By: Rui Fonseca
In the 80s, when Sick Building Syndrome was firstly recognized by World Health Organization, it brought to light the impact of poor Indoor Air Quality (IAQ) on the health and well-being of occupants. Since then, we started to rethink our approach to improve building design and indoor environment – better HVAC design, increased ventilation rates and effective air filtration, IAQ guidelines / national standards, and, in the 2000s, the rise of Green Building Movements, such as LEED, and more recently, WELL certifications.
However, it was Covid-19 pandemic that brought a renewed and intensified awareness, on a global stage, of the significance of IAQ in our daily lives, emphasizing the critical importance of good IAQ – not only for comfort but also for disease prevention – making it a top priority for individuals, businesses, and institutions around the world.
With the advent of Artificial Intelligence (AI) tools now available on a worldwide scale – Chat-GPT, DALL-E, Midjourney and multiple other tools being developed in succession – AI has rapidly emerged as a revolutionary force, promising to redefine and disrupt industries globally.
On Real Estate and Construction sectors, the integration of AI in IAQ management has the potential to quickly revolutionize the way we design, construct, operate and interact with buildings, contributing to energy savings, sustainable design and to occupant well-being and health.
- AI- Monitoring, Control & Optimization:
One of the first and most straightforward application of AI applications on IAQ is monitoring and control of systems dedicated to IAQ. Machine learning algorithms can continuously analyze data parameters from sensor data on the buildings, such as temperature, humidity,CO2 levels, and volatile organic compounds (VOCs) to detect trends and anomalies in real time.
AI enables a shift from a static and reactive approach from traditional HVAC systems, who often operate on pre-defined schedules and rules (e.g., provide constant air circulation during building operation time), to a dynamic, adaptable, and immediate approach to adjust HVAC operation and resolve identified issues.
IAQ optimization is hand-in-hand associated to energy-efficiency. AI algorithms can optimize HVAC systems to maintain high standards of IAQ while minimizing energy consumption – adjust temperature control based on occupant preferences, ventilation rates based on occupancy patterns and switch between mechanical and natural ventilation based on outside air quality.
- AI-Predictive Nature:
On a second level, AI-driven IAQ systems can provide valuable insights into a building’s environmental performance. With a helicopter view over the entire building systems’ performance, as well as external environmental factors, AI-driven tools can foresee potential issues before they escalate, allowing for the early detection of system faults or deviations from optimal IAQ standards.
This is especially valuable in complex HVAC systems, where AI can identify not only irregularities in equipment performance (e.g., malfunctioning filters or ventilation inefficiencies), but also identify the root-cause of faults or sources of pollutants, whether from building materials, HVAC systems, or external factors.
This proactive approach and insightful stance on the building performance enables building owners and facility managers to optimize maintenance, servicing and replacement of equipment to avoid sudden disruptions or downtime, paving the way towards more sustainable, healthier buildings.
- AI Impact on Building Design:
AI impact is not only limited to the operational phase, but its reach extends to the design and construction phases as well. AI holds great promise in aiding architects and engineers in designing highly energy-efficient buildings, with excellent IAQ.
AI models can support initial building and HVAC design, considering parameters, such as orientation, window placement, shading and airflow pathways, to optimize natural ventilation and cooling / heating load. This has the potential to minimize energy and ventilation requirements, while ensuring correct sizing of equipment and correct distribution of fresh air across the building.
Moreover, simulated control of shades, blinds and lighting systems can help balance the need for natural light with IAQ requirements, ensuring a circadian wellness for building occupants.
Lastly, one may foresee the optimization of materials selection to lower emissions of harmful pollutants and VOCs – by utilizing AI tools to calculate the environmental impact of materials from production to disposal as well as prioritizing IAQ-friendly, sustainable materials, we can create safe and sustainable buildings, while simultaneously reducing carbon footprint across the building’s entire lifecycle.
Conclusion
The effect of AI on buildings’ IAQ is profound and multifaceted. Supported by the real-time nature of AI-driven algorithms, it ensures a safer, more comfortable indoor environment, an invaluable asset for both residential and commercial buildings, where occupant well-being is a top priority.
AI models can learn the intricate relationship between building design, occupant health and environmental sustainability to support the optimization of IAQ while enhancing energy efficiency, across all building lifecycle stages – design, construction and operation & maintenance.
In sum, AI can help shaping a healthier, more comfortable, and more sustainable future for buildings directly impacting occupants’ well-being and productivity.
