• GitHub
  • Home
  • People
  • Research
  • Publications
  • Courses
  • News
  • Contact
  • Internal
UT Shield

Intelligent Environments Laboratory

The University of Texas at Austin
  • Home
  • People
    • Prof. Zoltan Nagy, PhD
    • June Young Park
    • José Ramón Vázquez-Canteli
    • Megan K. McHugh, MSE
    • Ayşegül Demir Dilsiz
    • Hagen Fritz
  • Research
  • Publications
  • GitHub
  • Courses
  • News
  • Contact

February 21, 2022, Filed Under: News

New Paper: Sleep Quality vs Air Quality

We have a new paper out in Building & Environment as part of the Whole Communites – Whole Health Grand Challeng at UT Austin.

Hagen Fritz, Kerry Kinney, Congyu Wu, David Schnyer and Zoltan Nagy Data fusion of mobile and environmental sensing devices to understand the effect of the indoor environment on measured and self-reported sleep quality

https://doi.org/10.1016/j.buildenv.2022.108835

Abtract: The Indoor Air Quality (Indoor Air Quality (IAQ)) of the bedroom environment has recently garnered attention since air pollution can affect sleep. Previous studies investigated IAQ and sleep quality in controlled environments which impacts both self-reported and measured sleep quality. Studies within a participant’s home environment are ecologically valid and reduce participant bias. Here, we study 20 participants over 2.5 months in Austin, TX. We monitored five components of IAQ using the BEVO Beacon, a calibrated purpose-built environmental monitor, and measured participant sleep quality through wearable activity trackers and 4-question surveys sent four times a week. We found significant decreases in sleep quality during nights with elevated CO, CO2, and temperature. Elevated CO was associated with a mean increase in 0.9 self-reported awakenings and decreases in device-measured sleep time of 21.6 min and sleep efficiency of 0.6%. Increased COand temperature were associated with decreases in device-measured sleep time of 17.5 and 15.2 min, respectively. Elevated PM2.5 and TVOCs concentrations were associated with overall improvements in sleep quality. Participants reported a mean of 4.4 fewer awakenings and had a 1.1% increased in measured sleep efficiency for nights with elevated PM2.5. Elevated TVOCs were associated with an increase in sleep time of 14.5 min. These findings indicate a need to study the relationship between these aggregate IAQ measures and sleep quality more closely. Our results also indicate that pollutants can independently affect sleep quality regardless of the CO2 measurements. Compared to literature, our study is the longest and includes the most IAQ parameters.

Research Highlight

UT Energy App – Privacy Policy

  The Intelligent Environments Laboratory is releasing the first version of the UT Energy App. This app is intended to provide a way to Read more 

About Us

The Intelligent Environments Laboratory (IEL), led by Prof. Zoltán Nagy, is an interdisciplinary research group within the Building Energy & Environments (BEE) and Sustainable Systems (SuS) Programs of the Department of Civil, Architectural and Environmental Engineering (CAEE) in the Cockrell School of Engineering of the University of Texas at Austin.

The aim of our research is to rethink the built environment and define Smart Buildings and Cities as spaces that adapt to their occupants and reduce their energy consumption.

We combine data science with building science and apply machine learning to the building and urban scale

Take a look at our projects !

Tags

air handling unit Annex 79 architecture artificial neural network Bluetooth city learn Community engaged research earthquakes environmental monitoring fault detection and diagnostics HVAC integrated design intelligent energy management Lighting Control machine learning Megan McHugh multi-agent systems Occupancy Occupant Centered Control Reinforcement Learning Review Smart Building smart city teaching Thermal Comfort
Tweets by Z0ltanNagy

Research

  • All Projects

UT Energy App – Privacy Policy

Fault detection and diagnostics of air handling units using machine learning and expert rule-sets

Reinforcement Learning in the Built Environment

Reinforcement learning for urban energy systems & demand response

Multi-Agent Reinforcement Learning for demand response & building coordination

IEA-EBC Annex 79: Occupant Centric Design and Operation of Buildings

People

  • Prof. Zoltan Nagy, PhD
  • June Young Park
  • José Ramón Vázquez-Canteli
  • Megan K. McHugh, MSE

Tags

air handling unit Annex 79 architecture artificial neural network Bluetooth city learn Community engaged research earthquakes environmental monitoring fault detection and diagnostics HVAC integrated design intelligent energy management Lighting Control machine learning Megan McHugh multi-agent systems Occupancy Occupant Centered Control Reinforcement Learning Review Smart Building smart city teaching Thermal Comfort
ITS

301 E Dean Keeton St
Austin, TX 78712
512-555-5555
nagy@utexas.edu

UT Home | Emergency Information | Site Policies | Web Accessibility | Web Privacy | Adobe Reader

© The University of Texas at Austin 2022

  • All Projects