An air quality digital twin for real-time outdoor air quality monitoring and prediction

TY - JOUR

T1 - An air quality digital twin for real-time outdoor air quality monitoring and prediction

AU - Li, Yitong

AU - Josephs, Holly

AU - Wu, Yaocheng

AU - Mainelis, Gediminas

AU - Andrews, Clinton

AU - Gong, Jie

N1 - Publisher Copyright: © 2025

PY - 2026/2/15

Y1 - 2026/2/15

N2 - Respiratory health is closely tied to air quality, making it essential to measure and model air quality. Digital twins provide a powerful approach for air quality monitoring and prediction due to their ability to integrate real-time data, generate air quality predictions, and provide actionable insights. In the City of Elizabeth, New Jersey, poor air quality is driven by heavy industrial activities, dense traffic on major highways, and emissions from the nearby marine terminals and Newark Liberty International Airport. To tackle these issues, this research aimed to develop a digital twin for air quality monitoring and management for the City of Elizabeth. Using LiDAR scans of Housing Authority buildings, Building Information Models (BIM) were created to digitally represent physical structures. A network of outdoor sensors was deployed to capture real-time data on pollutants, including particulate matter (PM₂.₅) and ozone (O3). Unlike traditional physics-based air quality models that rely on complex mathematical equations and require significant computational resources, this study employed a data-driven approach. By analyzing spatial and temporal patterns in air quality data, this method efficiently generated real-time air quality predictions. Integrating these predictions into digital twins enhances our understanding of air quality dynamics and enables stakeholders to communicate complex information effectively to the public. Furthermore, residents can make informed choices to improve their living conditions, such as determining the best times to open windows, use air filtration systems, or spend time in outdoor environment.

AB - Respiratory health is closely tied to air quality, making it essential to measure and model air quality. Digital twins provide a powerful approach for air quality monitoring and prediction due to their ability to integrate real-time data, generate air quality predictions, and provide actionable insights. In the City of Elizabeth, New Jersey, poor air quality is driven by heavy industrial activities, dense traffic on major highways, and emissions from the nearby marine terminals and Newark Liberty International Airport. To tackle these issues, this research aimed to develop a digital twin for air quality monitoring and management for the City of Elizabeth. Using LiDAR scans of Housing Authority buildings, Building Information Models (BIM) were created to digitally represent physical structures. A network of outdoor sensors was deployed to capture real-time data on pollutants, including particulate matter (PM₂.₅) and ozone (O3). Unlike traditional physics-based air quality models that rely on complex mathematical equations and require significant computational resources, this study employed a data-driven approach. By analyzing spatial and temporal patterns in air quality data, this method efficiently generated real-time air quality predictions. Integrating these predictions into digital twins enhances our understanding of air quality dynamics and enables stakeholders to communicate complex information effectively to the public. Furthermore, residents can make informed choices to improve their living conditions, such as determining the best times to open windows, use air filtration systems, or spend time in outdoor environment.

KW - Air quality monitoring and prediction

KW - Bayesian approach

KW - Digital twin

KW - Sensor network

KW - Spatiotemporal modeling

UR - https://www.scopus.com/pages/publications/105025196638

UR - https://www.scopus.com/pages/publications/105025196638#tab=citedBy

U2 - 10.1016/j.buildenv.2025.114127

DO - 10.1016/j.buildenv.2025.114127

M3 - Article

SN - 0360-1323

VL - 290

JO - Building and Environment

JF - Building and Environment

M1 - 114127

ER -