Evaluating Indoor Air Quality Monitoring Devices for Healthy Homes

Abstract

In light of COVID-19, people are increasingly anxious about indoor air quality data in places where they live and work. Access to this data using a consumer-grade air quality monitor has become a way of giving agency to building users so that they can understand the ventilation effectiveness of the spaces where they spend their time. Methods: Fourteen low-cost, air quality devices marketed to consumers were tested (seven types, two of each product): AirBird, Airthings View Plus, Aranet4 Home, Awair Omni, Eve Room, Laser Egg + CO2, and Purple Air PA-1. The study focus was accuracy and useability using three methods: a low-cost laboratory setting to test accuracy for CO2; a comparison to a calibrated, research grade meter for particulate matter (PM2.5), temperature, and relative humidity; and short-term field testing in a residential environment to understand the quality of feedback given to users. Results: Relating to accuracy, all devices were within acceptable ranges for temperature, relative humidity, and CO2, and only one brand’s results met the accuracy threshold with the research grade monitor when testing PM2.5. In terms of usability, a significant variation in response time and data visualization was found on the devices or in the smartphone applications. Conclusions: While accuracy in IAQ data is important, in low-cost air quality devices marketed to consumers it is just as important that the data be presented in a way that can be used to empower people to make decisions and modify their indoor environment. We concluded that response time, user-interface, data sharing, and visualization are important parameters that may be overlooked if a study just focuses on accuracy. The design of the device, including its appearance, size, portability, screen brightness, and sound or light warning, must also be considered. The act of measuring is important, and more studies should focus on how users interpret and react to building performance data.

IAQ Prediction in Apartments Using Machine Learning Techniques and Sensor Data

“…If confirmed, one MLT model could be developed to predict IAQ in multiple flats. 4. The temporal variation patterns of one air parameter in two apartments are distinct.…”

“…Continuous data streams for real-time analysis may be easily delivered. As a result of advances in gas sensing technology, sensor devices have become smaller, less expensive, and widely available [4]. Low-cost measurement devices provide users with a simple and quick way to determine the levels of some air pollutants and/or environmental factors.…”