AI as a catalyst for synergistic gains in indoor air quality and energy efficiency

Milana Trounce; Darin Anderson; William Bahnfleth; Liam Bates; Seema Bhangar; Rob Bolin; Wenhao Chen; Sabrina Chwalek; Scott Frank; Jason Hartke; Kazukiyo Kumagai; Georgia Lagoudas; Erik Malmstrom; Sean McCrady; Corey Metzger; Aleksander Mikszewski; Daniel Nall; Brendan Owens; John Salas; Steve Taylor; Walt Vernon; Lidia Morawska

doi:10.1016/j.buildenv.2025.114069

AI as a catalyst for synergistic gains in indoor air quality and energy efficiency

Milana Trounce
, Darin Anderson
, William Bahnfleth
, Liam Bates
, Seema Bhangar
, Rob Bolin
, Wenhao Chen
, Sabrina Chwalek
, Scott Frank
, Jason Hartke
, Kazukiyo Kumagai
, Georgia Lagoudas
, Erik Malmstrom
, Sean McCrady
, Corey Metzger
, Aleksander Mikszewski
, Daniel Nall
, Brendan Owens
, John Salas
, Steve Taylor

Walt Vernon, Lidia Morawska

Architectural Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

Indoor air quality (IAQ) and energy efficiency are often perceived as competing priorities in building operation. However, artificial intelligence (AI) offers tools that may synergistically optimize both, but its promise must be weighed against challenges in deployment and management. Drawing on insights from the Stanford IAQ Forum, ASHRAE Guideline 36, and emerging AI deployments in HVAC optimization, this paper explores how AI-enabled control systems can enhance IAQ while reducing energy waste. By leveraging high-frequency sensor data and standardized control sequences, AI can unlock real-time optimization, fault detection, and adaptive performance. This approach supports the implementation of IAQ performance standards without sacrificing sustainability or cost-effectiveness. Interim, scalable approaches are needed, as broad adoption faces technical, economic, and organizational barriers.

Original language	English (US)
Article number	114069
Journal	Building and Environment
Volume	289
DOIs	https://doi.org/10.1016/j.buildenv.2025.114069
State	Published - Feb 1 2026

All Science Journal Classification (ASJC) codes

Environmental Engineering
Civil and Structural Engineering
Geography, Planning and Development
Building and Construction

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.buildenv.2025.114069

Cite this

Trounce, M., Anderson, D., Bahnfleth, W., Bates, L., Bhangar, S., Bolin, R., Chen, W., Chwalek, S., Frank, S., Hartke, J., Kumagai, K., Lagoudas, G., Malmstrom, E., McCrady, S., Metzger, C., Mikszewski, A., Nall, D., Owens, B., Salas, J., ... Morawska, L. (2026). AI as a catalyst for synergistic gains in indoor air quality and energy efficiency. Building and Environment, 289, Article 114069. https://doi.org/10.1016/j.buildenv.2025.114069

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title = "AI as a catalyst for synergistic gains in indoor air quality and energy efficiency",

abstract = "Indoor air quality (IAQ) and energy efficiency are often perceived as competing priorities in building operation. However, artificial intelligence (AI) offers tools that may synergistically optimize both, but its promise must be weighed against challenges in deployment and management. Drawing on insights from the Stanford IAQ Forum, ASHRAE Guideline 36, and emerging AI deployments in HVAC optimization, this paper explores how AI-enabled control systems can enhance IAQ while reducing energy waste. By leveraging high-frequency sensor data and standardized control sequences, AI can unlock real-time optimization, fault detection, and adaptive performance. This approach supports the implementation of IAQ performance standards without sacrificing sustainability or cost-effectiveness. Interim, scalable approaches are needed, as broad adoption faces technical, economic, and organizational barriers.",

author = "Milana Trounce and Darin Anderson and William Bahnfleth and Liam Bates and Seema Bhangar and Rob Bolin and Wenhao Chen and Sabrina Chwalek and Scott Frank and Jason Hartke and Kazukiyo Kumagai and Georgia Lagoudas and Erik Malmstrom and Sean McCrady and Corey Metzger and Aleksander Mikszewski and Daniel Nall and Brendan Owens and John Salas and Steve Taylor and Walt Vernon and Lidia Morawska",

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Trounce, M, Anderson, D, Bahnfleth, W, Bates, L, Bhangar, S, Bolin, R, Chen, W, Chwalek, S, Frank, S, Hartke, J, Kumagai, K, Lagoudas, G, Malmstrom, E, McCrady, S, Metzger, C, Mikszewski, A, Nall, D, Owens, B, Salas, J, Taylor, S, Vernon, W & Morawska, L 2026, 'AI as a catalyst for synergistic gains in indoor air quality and energy efficiency', Building and Environment, vol. 289, 114069. https://doi.org/10.1016/j.buildenv.2025.114069

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T1 - AI as a catalyst for synergistic gains in indoor air quality and energy efficiency

AU - Trounce, Milana

AU - Anderson, Darin

AU - Bahnfleth, William

AU - Bates, Liam

AU - Bhangar, Seema

AU - Bolin, Rob

AU - Chen, Wenhao

AU - Chwalek, Sabrina

AU - Frank, Scott

AU - Hartke, Jason

AU - Kumagai, Kazukiyo

AU - Lagoudas, Georgia

AU - Malmstrom, Erik

AU - McCrady, Sean

AU - Metzger, Corey

AU - Mikszewski, Aleksander

AU - Nall, Daniel

AU - Owens, Brendan

AU - Salas, John

AU - Taylor, Steve

AU - Vernon, Walt

AU - Morawska, Lidia

PY - 2026/2/1

Y1 - 2026/2/1

N2 - Indoor air quality (IAQ) and energy efficiency are often perceived as competing priorities in building operation. However, artificial intelligence (AI) offers tools that may synergistically optimize both, but its promise must be weighed against challenges in deployment and management. Drawing on insights from the Stanford IAQ Forum, ASHRAE Guideline 36, and emerging AI deployments in HVAC optimization, this paper explores how AI-enabled control systems can enhance IAQ while reducing energy waste. By leveraging high-frequency sensor data and standardized control sequences, AI can unlock real-time optimization, fault detection, and adaptive performance. This approach supports the implementation of IAQ performance standards without sacrificing sustainability or cost-effectiveness. Interim, scalable approaches are needed, as broad adoption faces technical, economic, and organizational barriers.

AB - Indoor air quality (IAQ) and energy efficiency are often perceived as competing priorities in building operation. However, artificial intelligence (AI) offers tools that may synergistically optimize both, but its promise must be weighed against challenges in deployment and management. Drawing on insights from the Stanford IAQ Forum, ASHRAE Guideline 36, and emerging AI deployments in HVAC optimization, this paper explores how AI-enabled control systems can enhance IAQ while reducing energy waste. By leveraging high-frequency sensor data and standardized control sequences, AI can unlock real-time optimization, fault detection, and adaptive performance. This approach supports the implementation of IAQ performance standards without sacrificing sustainability or cost-effectiveness. Interim, scalable approaches are needed, as broad adoption faces technical, economic, and organizational barriers.

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DO - 10.1016/j.buildenv.2025.114069

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VL - 289

JO - Building and Environment

JF - Building and Environment

M1 - 114069

ER -

AI as a catalyst for synergistic gains in indoor air quality and energy efficiency

Abstract

All Science Journal Classification (ASJC) codes

UN SDGs

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