Calibrating Low-Cost Air Quality Sensors for High Accuracy PM2.5 Measurements Using Machine and Deep Learning, Enabling Monitoring of Air Quality for a Wider Range of Geographic Regions

Abstract

In recent years, air quality has decreased substantially because of increasing levels of harmful pollutants from various industrial practices, vehicle emissions, and wildfires. This has raised public health concerns, especially those around respiratory conditions. Particulate matter with diameters less than 2.5 µm or PM2.5 is considered a leading air pollutant, causing over 8 million deaths annually worldwide. Hence, accurate monitoring and prediction of air pollution, specifically PM2.5, is essential for public health protection; however, current air quality monitoring methods face limitations. Currently, air quality data is collected from ground-level monitoring stations, which utilize Environmental Protection Agency (EPA) sensors. While these sensors provide accurate data, their high cost prevents them from widespread use, restricting the geographic regions that can be monitored. On the other hand, low-cost air quality sensors can be distributed across different areas and fill geographic gaps in sensor coverage, but they are not very accurate. This trade-off between cost and accuracy presents a challenge in effectively monitoring widespread air quality. To address this challenge, this study presents a new approach to improving the accuracy of air quality measurements of low-cost sensors by calibrating them using various machine learning (ML) and deep learning (DL) techniques, allowing them to attain the high accuracy of EPA sensors while still remaining inexpensive. ML/DL models were trained on spatiotemporal data that was collected from ground-level monitoring stations, including PM2.5 levels, temperature, and humidity from the low-cost sensors and PM2.5 levels from the EPA sensors. Then, using these models, the temperature, humidity, and PM2.5 levels from the low-cost sensors were used to predict their corresponding highly accurate PM2.5 measurements from EPA sensors with RSMEs as low as 5.53. Better accuracy in low-cost sensors can lead to more extensive and reliable air quality monitoring networks, providing real-time data that can be used for public health protection. This approach can be applied globally, enabling better response strategies to air pollution and its associated health risks.