Evaluation of two low-cost PM monitors under different laboratory and indoor conditions

He, Ruikang; Han, Taewon; Bachman, Daniel; Carluccio, Dominick J; Jaeger, Rudolph J.; Zhang, Jie; Thirumurugesan, Sanjeevi; Andrews, Clinton J.; Mainelis, Gediminas

doi:10.1080/02786826.2020.1851649

Cited by 11 publications

(5 citation statements)

References 59 publications

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“…The DustTrak sensor offers simultaneous measurements of PM concentrations for different particle sizes (PM 1 , PM 2.5 , PM 3 , PM 10 , and total particles) [ 28 ]. The DustTrak is widely used in measuring PM concentrations in indoor and outdoor environments and evaluating low-cost PM sensors [ 29 , 30 , 31 , 32 , 33 , 34 ].…”

Section: Methodsmentioning

confidence: 99%

Field Evaluation and Calibration of Low-Cost Air Pollution Sensors for Environmental Exposure Research

Huang

Kwan

Cai

et al. 2022

Sensors

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This paper seeks to evaluate and calibrate data collected by low-cost particulate matter (PM) sensors in different environments and using different aggregated temporal units (i.e., 5-s, 1-min, 10-min, 30 min intervals). We first collected PM concentrations (i.e., PM1, PM2.5, and PM10) data in five different environments (i.e., indoor and outdoor of an office building, a train platform and lobby of a subway station, and a seaside location) in Hong Kong, using five AirBeam2 sensors as the low-cost sensors and a TSI DustTrak DRX Aerosol Monitor 8533 as the reference sensor. By comparing the collected PM concentrations, we found high linearity and correlation between the data reported by the AirBeam2 sensors in different environments. Furthermore, the results suggest that the accuracy and bias of the PM data reported by the AirBeam2 sensors are affected by rainy weather and environments with high humidity and a high level of hygroscopic salts (i.e., a seaside location). In addition, increasing the aggregation level of the temporal units (i.e., from 5-s to 30 min intervals) increases the correlation between the PM concentrations obtained by the AirBeam2 sensors, while it does not significantly improve the accuracy and bias of the data. Lastly, our results indicate that using a machine learning model (i.e., random forest) for the calibration of PM concentrations collected on sunny days generates better results than those obtained with multiple linear models. These findings have important implications for researchers when designing environmental exposure studies based on low-cost PM sensors.

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Section: Methodsmentioning

confidence: 99%

Field Evaluation and Calibration of Low-Cost Air Pollution Sensors for Environmental Exposure Research

Huang

Kwan

Cai

et al. 2022

Sensors

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“…51–53 In recent studies, the AirVisual Pro has been shown to be a reliable AQM with ease of access and better accuracy in indoor environments. 48,55…”

Section: Methodsmentioning

confidence: 99%

“…[51][52][53] In recent studies, the AirVisual Pro has been shown to be a reliable AQM with ease of access and better accuracy in indoor environments. 48,55 The PAC used in this study (EJ120, Oransi, Raleigh, US) uses a combination of an activated carbon lter and a MERV 17 (Minimum Efficiency Reporting Value) lter to provide a maximum air ow of 330 cubic feet per min (0.16 m 3 s À1 ). It is recommended for rooms sizes up to 116 m 2 with 2 air changes per hour.…”

Section: Instrumentationmentioning

confidence: 99%

Assessment of PM_2.5 concentrations, transport, and mitigation in indoor environments using low-cost air quality monitors and a portable air cleaner

Sankhyan

Witteman

Coyan

et al. 2022

Environ. Sci.: Atmos.

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“…The current IoT sensors for PM 2.5 have reasonable accuracy, within a factor of 2 from a reference, which suggests their suitability for indoor air quality management; however, their accuracy can be compromised if the dominant source includes particles of the ultrafine size range (Wang et al, 2015;Manikonda et al, 2016;Singer and Delp, 2018;Wang et al, 2020;Demanega et al, 2021). A more recent study showed very good agreement between low-cost IoT PM 2.5 sensors with scientific-grade instruments (Hegde et al, 2020;He et al, 2021) especially after applying RH corrections (Tagle et al, 2020;Zou et al, 2021). The CO 2 sensors generally have good agreement with reference monitors (Demanega et al, 2021).…”

Section: State-of-the-art Continuous Measurement Capabilities Indoorsmentioning

confidence: 96%

Transformational IoT sensing for air pollution and thermal exposures

Pantelic

Nazarian

Miller

et al. 2022

Front. Built Environ.

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Cities today encounter significant challenges pertaining to urbanization and population growth, resource availability, and climate change. Concurrently, unparalleled datasets are generated through Internet of Things (IoT) sensing implemented at urban, building, and personal scales that serve as a potential tool for understanding and overcoming these issues. Focusing on air pollution and thermal exposure challenges in cities, we reviewed and summarized the literature on IoT environmental sensing on urban, building, and human scales, presenting the first integrated assessment of IoT solutions from the data convergence perspective on all three scales. We identified that there is a lack of guidance on what to measure, where to measure, how frequently to measure, and standards for the acceptable measurement quality on all scales of application. The current literature review identified a significant disconnect between applications on each scale. Currently, the research primarily considers urban, building, and personal scale in isolation, leading to significant data underutilization. We addressed the scientific and technological challenges and opportunities related to data convergence across scales and detailed future directions of IoT sensing along with short- and long-term research and engineering needs. IoT application on a personal scale and integration of information on all scales opens up the possibility of developing personal thermal comfort and exposure models. The development of personal models is a vital promising area that offers significant advancements in understanding the relationship between environment and people that requires significant further research.

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Evaluation of two low-cost PM monitors under different laboratory and indoor conditions

Cited by 11 publications

References 59 publications

Field Evaluation and Calibration of Low-Cost Air Pollution Sensors for Environmental Exposure Research

Field Evaluation and Calibration of Low-Cost Air Pollution Sensors for Environmental Exposure Research

Assessment of PM_2.5 concentrations, transport, and mitigation in indoor environments using low-cost air quality monitors and a portable air cleaner

Transformational IoT sensing for air pollution and thermal exposures

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Evaluation of two low-cost PM monitors under different laboratory and indoor conditions

Cited by 11 publications

References 59 publications

Field Evaluation and Calibration of Low-Cost Air Pollution Sensors for Environmental Exposure Research

Field Evaluation and Calibration of Low-Cost Air Pollution Sensors for Environmental Exposure Research

Assessment of PM2.5 concentrations, transport, and mitigation in indoor environments using low-cost air quality monitors and a portable air cleaner

Transformational IoT sensing for air pollution and thermal exposures

Contact Info

Product

Resources

About

Assessment of PM_2.5 concentrations, transport, and mitigation in indoor environments using low-cost air quality monitors and a portable air cleaner