Field Evaluation of Low-Cost Particulate Matter Sensors in Beijing

Han, Mei; Han, Pengfei; Wang, Yinan; Zeng, Ning; Liu, Di; Cai, Qixiang; Deng, Zhaoze; Wang, Yinghong; Pan, Yuepeng; Tang, Xiao

doi:10.3390/s20164381

Cited by 30 publications

(17 citation statements)

References 41 publications

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“…The AQY1 user guide mentioned that this effect is corrected for by way of a humidity correction algorithm; however, we still observed that the precision of the low-cost sensors decreased as RH increased; an effect which was more prominent in the calibrated datasets, and which may have been larger had there been no control for it by the manufacturer. Other studies [40] suggested an overestimation of particle concentrations when RH is high, potentially explained by the operational nature of optical particle counters and the detection and interpretation of water droplets as PM [17,20,41,42]. There are, however, studies showing negligible effects of meteorological variables on PM readings [10,16], and that the biases to RH and Temp varied across each sensor model and node; demonstrating that each sensor response is unique [10], as we found.…”

Section: Discussionsupporting

confidence: 60%

Evaluating the Performance of Low-Cost Air Quality Monitors in Dallas, Texas

Khreis

Johnson

Jack

et al. 2022

IJERPH

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The emergence of low-cost air quality sensors may improve our ability to capture variations in urban air pollution and provide actionable information for public health. Despite the increasing popularity of low-cost sensors, there remain some gaps in the understanding of their performance under real-world conditions, as well as compared to regulatory monitors with high accuracy, but also high cost and maintenance requirements. In this paper, we report on the performance and the linear calibration of readings from 12 commercial low-cost sensors co-located at a regulatory air quality monitoring site in Dallas, Texas, for 18 continuous measurement months. Commercial AQY1 sensors were used, and their reported readings of O3, NO2, PM2.5, and PM10 were assessed against a regulatory monitor. We assessed how well the raw and calibrated AQY1 readings matched the regulatory monitor and whether meteorology impacted performance. We found that each sensor’s response was different. Overall, the sensors performed best for O3 (R2 = 0.36–0.97) and worst for NO2 (0.00–0.58), showing a potential impact of meteorological factors, with an effect of temperature on O3 and relative humidity on PM. Calibration seemed to improve the accuracy, but not in all cases or for all performance metrics (e.g., precision versus bias), and it was limited to a linear calibration in this study. Our data showed that it is critical for users to regularly calibrate low-cost sensors and monitor data once they are installed, as sensors may not be operating properly, which may result in the loss of large amounts of data. We also recommend that co-location should be as exact as possible, minimizing the distance between sensors and regulatory monitors, and that the sampling orientation is similar. There were important deviations between the AQY1 and regulatory monitors’ readings, which in small part depended on meteorology, hindering the ability of the low-costs sensors to present air quality accurately. However, categorizing air pollution levels, using for example the Air Quality Index framework, rather than reporting absolute readings, may be a more suitable approach. In addition, more sophisticated calibration methods, including accounting for individual sensor performance, may further improve performance. This work adds to the literature by assessing the performance of low-cost sensors over one of the longest durations reported to date.

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Section: Discussionsupporting

confidence: 60%

Evaluating the Performance of Low-Cost Air Quality Monitors in Dallas, Texas

Khreis

Johnson

Jack

et al. 2022

IJERPH

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“…We designed two versions of motherboards for imported and domestic sensor boards, which also hold other sensors. The motherboard is connected to a microprocessor named BeagleBone Green Wireless (BBGW) through general-purpose input/output (GPIO) pins [ 38 ], and meteorological data, including temperature and relative humidity (RH), were collected from the Adafruit BME280 sensor. All data were collected with a time resolution of 2 s, then stored in the microprocessor and later uploaded to a remote server through the IoT, and data were also stored in a built-in secure digital memory (SD) card daily for backup.…”

Section: Methodsmentioning

confidence: 99%

Calibrations of Low-Cost Air Pollution Monitoring Sensors for CO, NO2, O3, and SO2

Han

Liu

et al. 2021

Sensors

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Pollutant gases, such as CO, NO2, O3, and SO2 affect human health, and low-cost sensors are an important complement to regulatory-grade instruments in pollutant monitoring. Previous studies focused on one or several species, while comprehensive assessments of multiple sensors remain limited. We conducted a 12-month field evaluation of four Alphasense sensors in Beijing and used single linear regression (SLR), multiple linear regression (MLR), random forest regressor (RFR), and neural network (long short-term memory (LSTM)) methods to calibrate and validate the measurements with nearby reference measurements from national monitoring stations. For performances, CO > O3 > NO2 > SO2 for the coefficient of determination (R2) and root mean square error (RMSE). The MLR did not increase the R2 after considering the temperature and relative humidity influences compared with the SLR (with R2 remaining at approximately 0.6 for O3 and 0.4 for NO2). However, the RFR and LSTM models significantly increased the O3, NO2, and SO2 performances, with the R2 increasing from 0.3–0.5 to >0.7 for O3 and NO2, and the RMSE decreasing from 20.4 to 13.2 ppb for NO2. For the SLR, there were relatively larger biases, while the LSTMs maintained a close mean relative bias of approximately zero (e.g., <5% for O3 and NO2), indicating that these sensors combined with the LSTMs are suitable for hot spot detection. We highlight that the performance of LSTM is better than that of random forest and linear methods. This study assessed four electrochemical air quality sensors and different calibration models, and the methodology and results can benefit assessments of other low-cost sensors.

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“…Therefore, the GP2YX was selected for use in the low-cost multimodal device, but initial testing with the GP2YX produced erratic and highly inaccurate data. Alternatively, PlanTower sensors are often used in many commercial devices [48] and have been found to report data that correlates with reference equipment when measuring PM2.5 [49]- [51]. Therefore, a (PlanTower) PMSA003 (Table 1) sensor was selected here to measure PM2.5.…”

Section: Low-cost Ieq Sensingmentioning

confidence: 99%