Periodic Reporting for period 1 - LEARN (DEVELOPMENT OF NOVEL ASSESSMENTS FOR INDOOR AIR QUALITY MONITORING AND IMPACT ON CHILDREN'S HEALTH)
Período documentado: 2022-05-01 hasta 2023-10-31
LEARNing about the status of air quality in schools and its impact on the cognition of children is a major cornerstone in LEARN. Aiming to overcome the barriers of the currently existing technologies and take a bold step towards developing and deploying novel sensors to detect the presence of possibly harmful air pollutants such as volatile organic compounds and ultrafine particles, LEARN will measure and characterize indoor and outdoor air pollutants and evaluate the presence of biomarkers of exposure and their effect on children´s cognition while trying to recapitulate those effects using C.elegans as a biosensor. Advanced human-based in vitro models of lung and skin coupled with a revolutionary multi-sensing device will be used to investigate their mechanisms of toxicity in real time. Novel remediation strategies will be explored to improve air quality and promote children´s quality of life and life expectancy. The consortium is composed of 11 leading research teams, unrivaled in their fields (environmental epidemiologists, toxicologists, air quality specialists, systems biology, engineers, and citizen/social scientists). The scientific achievements expected to result from LEARN will unlock a large technology potential in IAQ for decades to come, leading to disruptive societal and economic impacts steaming from a radical improvement in the quality of life of children in Europe. LEARN is part of the European cluster on indoor air quality and health.
LEARN will recruit schools in Denmark, Belgium, and Greece to integrate and compare children with different characteristics. Through biomonitoring surveys in cohorts, LEARN will characterize the environmental exposure to indoor and outdoor air pollutants. IAQ assessment will include volatile compounds, particulate matter, and microbiological components present on particles, together with other relevant co-exposures to characterize “indoor exposome” in schools. Particulate matter will be measured by filter methods, and further determination of the biological fraction and the volatile chemicals will be performed in the lab using validated and approved methods. Particle mass concentrations will be calculated and the identified pollutants will be fully characterized by advanced techniques before being tested in 2D conventional in vitro and advanced organ-on-a-chip models of lung and skin, and biosensor models. We´ll assess the IAQ in parallel with the newly developed sensors and in combination with IAQ purification systems. LEARN will develop and prototype innovative sensor technologies for IAQ monitoring. The prototype will include a sensor selective to individual VOCs and a sensor system for UFPs. The work will be organized in three main tasks: 1) research and innovation, where the key building blocks will be investigated and validated in the lab; 2) sensor system development, focusing on the engineering of the sensor and prototypes; 3) testing and validation phase, in the lab and in the field to inform the status of the sensor development and to shape next iterations. The prototypes will serve for the validation of the effectiveness of the air purification systems and remediation strategies LEARN will develop advanced in vitro models of lung barrier and skin on a chip. A multi-sensing platform for the detection of reactive oxygen species, cell viability, and inflammatory markers will be integrated into the SoC model. The advanced lung and skin models will be used to assess the toxicological effects of chemical and particulate matter air pollutants. Standardization of a protocol to use C. elegans as a biosensor to evaluate toxicity and neurotoxicity of the environment will be performed. A novel operation concept for air purification systems based on UFPs concentration will be defined. Real IAQ data will be used to describe the efficiency of filters and air purifiers and prototypes of air purifier devices will be optimized. With the LEARN sensor development, the operation of the air purifier will be controlled by relevant data for classrooms. LEARN will centralize all data collected and generated within LEARN according to the FAIR data principles. An API and a user interface handling all data-related functionalities will be developed. Chemical monitoring data will be shared in the IPCHEM in collaboration with the JRC-IPCHEM.
LEARN will develop new sensor technology based on ionization techniques that surpass the current state-of-the-art IAQ assessments. These sensors will fill an important gap in the currently existing low-cost sensing technology while enabling portability and deployment at scale. It will be able to detect UFPs and individual VOCs with a target selectivity and sensitivity only available in high-cost and large-size laboratory instruments. The introduction of this low-cost technology would enable a new revolution of low-cost IoT sensors for indoor environments, similar to the one recently observed in citizen-science outdoor air quality monitoring campaigns. The applicability of this technology in two different scenarios across multiple countries (i.e. Denmark, Belgium, and Greece) opens the door for large-scale validation of the reliability of the sensor while providing sufficient big data to support and contribute to the definition of future guidelines and standards on ideal air quality characteristics for children. Along with LEARN, LBoC and multi-sensing SoC models will be developed to assess toxicological exposure to air pollutants. LEARN proposes the development of a revolutionary multi-sensing device to be integrated with the SoC to enable the monitoring of the toxicological effects of chemicals and nanomaterials in real time. LBoC models will be optimized and used to characterize the toxicological profile of selected pollutants in the lung tissue. The obtained detailed insights will be explored for the identification of cellular biomarkers of air pollution-associated diseases. From an in vivo perspective, LEARN will also leverage the application of C. elegans as a novel biosensor for IAQ.