Low-Cost Outdoor Air Quality Monitoring and In-Field Sensor Calibration.

The significance of air pollution and problems associated with it is 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 typically are deployed sparsely, resulting in limited spatial resolution for measurements. Recently, low-cost air quality sensors have emerged as an alternative that can improve 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. The accuracy of low-cost sensors can be improved through periodic re-calibration with particularly machine learning based calibration having 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.