensing system, developed in WP1, to achieve four goals. The research team will collect the requirements for the integrated system and monitor its integration, and later develop the SynAir-G integrated autonomous sensing platform. Then, it will design and develop diversified mitigation solutions for IAQ improvements, and finally, develop the user interfaces, alert system and modules for device connectivity.

Lead: UoP

WP6 targets to implement data ingestion engine, receiving, processing, and forwarding the analysis of high-quality data from sensors, actuators, wearables, questionnaires, and gamified apps. Then, the team of WP6 will compute precise spatiotemporal models of air quality in school classrooms by applying explainable AI for sensor data analysis in addition to mathematical models for pollutant dispersion. Later, it will analyse the interaction effects of chemical, biological pollutants and ambient conditions on health outcomes, by using state-of-the-art AI methods and mathematical modelling. Finally, it will develop and provide a cloud-based platform for comprehensive indoor air quality monitoring and improvement.

Lead: INLE

WP7 aims to organise and deploy optimal communication, dissemination, exploitation and data management of the project.

Lead: NKUA

WP8 aims to supervise and control the scientific progress of the project; and assess the project in its ethical standard and implement the gender equality policy within the project. The team, in charge of WP8, will manage the project under its financial, administrative and legal aspects, and prepare the progress reports. Moreover, it will guarantee the logistic support and follow-up for the consortium meetings and workshops. Finally, it will be in charge of designing and implementing the networking strategy for the project.

Public deliverables

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