Real-Time Machine Learning for Air Quality and Environmental Noise Detection
Sayed Khushal Shah, Zeenat Tariq, Jeehwan Lee, Yugyung Lee
Abstract
In metropolitan cities, outdoor air pollution and ambient outdoor noise transmission are significant environmental hazards, degrading indoor environmental quality. Natural ventilation is often a viable option for diluting indoor air pollutants, but transportation noise transmission is a crucial ecological conflict. Therefore, personal control over the concentration of outdoor/indoor air pollutants and noise levels is a significant threshold for natural ventilation availability. This study proposes real-time data detection and notification solutions using sensors and artificial intelligence (AI) to improve indoor air quality and outdoor air quality and outdoor noise transmission. An intelligent real-time detection and notification system was implemented in a distributed computing framework with cloud and edge computing. The objective of this study is placed on three facets: (1) development of sensors and NVIDIA Jetson Nano prototype for air quality and noise level detection, (2) application of machine learning for air quality and noise level prediction and classification, and (3) web interface for real-time monitoring and prediction for air quality and noise level detection. The results showed that the proposed user interface provides building occupants with real-time data of outdoor/indoor Air Quality Index (AQI) and noise levels for the optimized occupant control over Indoor Air Quality (IAQ). The personal control over indoor environmental quality (IEQ) enables occupants to promote natural ventilation behaviors and integrate with the existing building system on optimized IEQ by interacting with AI-based real-time data.