Abstract
Two low-cost fine particulate matter (PM2.5) sensor systems have been established by the government and community in Taiwan. Each system combines hundreds of PM2.5 sensors through an Internet of Things architecture. Since these sensors have not been calibrated, their performance has been questioned. In this study, the spatial interpolation data from air quality monitoring stations (AQMSs) was used to quantify the performances of the two sensor systems. The linearity, sensitivity, offset, precision, accuracy, and bias of the two sensor systems were estimated. The results indicate that the linearity of the government’s sensor system was higher than that of the community sensor system. However, the sensitivity of the government’s system was lower than that of the community system. The relative standard deviation, relative error, offset, and bias of the community sensor system were higher than those of the government sensor system. However, the government sensor system exhibited superior spatial interpolation results for the AQMS data than the community sensor system did. The precision and accuracy of the two sensor systems were poor during a period of low PM2.5 concentrations. A working platform of improvements consisting of monitoring the operation loop and automatic correction loop is proposed. The monitoring operation loop comprises five modules, namely outlier detection, temporal anomaly analysis, spatial anomaly analysis, spatiotemporal anomaly analysis, and trajectory analysis modules. The automatic correction loop contains spatial interpolation module, a sensor performance detection module, and a correction module. The proposed working platform can enhance the performance of low-cost sensor systems, especially as alert systems for reportable events.
Highlights
The equipment and operation costs of an air quality monitoring station (AQMS) are high
A working platform consisting of a monitoring operation loop and an automatic correction loop is proposed for the government’s low-cost sensor system to improve its performance, especially for air quality monitoring in industrial areas
All the ambient particulate mass monitors in the AQMS are fitted according to the US Environmental Protection Agency PM2.5 Federal Equivalent Method, which regulates the equipment architecture, analysis principle, and calibration method of ambient PM2.5 monitoring
Summary
The equipment and operation costs of an air quality monitoring station (AQMS) are high. The 1st EuNetAir Air Quality Joint Intercomparison Exercise was organized in Aveiro (Portugal) in October 2014 for evaluating and assessing different micro sensor systems The results of this exercise indicated that the overall performance of low-cost sensors depends on their characteristics and on the platform adopted [13]. Lin et al [15] proposed a multi sensor space–time data fusion framework for analyzing the data from 1176 low-cost sensors Their results indicated that reasonable and superior estimates of the spatiotemporal PM2.5 concentrations are obtained. The manufacturer of the sensors used by the people often does not indicate the calibration and quality control procedures that must be performed to maintain the sensor’s performance [18] Both low-cost sensors employ the light-scattering method to measure PM2.5 mass concentrations based on their confidential proprietary algorithm. If rainfall occurred in the first week of the even month, the week without rain was considered to be the representative week
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