Improving data reliability: A quality control practice for low-cost PM2.5 sensor network.

Abstract

Low-cost air quality sensor networks have been increasingly used for high spatial resolution air quality monitoring in recent years. Ensuring data reliability during continuous operation is critical for these sensor networks. Using particulate matter sensor as an example, this study reports a data quality control method, including sensor selection, pre-calibration, and online inspection. It was used in developing and operating the dense low-cost particle sensor networks in two Chinese cities. Firstly, seven mainstream sensors were tested and one model of particle sensor was selected due to its better linearity and stability. For a batch of sensors of the same model, although they were calibrated after manufactured, there are differences in response toward the same concentration of pollutants. The systematical variation of sensors was corrected and unified through pre-calibration. After deploying them in the field, a data analysis method is established for online inspecting their working status. Using data from these sensors, it evaluates parameters such as intraclass correlation coefficients and normalized root mean square error. These two metrics help to construct a two-dimensional coordinate system and to classify sensors into four status, including normal, fluctuation, hotspots, and malfunction. During a one-month operation in the two cities, 8 (out of 82) and 10 (out of 59) sensors with suspected malfunctions were screened out for further on-site inspection. Moreover, the sensor networks show potential in identifying illegal emission sources that cannot be typically detected by sparse regulatory air quality monitoring stations.