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Low-cost PM2.5 Sensors: An Assessment of their Suitability for Various Applications

AEROSOL AND AIR QUALITY RESEARCH(2020)

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摘要
Recently, there has been a substantial increase in the availability and use of low-cost particulate matter sensors in a wide range of air quality applications. They carry the promise of revolutionising air quality monitoring, yet considerable reservations exist regarding their performance and capabilities, thus hindering the broader-scale utilization of these devices. In order to address these concerns and assess their feasibility and accuracy for various applications, we evaluated six low-cost PM2.5 sensors (the Sharp GP2Y1010AU0F, Shinyei PPD42NS, Plantower PMS1003, Innociple PSM305, Nova SDS011 and Nova SDL607) in laboratory and field conditions using two combustion aerosols, concrete dust and ambient particles. In assessing the performance of these sensors, we focussed on indicators such as the linearity, accuracy and precision, critically differentiating between these qualities, and employed inter-comparison (the coefficient of determination, R-2). In the laboratory, all sensors responded well, with an R-2 > 0.91 when the PM2.5 concentration was > 50 mu g m(-3), as measured by the DustTrak. In particular, the PMS1003 demonstrated good accuracy and precision in both laboratory and ambient conditions. However, some limitations were noted for the tested sensors at lower concentrations. For example, the Sharp and Shinyei sensors showed poor correlations (R-2 < 0.1) with the DustTrak when the ambient PM2.5 concentration was < 20 mu g m(-3). These results suggest that the sensors should be calibrated individually for each source in the environment of their intended use. We demonstrate that when tested appropriately and used with a full understanding of their capabilities and limitations, low-cost sensors can serve as an unprecedented aid in a broad spectrum of air quality applications, including the emerging field of citizen science.
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关键词
Low-cost sensors,PM sensors,Atmospheric aerosols,Air pollution
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