Long-Term Evaluation and Calibration of Low-Cost Particulate Matter (PM) Sensor

Lee, Hoochang; Kang, Jiseock; Kim, Sungjung; Im, Yunseok; Yoo, Seung-Sung; Lee, Dongjun

doi:10.3390/s20133617

Cited by 39 publications

(29 citation statements)

References 26 publications

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“…Spinelle et al [120,121] use an ANN model based on FFNNs applied to the calibration of CO, CO 2 , NO, NO 2 and O 3 . Lee et al [71] design a combination calibration process selecting the prevailing calibration models between LR and ANN based on those two models' distribution of residuals.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration

Concas

Mineraud

Lagerspetz

et al. 2021

ACM Trans. Sen. Netw.

124

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The significance of air pollution and the problems associated with it are fueling deployments of air quality monitoring stations worldwide. The most common approach for air quality monitoring is to rely on environmental monitoring stations, which unfortunately are very expensive both to acquire and to maintain. Hence, environmental monitoring stations are typically sparsely deployed, resulting in limited spatial resolution for measurements. Recently, low-cost air quality sensors have emerged as an alternative that can improve the granularity of monitoring. The use of low-cost air quality sensors, however, presents several challenges: They suffer from cross-sensitivities between different ambient pollutants; they can be affected by external factors, such as traffic, weather changes, and human behavior; and their accuracy degrades over time. Periodic re-calibration can improve the accuracy of low-cost sensors, particularly with machine-learning-based calibration, which has shown great promise due to its capability to calibrate sensors in-field. In this article, we survey the rapidly growing research landscape of low-cost sensor technologies for air quality monitoring and their calibration using machine learning techniques. We also identify open research challenges and present directions for future research.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration

Concas

Mineraud

Lagerspetz

et al. 2021

ACM Trans. Sen. Netw.

124

View full text Add to dashboard Cite

show abstract

“…In [109], a new combined calibration method was introduced to increase LCPMS accuracy during a field colocation experiment. Specifically, a multisensor platform was developed and colocated with the governmental BAM at the government station (Dongjak-gu, Seoul, Korea) to evaluate low-cost light scattering PM2.5 sensors (Beijing Plantower PMS7003).…”

Section: Calibration Of a Low-cost Pmsmentioning

confidence: 99%

A Review of Low-Cost Particulate Matter Sensors from the Developers’ Perspectives

Alfano

Barretta

Giudice

et al. 2020

Sensors

117

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The concerns related to particulate matter’s health effects alongside the increasing demands from citizens for more participatory, timely, and diffused air quality monitoring actions have resulted in increasing scientific and industrial interest in low-cost particulate matter sensors (LCPMS). In the present paper, we discuss 50 LCPMS models, a number that is particularly meaningful when compared to the much smaller number of models described in other recent reviews on the same topic. After illustrating the basic definitions related to particulate matter (PM) and its measurements according to international regulations, the device’s operating principle is presented, focusing on a discussion of the several characterization methodologies proposed by various research groups, both in the lab and in the field, along with their possible limitations. We present an extensive review of the LCPMS currently available on the market, their electronic characteristics, and their applications in published literature and from specific tests. Most of the reviewed LCPMS can accurately monitor PM changes in the environment and exhibit good performances with accuracy that, in some conditions, can reach R2 values up to 0.99. However, such results strongly depend on whether the device is calibrated or not (using a reference method) in the operative environment; if not, R2 values lower than 0.5 are observed.

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“…Various methods such as gravimetric method, beta-ray-based measurement method, resonance frequency measurement method, and light scattering method for measuring particulates have been proposed [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. In the case of gravimetric method [ 16 ], it is used as a standard measurement method because it has the best measurement accuracy.…”

Section: Introductionmentioning

confidence: 99%

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

Cho

Baek

2021

Sensors

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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 μg/m3 and the standard deviation was approximately 1.1 μg/m3 in the air. When the proposed approaches are used for other optical sensors, it can result in better performance.

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Long-Term Evaluation and Calibration of Low-Cost Particulate Matter (PM) Sensor

Cited by 39 publications

References 26 publications

Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration

Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration

A Review of Low-Cost Particulate Matter Sensors from the Developers’ Perspectives

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

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