Performance evaluation of twelve low-cost PM2.5 sensors at an ambient air monitoring site

Feenstra, Brandon; Papapostolou, Vasileios; Hasheminassab, Sina; Zhang, Hang; Boghossian, Berj Der; Cocker, David R.; Polidori, Andrea

doi:10.1016/j.atmosenv.2019.116946

Cited by 179 publications

(137 citation statements)

References 33 publications

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“…Overall, the study generated a high data recovery rate of regular measurements of airborne PM 2.5 concentrations with a granularity of 1–2 min over one month; only one of the 13 sites was unable to collect data due to a technical problem. Our data recovery rate of 94% compares favorably with that achieved by a field evaluation of a similar device alongside eleven others carried out by Feenstra et al [ 34 ] in CA, USA (96%). Feenstra and colleagues evaluated twelve different sensors [namely, Shinyei PM Evaluation Kit (Shinyei Tech., China), Alphasense OPC-N2 (Alphasense, Essex, UK), TSI AirAssure (TSI Inc., MN, USA), Hanvon N1 (Hanwang Tech.…”

Section: Discussionsupporting

confidence: 83%

“…Furthermore, we deduced that the availability of electricity could have mainly influenced the almost 100% data recovery by the AirBeam and OPC-N2. The lower percentage data recovery during our study was mainly due to power cuts [ 27 , 34 ].…”

Section: Discussionmentioning

confidence: 99%

“…If the sensor network data shows patterns which seem plausible based on other studies, or which on probing have an explanation, then the data themselves can be accepted because they have revealed something useful. Much as it would be ideal to have over 85% data recovery [ 34 , 35 ], shorter data recoveries (for example 50% which translates to 12 h a day) at local level could also stimulate new discussions about air pollution within the community through citizen science efforts and help identify potential air pollution “hot spots”. This can be a valuable way of spreading awareness and having public discussions, provided the potential deficiencies in the data are also part of the conversation.…”

Section: Discussionmentioning

confidence: 99%

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Measuring Air Quality for Advocacy in Africa (MA3): Feasibility and Practicality of Longitudinal Ambient PM2.5 Measurement Using Low-Cost Sensors

Awokola

Okello

Mortimer

et al. 2020

IJERPH

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Ambient air pollution in urban cities in sub-Saharan Africa (SSA) is an important public health problem with models and limited monitoring data indicating high concentrations of pollutants such as fine particulate matter (PM2.5). On most global air quality index maps, however, information about ambient pollution from SSA is scarce. We evaluated the feasibility and practicality of longitudinal measurements of ambient PM2.5 using low-cost air quality sensors (Purple Air-II-SD) across thirteen locations in seven countries in SSA. Devices were used to gather data over a 30-day period with the aim of assessing the efficiency of its data recovery rate and identifying challenges experienced by users in each location. The median data recovery rate was 94% (range: 72% to 100%). The mean 24 h concentration measured across all sites was 38 µg/m3 with the highest PM2.5 period average concentration of 91 µg/m3 measured in Kampala, Uganda and lowest concentrations of 15 µg/m3 measured in Faraja, The Gambia. Kampala in Uganda and Nnewi in Nigeria recorded the longest periods with concentrations >250 µg/m3. Power outages, SD memory card issues, internet connectivity problems and device safety concerns were important challenges experienced when using Purple Air-II-SD sensors. Despite some operational challenges, this study demonstrated that it is reasonably practicable and feasible to establish a network of low-cost devices to provide data on local PM2.5 concentrations in SSA countries. Such data are crucially needed to raise public, societal and policymaker awareness about air pollution across SSA.

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Section: Discussionsupporting

confidence: 83%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Measuring Air Quality for Advocacy in Africa (MA3): Feasibility and Practicality of Longitudinal Ambient PM2.5 Measurement Using Low-Cost Sensors

Awokola

Okello

Mortimer

et al. 2020

IJERPH

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“…The average magnitude of the errors, Mean Bias Error (MBA) and Mean Absolute Error (MAE), were used to estimate the accuracy of the sensor compared to the filter-based samples. The metric expressed in μg m −3 was calculated as shown elsewhere (Feenstra et al, 2019).…”

Section: Statisticsmentioning

confidence: 99%

“…The study also indicates MAE in the range of 5-11, which decreased to 3-4 after correcting the sensor data through random forest regression. In a similar magnitude, Feenstra et al (2019) informed MAE of 4-7 for lowcost PM 2.5 sensors models Shinyei and Alphasense, which were tested in Riverside, California, an area with a Mediterranean climate that experienced average RH between 48 and 68%, similar to that found in Las Condes (Sup. Fig.…”

Section: Accuracy Of Rh Sensor Measurements Compared To Reference Valuesmentioning

confidence: 99%

Field performance of a low-cost sensor in the monitoring of particulate matter in Santiago, Chile

Tagle

Rojas

Reyes

et al. 2020

Environ Monit Assess

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Integration of low-cost air quality sensors with the internet of things (IoT) has become a feasible approach towards the development of smart cities. Several studies have assessed the performance of low-cost air quality sensors by comparing their measurements with reference instruments. We examined the performance of a low-cost IoT particulate matter (PM 10 and PM 2.5 ) sensor in the urban environment of Santiago, Chile. The prototype was assembled from a PM 10 -PM 2.5 sensor (SDS011), a temperature and relative humidity sensor (BME280) and an IoT board (ESP8266/ Node MCU). Field tests were conducted at three regulatory monitoring stations during the 2018 austral winter and spring seasons. The sensors at each site were operated in parallel with continuous reference air quality monitors (BAM 1020 and TEOM 1400) and a filterbased sampler (Partisol 2000i). Variability between sensor units (n = 7) and the correlation between the sensor and reference instruments were examined. Moderate inter-unit variability was observed between sensors for PM 2.5 (normalized root-mean-square error 9-24%) and PM 10 (10-37%). The correlations between the 1-h average concentrations reported by the sensors and continuous monitors were higher for PM 2.5 (R 2 0.47-0.86) than PM 10 (0.24-0.56). The correlations (R 2 ) between the 24-h PM 2.5 averages from the sensors and reference instruments were 0.63-0.87 for continuous monitoring and 0.69-0.93 for filter-based samplers. Correlation analysis revealed that sensors tended to overestimate PM concentrations in high relative humidity (RH > 75%) and underestimate when RH was below 50%. Overall, the prototype evaluated exhibited adequate performance and may be potentially suitable for monitoring daily PM 2.5 averages after correcting for RH.

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Surface Monitoring of Fire Pollution

Bredder

2023

Landscape Fire, Smoke, and Health

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Performance evaluation of twelve low-cost PM2.5 sensors at an ambient air monitoring site

Cited by 179 publications

References 33 publications

Measuring Air Quality for Advocacy in Africa (MA3): Feasibility and Practicality of Longitudinal Ambient PM2.5 Measurement Using Low-Cost Sensors

Measuring Air Quality for Advocacy in Africa (MA3): Feasibility and Practicality of Longitudinal Ambient PM2.5 Measurement Using Low-Cost Sensors

Field performance of a low-cost sensor in the monitoring of particulate matter in Santiago, Chile

Surface Monitoring of Fire Pollution

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