The TwinAIR System
1. Environmental Approach
The main parts of the environmental approach are the:
Identification and Characterization of pollutants:
Measurements of key pollutants, (e.g. NO, CO2, VOC) and particles provide information on pollution concentration-time profiles in pilot indoor spaces. Sensors are used to obtain high frequency data. Further analysis of air composition is conducted to yield a rich dataset, which determines sources of pollution. Key chemicals associated with pollution sources allow the magnitude of the source to be assessed.
Indoor and Outdoor Correlation:
Once sources are identified, mitigation strategies (e.g., increasing the ventilation rate) are undertaken and through the measurements made the success of these mitigations is assessed. The correlation between outdoor and indoor modes is achieved through the use of proven methods and machine learning techniques enhanced with measurement datasets, which provide the research team with the ability to develop infiltration models. These models measure the infiltration factor in several different types of buildings with high levels of accuracy.
2. Health Approach
Air monitoring with different innovative and sensitive methods evaluating the chemical pollutants and biological contaminants affecting health;
Individual health measurements and questionnaires capturing continuously self-reported health status, respiratory and allergic symptoms, and mental health and general well-being linked to respective IAQ; these data accompany and strengthen the monitoring and risk assessment process;
Human microbiome studies aiming to examine the effects of the air composition, both chemical and biological, in the human nasal/pharyngeal and skin microbiome and resistome. Also, in vitro and ex vivo 3D lung models are carried out to obtain insight into indoor air composition (chemical and biological) effects and human lung functionality.
3. Technical Approach
TwinAIR introduces a multi-level Digital Twin (DT) for real time air quality data utilization and response enhancement to ensure optimal air conditions comfort and wellbeing. Through the expansion of the functionality of current digital twin technology, TwinAIR represents indoor air quality in the built environment. Applications and end-user services are built on top of the TwinAIR Data Management Platform, which comprises an open-source, interoperable and secure big data management framework. A Virtual Working Space, comprised by data analytics services and digital twin-based simulation models for evaluating IAQ, as well as complex event processing mechanisms are also built on top of the Data Management Platform. These utilize an analytics service bundle comprising various AI tools utilizing Machine Learning (ML)/Deep Learning (DL) approaches as well as Big Data (BD)/Small Data (SD) concepts.
Digital IAQ Characterization