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. 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. During the second reporting period, SynAir-G achieved substantial scientific, technological, and operational progress across all work packages, despite a number of challenges that affected timelines. Major advances were made in the development and optimization of the project’s environmental sensing ecosystem. The ENSENSIA low-cost multi-sensor system was fully developed, calibrated through extensive co-location campaigns, and deployed in 25 classrooms across five European countries, generating rich datasets on CO2, PM2.₅, NOx, O₃, VOCs, temperature, and humidity.
