Characterisation and calibration of low-cost PM sensors at high temporal resolution to reference-grade performance

Bulot, Florentin M. J.; Ossont, Steven J.; Morris, Andrew K. R.; Basford, Philip J.; Easton, Natasha H. C.; Mitchell, Hazel L.; Foster, Gavin L; Cox, Simon J.; Loxham, Matthew

doi:10.1016/j.heliyon.2023.e15943

Cited by 8 publications

(7 citation statements)

References 36 publications

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“…The residual heat generated by the electronics and the sensors inside the air quality monitors means that the RH and temperature condition inside the air quality monitor are higher than the conditions surrounding the air quality monitors. For instance, in one of our previous studies, we noticed the RH was 15% lower inside the air quality monitor than outside [12]. It is not clear whether the particles have sufficient time to adjust to these conditions before being measured by the sensors.…”

Section: Experimental Conditionsmentioning

confidence: 84%

“…Indeed, in Delhi, India, Hagan et al [11] used the first three size ranges of an Alphasense OPC-N2, in conjunction with data on other air pollutants (CO, NO 2 , SO 2 , O 3 ) to successfully identify sources of pollution using positive matrix factorisation. Additionally, we previously demonstrated reference-grade improvements to the performances of Plantower PMS5003 and Sensirion SPS30 through calibration methods based on the PNCs reported by these sensors [12]. Similarly, Wallace et al [13,14] developed a calibration method using the PNCs reported by the Plantower PMS5003 that outperformed calibration methods based on mass concentrations.…”

Section: Introductionmentioning

confidence: 90%

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Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution—Part B—Particle Number Concentrations

Bulot,

Russell,

Rezaei

et al. 2023

Sensors

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Low-cost Particulate Matter (PM) sensors offer an excellent opportunity to improve our knowledge about this type of pollution. Their size and cost, which support multi-node network deployment, along with their temporal resolution, enable them to report fine spatio-temporal resolution for a given area. These sensors have known issues across performance metrics. Generally, the literature focuses on the PM mass concentration reported by these sensors, but some models of sensors also report Particle Number Concentrations (PNCs) segregated into different PM size ranges. In this study, eight units each of Alphasense OPC-R1, Plantower PMS5003 and Sensirion SPS30 have been exposed, under controlled conditions, to short-lived peaks of PM generated using two different combustion sources of PM, exposing the sensors’ to different particle size distributions to quantify and better understand the low-cost sensors performance across a range of relevant environmental ranges. The PNCs reported by the sensors were analysed to characterise sensor-reported particle size distribution, to determine whether sensor-reported PNCs can follow the transient variations of PM observed by the reference instruments and to determine the relative impact of different variables on the performances of the sensors. This study shows that the Alphasense OPC-R1 reported at least five size ranges independently from each other, that the Sensirion SPS30 reported two size ranges independently from each other and that all the size ranges reported by the Plantower PMS5003 were not independent of each other. It demonstrates that all sensors tested here could track the fine temporal variation of PNCs, that the Alphasense OPC-R1 could closely follow the variations of size distribution between the two sources of PM, and it shows that particle size distribution and composition are more impactful on sensor measurements than relative humidity.

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Section: Experimental Conditionsmentioning

confidence: 84%

Section: Introductionmentioning

confidence: 90%

Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution—Part B—Particle Number Concentrations

Bulot,

Russell,

Rezaei

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

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“…Another challenge includes the need for sensor maintenance, typically involving recalibration approximately twice a year [57]. Additionally, many of these techniques address mass concentration, aligning with the focus of our study.…”

Section: Calibration Challengesmentioning

confidence: 96%

AirMLP: A Multilayer Perceptron Neural Network for Temporal Correction of PM2.5 Values in Turin

Casari,

Po,

Zini

2023

Sensors

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In recent times, pollution has emerged as a significant global concern, with European regulations stipulating limits on PM 2.5 particle levels. Addressing this challenge necessitates innovative approaches. Smart low-cost sensors suffer from imprecision, and can not replace legal stations in terms of accuracy, however, their potential to amplify the capillarity of air quality evaluation on the territory is not under discussion. In this paper, we propose an AI system to correct PM 2.5 levels in low-cost sensor data. Our research focuses on data from Turin, Italy, emphasizing the impact of humidity on low-cost sensor accuracy. In this study, different Neural Network architectures that vary the number of neurons per layer, consecutive records and batch sizes were used and compared to gain a deeper understanding of the network’s performance under various conditions. The AirMLP7-1500 model, with an impressive R-squared score of 0.932, stands out for its ability to correct PM 2.5 measurements. While our approach is tailored to the city of Turin, it offers a systematic methodology for the definition of those models and holds the promise to significantly improve the accuracy of air quality data collected from low-cost sensors, increasing the awareness of citizens and municipalities about this critical environmental information.

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“…Studies have also pointed out biases related to relative humidity and high ambient PM levels, and suggested various approaches to correct the sensor's concentration outputs (Bulot et al, 2023;Chu et al, 2020). The sensor's poor size selectivity, performance dependence on ambient particle size distributions, and non-linear response to elevated PM concentrations have also been revealed (Delp & Singer, 2020).…”

Section: Application Of Calibration Equationsmentioning

confidence: 99%

Evaluating Performance of Socio-Technical Systems for Improved Reliability: Evidence from Sensor and Human Networks

2023

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Air pollution, especially PM2.5 (aerosols < 2.5μm diameter), contributes to 8-10 million early deaths annually. The WHO's 2021 guidelines on PM2.5 exposure show 97.3% of people globally live beyond safe exposure levels, reducing global average life expectancy by 2.2 years. This dissertation investigates the efficacy of IoT-based low-cost sensors in delivering credible, localized pollution data and fostering public environmental advocacy. Three essays structure the exploration:Essay 1 reviews the evolution and performance of low-cost PM sensors (from 2012-2022) and highlights the efficacy of appropriate calibration models to improve performance.

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Characterisation and calibration of low-cost PM sensors at high temporal resolution to reference-grade performance

Cited by 8 publications

References 36 publications

Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution—Part B—Particle Number Concentrations

Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution—Part B—Particle Number Concentrations

AirMLP: A Multilayer Perceptron Neural Network for Temporal Correction of PM2.5 Values in Turin

Evaluating Performance of Socio-Technical Systems for Improved Reliability: Evidence from Sensor and Human Networks

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