Practical Particulate Matter Sensing and Accurate Calibration System Using Low-Cost Commercial Sensors

Air pollution is a social problem, because the harmful suspended materials can cause diseases and deaths to humans. Specifically, particulate matters (PM), a form of air pollution, can contribute to cardiovascular morbidity and lung diseases. Nowadays, humans are exposed to PM pollution everywhere because it occurs in both indoor and outdoor environments. To purify or ventilate polluted air, one need to accurately monitor the ambient air quality. Therefore, this study proposed a practical particulate matter sensing and accurate calibration system using low-cost commercial sensors. The proposed system basically uses noisy and inaccurate PM sensors to measure the ambient air pollution. This paper mainly deals with three types of error caused in the light scattering method: short-term noise, part-to-part variation, and temperature and humidity interferences. We propose a simple short-term noise reduction method to correct measurement errors, an auto-fitting calibration for part-to-part repeatability to pinpoint the baseline of the signal that affects the performance of the system, and a temperature and humidity compensation method. This paper also contains the experiment setup and performance evaluation to prove the superiority of the proposed methods. Based on the evaluation of the performance of the proposed system, part-to-part repeatability was less than 2 mu g/m(3) and the standard deviation was approximately 1.1 mu g/m(3) in the air. When the proposed approaches are used for other optical sensors, it can result in better performance.

Automated summary

Air pollution is a social problem that can lead to health issues for humans, thus accurate monitoring of air quality is crucial. This study proposed a particulate matter sensing and calibration system using low-cost sensors to address measurement errors and environmental interferences effectively.