Aerosol Chamber Characterization for Commercial Particulate Matter (PM) Sensor Evaluation

Hapidin, Dian Ahmad; Saputra, Casmika; Maulana, Dian Syah; Munir, Muhammad Miftahul; Khairurrijal, Khairurrijal

doi:10.4209/aaqr.2017.12.0611

Cited by 33 publications

(26 citation statements)

References 33 publications

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“…The good linearity of the SDS011 sensor for PM 2.5 integrated in the AIRQino were evaluated both in the field and in the laboratory: in the field correlations of up to 0.9 where found with reference sensors when the SDS011 was deployed in different urban environments such as Thessaloniki (Greece) and Wrocław (Poland) [36,37]. Similarly, high coefficients (>0.9) were also found in the laboratory when the sensor was tested in an aerosol chamber [38]. The AIRQino itself was tested in a similarly temperate urban environment in Florence (Italy) obtaining raw correlation coefficients >0.8 when compared with reference stations [23].…”

Section: Discussionmentioning

confidence: 99%

Long-Term Performance Assessment of Low-Cost Atmospheric Sensors in the Arctic Environment

Carotenuto

Brilli

Gioli

et al. 2020

Sensors

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The Arctic is an important natural laboratory that is extremely sensitive to climatic changes and its monitoring is, therefore, of great importance. Due to the environmental extremes it is often hard to deploy sensors and observations are limited to a few sparse observation points limiting the spatial and temporal coverage of the Arctic measurement. Given these constraints the possibility of deploying a rugged network of low-cost sensors remains an interesting and convenient option. The present work validates for the first time a low-cost sensor array (AIRQino) for monitoring basic meteorological parameters and atmospheric composition in the Arctic (air temperature, relative humidity, particulate matter, and CO2). AIRQino was deployed for one year in the Svalbard archipelago and its outputs compared with reference sensors. Results show good agreement with the reference meteorological parameters (air temperature (T) and relative humidity (RH)) with correlation coefficients above 0.8 and small absolute errors (≈1 °C for temperature and ≈6% for RH). Particulate matter (PM) low-cost sensors show a good linearity (r2 ≈ 0.8) and small absolute errors for both PM2.5 and PM10 (≈1 µg m−3 for PM2.5 and ≈3 µg m−3 for PM10), while overall accuracy is impacted both by the unknown composition of the local aerosol, and by high humidity conditions likely generating hygroscopic effects. CO2 exhibits a satisfying agreement with r2 around 0.70 and an absolute error of ≈23 mg m−3. Overall these results, coupled with an excellent data coverage and scarce need of maintenance make the AIRQino or similar devices integrations an interesting tool for future extended sensor networks also in the Arctic environment.

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Section: Discussionmentioning

confidence: 99%

Long-Term Performance Assessment of Low-Cost Atmospheric Sensors in the Arctic Environment

Carotenuto

Brilli

Gioli

et al. 2020

Sensors

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“…Laboratory studies have shown that many PM sensors have a high correlation with a reference instrument, which is often an optical-based research-grade instrument calibrated to filter mass or a designated regulatory instrument that has been validated against filter measurements [ 10 ]. Additionally, some PM sensors maintain linearity at concentrations as high as 500 µg/m 3 [ 7 , 11 , 12 , 13 , 14 ] with a quadratic response to concentrations as high as 10,000 µg/m 3 [ 11 , 14 ]. However, sensor performance has varied by manufacturer and model, even when using the same original equipment manufacturer (OEM) sensor, demonstrating the importance of the physical configuration of the sensor package, sensor design (e.g., laser, light detector, flow path, etc.…”

Section: Introductionmentioning

confidence: 99%

“…However, sensor performance has varied by manufacturer and model, even when using the same original equipment manufacturer (OEM) sensor, demonstrating the importance of the physical configuration of the sensor package, sensor design (e.g., laser, light detector, flow path, etc. ), and data processing algorithms [ 11 , 15 , 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Field Evaluation of Low-Cost Particulate Matter Sensors for Measuring Wildfire Smoke

Holder

Mebust

Maghran

et al. 2020

Sensors

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Until recently, air quality impacts from wildfires were predominantly determined based on data from permanent stationary regulatory air pollution monitors. However, low-cost particulate matter (PM) sensors are now widely used by the public as a source of air quality information during wildfires, although their performance during smoke impacted conditions has not been thoroughly evaluated. We collocated three types of low-cost fine PM (PM2.5) sensors with reference instruments near multiple fires in the western and eastern United States (maximum hourly PM2.5 = 295 µg/m3). Sensors were moderately to strongly correlated with reference instruments (hourly averaged r2 = 0.52–0.95), but overpredicted PM2.5 concentrations (normalized root mean square errors, NRMSE = 80–167%). We developed a correction equation for wildfire smoke that reduced the NRMSE to less than 27%. Correction equations were specific to each sensor package, demonstrating the impact of the physical configuration and the algorithm used to translate the size and count information into PM2.5 concentrations. These results suggest the low-cost sensors can fill in the large spatial gaps in monitoring networks near wildfires with mean absolute errors of less than 10 µg/m3 in the hourly PM2.5 concentrations when using a sensor-specific smoke correction equation.

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“…16 The technical details of the PM 2.5 chamber are presented elsewhere. 23 A compressor pump (Pump 1), equipped with by a buffer, pressure regulator, dryer, and HEPA lter, provided a stable, dry, and clean air ow to the air ltration test system. The air ow from the compressor pump was delivered to a mass ow controller (Horriba, Stec SEC-500) (Q2) and to the PM 2.5 chamber (Q1).…”

Section: Characterization Of Nanober Membranesmentioning

confidence: 99%

The synthesis of nanofiber membranes from acrylonitrile butadiene styrene (ABS) waste using electrospinning for use as air filtration media

et al. 2019

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Aerosol Chamber Characterization for Commercial Particulate Matter (PM) Sensor Evaluation

Cited by 33 publications

References 33 publications

Long-Term Performance Assessment of Low-Cost Atmospheric Sensors in the Arctic Environment

Long-Term Performance Assessment of Low-Cost Atmospheric Sensors in the Arctic Environment

Field Evaluation of Low-Cost Particulate Matter Sensors for Measuring Wildfire Smoke

The synthesis of nanofiber membranes from acrylonitrile butadiene styrene (ABS) waste using electrospinning for use as air filtration media

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