Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration: A Survey and Critical Analysis

TL;DR

This paper surveys low-cost outdoor air quality sensors, their calibration challenges, and the application of machine learning techniques to improve their accuracy, highlighting open research issues and future directions.

Contribution

It provides a comprehensive review of sensor technologies, calibration methods, and identifies key challenges and future research directions in low-cost air quality monitoring.

Findings

Machine learning improves sensor calibration accuracy.

Low-cost sensors face cross-sensitivity and environmental effects.

Calibration techniques are evolving with new research.

Abstract

The significance of air pollution and the problems associated with it are fueling deployments of air quality monitoring stations worldwide. The most common approach for air quality monitoring is to rely on environmental monitoring stations, which unfortunately are very expensive both to acquire and to maintain. Hence environmental monitoring stations are typically sparsely deployed, resulting in limited spatial resolution for measurements. Recently, low-cost air quality sensors have emerged as an alternative that can improve the granularity of monitoring. The use of low-cost air quality sensors, however, presents several challenges: they suffer from cross-sensitivities between different ambient pollutants; they can be affected by external factors, such as traffic, weather changes, and human behavior; and their accuracy degrades over time. Periodic re-calibration can improve the accuracy of low-cost sensors, particularly with machine-learning-based calibration, which has shown great promise due to its capability to calibrate sensors in-field. In this article, we survey the rapidly growing research landscape of low-cost sensor technologies for air quality monitoring and their calibration using machine learning techniques. We also identify open research challenges and present directions for future research.